[
  {
    "url": "https://frsc.gov.ng/wp-content/uploads/2023/04/FRSC-STATISTICS-DIGEST-FOURTH-QUARTER-2022.pdf",
    "text": "### FRSC STATISTICAL DIGEST\n\nPublisher \nDauda Ali Biu, FCNA, CPA \nCorps Marshal, \n\nEditorial Board Editor-in-Chief ACM AB Datsama, fsi\n\nEditor(s) CC BM Tunau (CC Statistics) DCC AH Ibrahim (DCC Ops. Statistics)\n\nAsst. Editor/Graphics SRC M Umar (PRS)\n\nSecretary RC SO Egbujiobi (CS)\n\n| SRC DO Ayeni | (OPS) | |\n| --- | --- | --- |\n| SRC IA Alkali | (F&A) | |\n| SRC S Ogiri | (CPEO) | |\n\nMembers \nCC A Oki (MVA) \nSRC SO Olasupo (AHR) \nCRC O Olivia (CLA) \nSRC DO Ayeni (OPS) \nSRC IA Alkali (F&A) \nSRC S Ogiri (CPEO) \nSRC IO Hassan(SERVICOM) \nSRC MB Zaki(TRG) \nSRC C Agbo(COSEN) \nSRC E Ogbole (CTSO) \nRC M Ndifon (PRS) \nRC IO Adesegun (CP) \nARC OI Aluko(CMRS) \n\nNigeria Road Safety Strategy II (NRSS II) Given the successes recorded after an appraisal of the implementation of the Nigeria Road Safety Strategy I (NRSS 2014-2018), the NRSS II (2021-2030) was produced to address the current overlaps while streamlining the current roles and responsibilities of all relevant stakeholders in order to consolidate and further sustain the achievements recorded in the maiden edition which effectively brought up well articulated course of Road Safety Management in Nigeria. The clarion call to develop the second edition of NRSS (2021-2030) therefore behooves on all well-meaning persons, government at all levels, corporate organizations, non-governmental organizations, international communities and stakeholders to collectively work together as a team in ensuring that the path towards ending the carnage on our road is aggressively pursued and achieved. Furthermore, the NRSS II specifically provides the platform for more coordinated attention on Child Road Safety in Nigeria. Road Traffic Injury is a leading cause of death and disability in children as over 40% of the Nigeria population is aged 0-14, which makes safety of the children a critical focus in NRSS II (2021-2030). More so, this strategy document will put the safety of children on the road at the forefront in Nigeria. In pursuing this, priority will be given to the United Nations Sustainable Development Goals (UN-SDGs) which include targets for 2030 as; reducing road traffic fatalities by 50% and provide access to safe, affordable, accessible and sustainable transport system for all. Meanwhile, as the lead agency, the Corps will from time to time seek the support of relevant stakeholders and motoring public to forestall every ugly incidence that had been nagging the progress and retarding the economic development of the Nation due to Road Traffic Crashes. The NRSS II (2021-2030) when conscientiously implemented through extensive and encompassing stakeholders’ sensitization and enlightenment programme (which will be developed and carried out across the Federation) will build upon the background successes of the NRSS I (2014-2018), and thereby advance the course of Road Safety campaign in creating safe motoring environment for all.\n\nACM ACM Aliyu B. Datsama, fsi Assistant Corps Marshal ACM Policy, Research and Statistics\n\nTABLE OF CONTENTS\n\n| SN | Departments | Abbreviation Page |\n| --- | --- | --- |\n| 1 | Operations | OPS 5 |\n| 2 | Administration And Human Resources | AHR 7 |\n| 3 | Finance And Accounts | F&A 9 |\n| 4 | Motor Vehicle Administration | MVA 12 |\n| 5 | Policy Research and Statistics | PRS 15 |\n| 6 | Special Duties and External Relations | SDER 17 |\n| 7 | Training | TRG 20 |\n| 8 | Technical Services Department | TSD 22 |\n| 9 | Corps Legal Office | CLO 23 |\n| 10 | Corps Public Education Office | CPEO 28 |\n| 11 | Corps Medical And Rescue Services | CMRS 29 |\n| 12 | Corps Transport Standardization Office | CTSO 30 |\n| 13 | Corps Secretary | CS 33 |\n| 14 | Corps Provost | CP 34 |\n| 15 | SERVICOM | 35 |\n| 16 | Corps Safety Engineering | COSEN 36 |\n\nOperations\n\nTable 1: Distribution Of FRSC Commands\n\nS/N ZONE ZONAL CMDS SECTOR CMDS UNIT CMDS OUTPOSTS TOTAL FORMATIONS\n\n| | | | 1 | | | RSHQ | | | 0 | | | | 0 | | | 1 | | | | 1 | | | | 2 | | | | |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| | | | 2 | | | RS1, Kaduna | | | 1 | | | | 4 | | | 31 | | | | 07 | | | | 43 | | | | |\n| | | | 2 | | | RS2, Lagos | | | 1 | | | | 2 | | | 24 | | | | 09 | | | | 36 | | | | |\n| | | | 3 | | | RS3, Yola | | | 1 | | | | 3 | | | 11 | | | | 0 | | | | 15 | | | | |\n| | | | 4 | | | RS4, Jos | | | 1 | | | | 3 | | | 20 | | | | 4 | | | | 28 | | | | |\n| | | | 5 | | | RS5, Benin | | | 1 | | | | 3 | | | 22 | | | | 5 | | | | 31 | | | | |\n| | | | 6 | | | RS6, P/Harcourt | | | 1 | | | | 4 | | | 14 | | | | 2 | | | | 21 | | | | |\n| | | | 7 | | | RS7, Gwarinpha | | | 1 | | | | 2 | | | 22 | | | | 12 | | | | 37 | | | | |\n| | | | 8 | | | RS8, Ilorin | | | 1 | | | | 3 | | | 23 | | | | 5 | | | | 32 | | | | |\n| | | | 9 | | | RS9, Enugu | | | 1 | | | | 4 | | | 16 | | | | 1 | | | | 22 | | | | |\n| | | | 10 | | | RS10, Sokoto | | | 1 | | | | 3 | | | 11 | | | | 2 | | | | 17 | | | | |\n| | | | 11 | | | RS11, Osogbo | | | 1 | | | | 3 | | | 25 | | | | 5 | | | | 34 | | | | |\n| | | | 12 | | | RS12, Bauchi | | | 1 | | | | 3 | | | 13 | | | | 0 | | | | 17 | | | | |\n| | | | | | | TOTAL | | | 12 | | | | 37 | | | 233 | | | | 54 | | | | 336 | | | | |\n\nTable 2: Total War on Critical offences\n\nST\n\nND rd\n\nTH\n\nS/N ZONE TOTAL ARREST 1\n\nTOTAL ARREST 2\n\nTOTAL ARREST 3\n\nTOTAL ARREST 4\n\nGRAND TOTAL\n\nQ\n\nQ\n\nQ\n\nQ\n\n1 RS1 Kaduna 2131 2,228 2686 2,012 9057\n\n2 RS2 Lagos \n1818 2,878 2719 1,874 9289 \n3 RS3 Yola 593 837 945 494 2869 \n4 RS4 Jos 1322 1,998 2101 1,335 6756 \n5 RS5 Benin \n1213 1,314 1441 1,013 4981 \n\n6 RS6 Port Harcourt\n\n1779 2,273 2550 966 7568\n\n7 RS7 Abuja \n1106 2,223 2459 1,474 7262 \n8 RS8 1060 2,229 2118 1,202 6609 \n9 RS9, Enugu \n450 750 673 683 2556 \n\n10 RS10, Sokoto\n\n525 1,058 1177 732 3492\n\n11 RS11, Osogbo\n\n1281 2,532 2390 1,694 7897\n\n12 RS12 Bauchi\n\n957 1,359 1340 1,084 4740 TOTAL 14,235 22,079 22,599 14,565 73478\n\nChart 1:Chart Illustrating War on Critical Offences\n\n3500\n\n3000\n\n2500\n\n2000\n\n1500\n\n1000\n\n500\n\nTOTAL ARREST 1ST Q TOTAL ARREST 2ND Q TOTAL ARREST 3rd Q TOTAL ARREST 4TH Q\n\n0\n\nTable 3: SUMMARY OF OFFENDERS/OFFENCES\n\n| S/N | ZONE | TOTAL OFFENDERS | % | TOTAL OFFENCES | % |\n| --- | --- | --- | --- | --- | --- |\n| 1. | RS1, Kaduna | 3,737 | 16 | 4,118 | 16 |\n| 2. | RS2, Lagos | 3,481 | 15 | 4,101 | 16 |\n| 3. | RS3, Yola | 872 | 4 | 915 | 4 |\n| 4. | RS4, Jos | 2,071 | 9 | 2,266 | 9 |\n| 5. | RS5, Benin | 1,621 | 7 | 1,847 | 7 |\n| 6. | RS6 Port Harcourt | 1,342 | 6 | 1,469 | 6 |\n| 7. | RS7 Abuja | 2,401 | 10 | 2,548 | 10 |\n| 8. | RS8, Ilorin | 1,845 | 8 | 2,012 | 8 |\n| 9. | RS9, Enugu | 928 | 4 | 1,075 | 4 |\n| 10. | RS10, Sokoto | 936 | 4 | 975 | 4 |\n| 11. | RS11, Osogbo | 2,414 | 10 | 2,628 | 10 |\n| 12. | RS12, Bauchi | 1,715 | 7 | 1,759 | 7 |\n| | TOTAL | 23,363 | 100 | 25,713 | 100 |\n\nChart 2: Chart Illustrating Offenders/Offences\n\n4,500\n\n4,000\n\n3,500\n\n3,000\n\n2,500\n\n2,000\n\n1,500\n\nTOTAL OFFENDERS TOTAL OFFENCES\n\n1,000\n\n500\n\n0\n\nTable 4: General Admin Activities\n\n#### Admin and Human Resources\n\nS/ N\n\nActivity/Programme No Received No Treated\n\nNo \nOutstandin \ng \na. Application for Maternity leave. 69 69 0 \nb. Application for Permission to get \nMarried \n\n217 217 0 \nc. Application for Change of Name 11 11 0 \nd. Application for Annual Leave 313 313 0 \ne. Application for Pass 37 37 0 \nf. Total Incoming Mails 3387 3387 0 \ng. Total Outgoing Mails 3177 3177 0 \nh. NHF Passbook Update 92 92 0 \ni. NHF Deceased 5 5 0 \nj. Payment of NHF to Retirees 65 65 0 \nk. Total number of Corps Members 15 15 0 \nl. Total number of IT Students 0 0 0 \n69 \n\n217\n\n11\n\n313\n\n37\n\n3387\n\n3177\n\n92\n\n5\n\n65\n\n15\n\n0\n\n69\n\n217\n\n11\n\n313\n\n37\n\n3387\n\n3177\n\n92\n\n5\n\n65\n\n15\n\n0\n\n0\n\n0\n\n0\n\n0\n\n0\n\n0\n\n0\n\n0\n\n0\n\n0\n\n0\n\n0\n\nApplication for Maternity leave.\n\nApplication for Permission to get…\n\nApplication for Change of Name\n\nApplication for Annual Leave\n\nApplication for Pass\n\nTotal Incoming Mails\n\nTotal Outgoing Mails\n\nNHF Passbook Update\n\nNHF Deceased\n\nPayment of NHF to Retirees\n\nTotal number of Corps Members\n\nTotal number of IT Students\n\nNo Outstanding No Treated No Received\n\nChart 3: Chart Illustrating General Admin Activities Treated\n\nTable 5: Disciplinary Cases Treated\n\nChart 4: Chart Illustrating Disciplinary Cases Treated\n\nDismissal\n\nTermination of Appointment\n\nReduction in Rank\n\nLoss of Seniority\n\nSuspension from Service\n\nPlace on Interdiction\n\nMajor Entry\n\nSevere Reprimand\n\nWarning Letter\n\nMinor Entry\n\nReprimand\n\nQuery\n\nDischarged\n\nAppeal\n\nFDP Cases received from CMDs\n\nSummary Trials received\n\nReview to CM\n\nDischarge and Acquittal\n\nLifting of Interdiction\n\nFDP Cases concluded\n\nNumber of FDP Sitting\n\nNo Outstanding No Treated No Received\n\nS/N Activities/ Punishments No Received No Treated No Outstanding 1 Dismissal 2 2 0 2 Termination of Appointment 1 1 0 3 Reduction in Rank 0 0 0 4 Loss of Seniority 0 0 0 5 Suspension from Service 0 0 0 6 Place on Interdiction 1 1 0 7 Major Entry 17 17 0 8 Severe Reprimand 64 64 0 9 Warning Letter 4 4 0 10 Minor Entry 0 0 0 11 Reprimand 0 0 0 12 Query 43 43 0 13 Discharged 0 0 0 14 Appeal 18 18 0 15 FDP Cases received from CMDs 59 59 0 16 Summary Trials received 76 76 0 17 Review to CM 61 61 0 18 Discharge and Acquittal 31 31 0 19 Lifting of Interdiction 0 0 0 20 FDP Cases concluded 15 15 0 21 Number of FDP Sitting 17 17 0 TOTAL 450 450 0\n\n#### Finance and Accounts Department\n\nTable 6: Incoming Requests For Variation Of Staff Salaries (Officers)\n\nINCOMING REQUESTS FOR VARIATION OF STAFF SALARIES (Officers)\n\nNO. RECEIVED NO. TREATED ON-GOING\n\n| 1st Quarter 2022 | 85 | 85 | 0 |\n| --- | --- | --- | --- |\n| 2nd Quarter 2022 | 95 | 95 | 0 |\n| 3rd Quarter 2022 | 76 | 76 | 0 |\n| 4th Quarter 2022 | 71 | 71 | 0 |\n| Total | 327 | 327 | 0 |\n\nChart 5: Chart Illustrating Requests For Variation Of Staff Salaries (Officers)\n\n100\n\n90\n\n80\n\n70\n\n60\n\n50\n\nNO. RECEIVED NO. TREATED ON-GOING\n\n40\n\n30\n\n20\n\n10\n\n0\n\n1st Quarter 2022\n\n2nd Quarter 2022\n\n3rd Quarter 2022\n\n4th Quarter 2022\n\nTable 7: Incoming Requests For Variation Of Staff Salaries (Marshals)\n\n| INCOMING REQUESTS FOR VARIATION OF STAFF SALARIES (Marshals) | NO. RECEIVED | NO. TREATED | ON-GOING |\n| --- | --- | --- | --- |\n| 1st Quarter 2022 | 302 | 302 | 0 |\n| 2nd Quarter 2022 | 260 | 260 | 0 |\n| 3rd Quarter 2022 | 434 | 434 | 0 |\n| 4th Quarter 2022 | 188 | 188 | 0 |\n| Total | 1184 | 1184 | 0 |\n\nChart 6: Incoming Requests For Variation Of Staff Salaries (Marshals)\n\n500\n\n450\n\n400\n\n350\n\n300\n\n250\n\nNO. RECEIVED NO. TREATED ON-GOING\n\n200\n\n150\n\n100\n\n50\n\n0\n\n1st Quarter 2022\n\n2nd Quarter 2022\n\n3rd Quarter 2022\n\n4th Quarter 2022\n\nTable 8: Summary of Transfer Allowances Activities\n\nNO. RECEIVED\n\nNO. COMPUTED ON-GOING\n\nTRANSFER ALLOWANCE COMPUTATION\n\n| 1st Quarter 2022 | 73 | 73 | 0 |\n| --- | --- | --- | --- |\n| 2nd Quarter 2022 | 321 | 321 | 0 |\n| 3rd Quarter 2022 | 4716 | 4716 | 0 |\n| 4th Quarter 2022 | 9920 | 9920 | 0 |\n| Total | 1530 | 1530 | 0 |\n\nChart 7: Summary of Transfer Allowances Activities\n\n12000\n\n10000\n\n8000\n\n6000\n\nNO. RECEIVED NO. COMPUTED ON-GOING\n\n4000\n\n2000\n\n0\n\n1st Quarter 2022\n\n2nd Quarter 2022\n\n3rd Quarter 2022\n\n4th Quarter 2022\n\nTable 9: Showing The Breakdown Of Number Plates Produced In Oct., Nov., Dec., 2022\n\n#### Motor Vehicle Administration\n\nS/ N\n\nCategory Oct-22 Nov-22 Dec-22\n\nTOTAL 1 Government Motor Vehicle 99 289 549 937 2 Government Articulated 0 0 0 0\n\n3 Private Motor Vehicle\n\n27,187 32,034 32,327\n\n91,548\n\n4 Commercial Motor Vehicle\n\n9,733 7,224 5,330\n\n22,287 \n5 Articulated 15 0 0 15 \n6 Fancy 52 71 36 159 \n7 Out of Series 10 3 13 26 \n8 Military/Paramillitary 118 43 30 191 \n9 FG 242 699 282 1,223 \n10 Diplomatic 0 0 0 0 \n11 Comp 0 0 0 0 \n12 Gov fan 3 17 23 43 \n13 Dealer 285 38 58 381 \nSub Total 37,744 40,418 38,648 \n\n116,81 \n0 \n\nMotorcycle 1 Government Motor Cycle 3 4 241 248\n\n2 Private Motor Cycle\n\n15,557 14,634 11,877\n\n42,068\n\n3 Commercial Motor Cycle\n\n19,883 21,216 14,171\n\n55,270 \n4 Military/Paramillitary Motor Cycle 0 4 1 5 \n5 Fancy 0 0 3 3 \n6 FG 0 17 3 20 \n7 Dealer 0 0 0 0 \n35,443 35,875 26,296 \n\n97,614 0 1 Motor Vehicle 3 31 7 41 2 Motor Cycle 4 3 0 7 Sub Total 7 34 7 48 Total Production 0\n\na Motor Vehicle 37,744 40,418 38,648 \n116,81 \n0 \n\nb Motorcycle 35,443 35,875 26,296 97,614 c Reprint 7 34 7 48\n\nGrand Total 73,194 76,327 64,951 \n214,47 \n2 \n\nTable 10: Table Showing National Drivers License (NDL) Production By State and it’s Comparative Analysis\n\nTABLE 6 :3TH QUARTER 2022 & 4TH QUARTER 2022 COMPARATIVE ANALYSIS OF NDL PRODUCTION. S/ N STATE 3RD QUARTER 2022 4TH QUARTER 2022 DIFFERENCE % 1 ABIA 4,137 4,846 709 17% 2 ADAMAWA 1,502 2,125 623 41% 3 AKWA-IBOM 3,658 4,278 620 17% 4 ANAMBRA 6,222 7,129 907 15% 5 BAUCHI 1,928 2,249 321 17% 6 BAYELSA 2,895 2,084 -811 -28% 7 BENUE 2,195 2,224 29 1% 8 BORNO 2,205 2,240 35 2% 9 CROSS RIVER 1,932 1,934 2 0% 10 DELTA 17,218 14,268 -2,950 -17% 11 EBONYI 1,384 1,527 143 10% 12 EDO 11,412 11,218 -194 -2% 13 EKITI 2,017 2,082 65 3% 14 ENUGU 5,613 5,277 -336 -6% 15 FCT 26,376 26,504 128 0% 16 GOMBE 1,500 1,623 123 8% 17 IMO 6,135 3,736 -2,399 -39% 18 JIGAWA 751 972 221 29% 19 KADUNA 9,884 9,339 -545 -6% 20 KANO 8,878 7,832 -1,046 -12% 21 KATSINA 1,574 1,293 -281 -18% 22 KEBBI 508 590 82 16% 23 KOGI 2,478 1,780 -698 -28% 24 KWARA 3,767 3,496 -271 -7% 25 LAGOS 68,482 69,332 850 1% 26 NASARAWA 3,352 3,026 -326 -10% 27 NIGER 2,843 2,794 -49 -2% 28 OGUN 22,579 18,773 -3,806 -17% 29 ONDO 5,427 4,196 -1,231 -23% 30 OSUN 4,669 4,057 -612 -13% 31 OYO 15,629 12,814 -2,815 -18% 32 PLATEAU 4,726 4,056 -670 -14% 33 RIVERS 14,990 15,023 33 0% 34 SOKOTO 1,045 1,150 105 10% 35 TARABA 595 761 166 28% 36 YOBE 1,229 1,116 -113 -9% 37 ZAMFARA 589 442 -147 -25% TOTAL 272,324 258,186 -14,138 -5%\n\nChart 8: National Driver License production\n\n#### Policy Research and Statistics\n\nTable 11: RTC Cases on State Basis\n\nSTATE TOTAL CASES NUMBER INJURED NUMBER KILLED PEOPLE INVOLVED Abia 33 89 9 209 Adamawa 34 117 3 213 Akwa Ibom 23 75 9 164 Anambra 40 121 20 344 Bauchi 122 523 94 888 Bayelsa 8 17 1 27 Benue 73 225 16 395 Borno 39 207 22 375 Cross River 39 81 17 257 Delta 48 174 25 306 Ebonyi 55 118 17 368 Edo 63 142 28 487 Ekiti 19 40 16 101 Enugu 56 157 18 481 FCT 482 791 109 2103 Gombe 115 363 9 656 Imo 31 97 4 235 Jigawa 179 624 46 1043 Kaduna 205 810 164 1728 Kano 120 411 57 781 Katsina 36 169 77 278 Kebbi 44 186 39 391 Kogi 138 413 36 1287 Kwara 91 252 30 632 Lagos 167 268 57 808 Nasarawa 234 580 78 1216 Niger 145 553 41 1075 Ogun 301 663 102 1737 Ondo 109 247 59 593 Osun 101 319 30 795 Oyo 165 423 84 984 Plateau 84 254 32 522 Rivers 26 43 9 156 Sokoto 37 148 38 259 Taraba 68 192 8 361 Yobe 57 237 60 423 Zamfara 30 103 12 174 TOTAL 3617 10232 1476 22852\n\nChart 9: RTC Cases on State Basis\n\nAbia\n\nAdamawa\n\nAkwa Ibom\n\nAnambra\n\nBauchi\n\nBayelsa\n\nBenue\n\nBorno\n\nCross River\n\nDelta\n\nEbonyi\n\nEdo\n\nEkiti\n\nEnugu\n\nFCT\n\nGombe\n\nImo\n\nJigawa\n\nKaduna\n\nKano\n\nKatsina\n\nKebbi\n\nKogi\n\nKwara\n\nLagos\n\nNasarawa\n\nNiger\n\nOgun\n\nOndo\n\nOsun\n\nOyo\n\nPlateau\n\nRivers\n\nSokoto\n\nTaraba\n\nYobe\n\nZamfara\n\nChart Title\n\nPEOPLE INVOLVED NUMBER KILLED NUMBER INJURED TOTAL CASES\n\n#### Special Duties and External Relations\n\nTable 12:Partnership Analysis of Government Agencies and NGO\n\nTOTAL\n\nZONES COMMAND GOVT AGENCIES\n\nNGOs CORPORATE ORGs\n\n| RSHQ ABUJA | RSHQ ABUJA | | | | 42 | | 103 | 16 | 161 | | | |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| RS 1 KADUNA | RS1.1 KADUNA | | | | 14 | | 8 | 8 | | 30 | | |\n| | RS1.2 KANO | | | | 15 | | 1 | 6 | | 22 | | |\n| | RS1.3 KATSINA | | | | 4 | | 1 | 15 | | 20 | | |\n| | RS1.4 JIGAWA | | | | 22 | | 6 | 3 | | 31 | | |\n| RS 2 LAGOS | RS2.1 LAGOS | | | | 6 | | 2 | 17 | | 25 | | |\n| | RS2.2 OGUN | | | | 24 | | 0 | 40 | | 64 | | |\n| RS 3 YOLA | RS3.1 ADAMAWA | | | | 64 | | 3 | 6 | | 73 | | |\n| | RS3.2 GOMBE | | | | 21 | | 0 | 7 | | 28 | | |\n| | RS3.3 TARABA | | | | 9 | | 0 | 5 | | 14 | | |\n| RS 4 JOS | RS4.1 PLATEAU | | | | 12 | | 0 | 14 | | 26 | | |\n| | RS4.2 BENUE | | | | 17 | | 1 | 5 | | 23 | | |\n| | RS4.3 | | | | 10 | | 0 | 4 | | 14 | | |\n| | NASSARAWA | | | | | | | | | | | |\n| RS 5 BENIN | RS5.1 EDO | | | | 10 | | 0 | 11 | | 21 | | |\n| | RS5.2 DELTA | | | | 11 | | 0 | 0 | | 11 | | |\n| | RS5.3 ANAMBRA | | | | 7 | | 0 | 68 | | 75 | | |\n| RS 6 P/H | RS6.1 RIVER | | | | 21 | | 3 | 14 | | 38 | | |\n| | RS6.2 C/RIVER | | | | 3 | | 0 | 7 | | 10 | | |\n| | RS6.3 A/IBOM | | | | 11 | | 1 | 7 | | 19 | | |\n| | RS6.4 BAYELSA | | | | | 22 | 4 | 0 | | 26 | | |\n| RS 7 ABUJA | RS7.1 FCT | | | | 14 | | 6 | 11 | | 31 | | |\n| | RS7.2 NIGER | | | | 1 | | 10 | 10 | | 21 | | |\n| RS 8 ILORIN | RS8.1 KWARA | | | | 3 | | 1 | 8 | | 12 | | |\n| | RS8.2 EKITI | | | | 25 | | 4 | 7 | | 36 | | |\n| | RS8.3 KOGI | | | | 13 | | 18 | 5 | | 36 | | |\n| RS 9 ENUGU | RS9.1 ENUGU | | | | 0 | | 0 | 0 | | 0 | | |\n| | RS9.2 EBONYI | | | | 5 | | 0 | 3 | | 8 | | |\n| | RS9.3ABIA | | | | 7 | | 0 | 6 | | 13 | | |\n| | RS9.4 IMO | | | | 11 | | 0 | 11 | | 22 | | |\n| RS 10 SOKOTO | RS10.1 SOKOTO | | | | 2 | | 2 | 0 | | 4 | | |\n| | RS10.2 KEBBI | | | | 11 | | 3 | 3 | | 17 | | |\n| | RS10.3 ZAMFARA | | | | 65 | | 8 | 14 | | 87 | | |\n| RS 11 OSOGBO | RS11.1 OSUN | | | | 6 | | 1 | 4 | | 11 | | |\n| | RS11.2 ONDO | | | | 8 | | 1 | 8 | | 17 | | |\n| | RS11.3 OYO | | | | 6 | | 0 | 47 | | 53 | | |\n| RS 12 BAUCHI | RS12.1 BAUCHI | | | | 5 | | 0 | 3 | | 8 | | |\n| | RS12.2 BORNO | | | | 11 | | 0 | 8 | | 19 | | |\n| | RS12.3 YOBE | | | | 0 | | 0 | 0 | | 0 | | |\n| TOTAL | | | | | 576 | 187 | | 401 | 1172 | | | |\n\nChart 10: Partnership Analysis of Government Agencies and NGO\n\n0 20 40 60 80 100 120\n\nRSHQ ABUJA\n\nRS1.1 KADUNA\n\nRS1.2 KANO\n\nRS1.3 KATSINA\n\nRS1.4 JIGAWA\n\nRS2.1 LAGOS\n\nRS2.2 OGUN\n\nRS3.1 ADAMAWA\n\nRS3.2 GOMBE\n\nRS3.3 TARABA\n\nRS4.1 PLATEAU\n\nRS4.2 BENUE\n\nRS4.3 NASSARAWA\n\nRS5.1 EDO\n\nRS5.2 DELTA\n\nRS5.3 ANAMBRA\n\nRS6.1 RIVER\n\nRS6.2 C/RIVER\n\nRS6.3 A/IBOM\n\nRS6.4 BAYELSA\n\nRS7.1 FCT\n\nRS7.2 NIGER\n\nRS8.1 KWARA\n\nRS8.2 EKITI\n\nRS8.3 KOGI\n\nRS9.1 ENUGU\n\nRS9.2 EBONYI\n\nRS9.3ABIA\n\nRS9.4 IMO\n\nRS10.1 SOKOTO\n\nRS10.2 KEBBI\n\nRS10.3 ZAMFARA\n\nRS11.1 OSUN\n\nRS11.2 ONDO\n\nRS11.3 OYO\n\nRS12.1 BAUCHI\n\nRS12.2 BORNO\n\nRS12.3 YOBE\n\nR\n\nS\n\nH\n\nQ\n\nA\n\nB\n\nU\n\nJ\n\nA\n\nR\n\nS\n\n1\n\nK\n\nA\n\nD\n\nU\n\nN\n\nA\n\nR\n\nS\n\n2\n\nL\n\nA\n\nG\n\nO\n\nS\n\nR\n\nS\n\n3\n\nY\n\nO\n\nL\n\nA\n\nR\n\nS\n\n4\n\nJ\n\nO\n\nS\n\nR\n\nS\n\n5\n\nB\n\nE\n\nN\n\nI\n\nN\n\nR\n\nS\n\n6\n\nP /\n\nH\n\nR\n\nS\n\n7\n\nA\n\nB\n\nU\n\nJ\n\nA\n\nR\n\nS\n\n8\n\nI\n\nL\n\nO\n\nR\n\nI\n\nN\n\nR\n\nS\n\n9\n\nE\n\nN\n\nU\n\nG\n\nU\n\nR\n\nS\n\n1\n\n0\n\nS\n\nO\n\nK\n\nO\n\nT\n\nO\n\nR\n\nS\n\n1\n\n1\n\nO\n\nS\n\nO\n\nG\n\nB\n\nO\n\nR\n\nS\n\n1\n\n2\n\nB\n\nA\n\nU\n\nC\n\nH\n\nI\n\nCORPORATE ORGs\n\nNGOs\n\nGOVT AGENCIES\n\nTable 13: Summary Of Activities Of National Community Post Crash Care Initiative (NCPCCI) by Volunteers For Third Quarter 2022\n\nChart 11 : Activities for Q4\n\n2022 NO.OF RESCUE CARRIED OUT\n\nMEETINGS (MAX 27)\n\nATTENDANC E (MAX 540)\n\nTRAINING (MAX 27)\n\nOCT. 12 14 30 0 NOV. 15 4 43 2 DEC. 15 3 32 1 TOTAL 43 21 105 3\n\n0\n\n5\n\n10\n\n15\n\n20\n\n25\n\n30\n\n35\n\n40\n\n45\n\n50\n\nNO.OF \nRESCUE \nCARRIED OUT \n\nMEETINGS (MAX 27)\n\nATTENDANCE (MAX 540)\n\nTRAINING (MAX 27)\n\nOCT. \nNOV. \nDEC. \n\nTable 5: Comparative Analysis Of November/December 2014 Training\n\nTable 14: NUMBER OF STAFF ON SPONSORSHIP\n\nS/N NO OF STAFF ON \nSPONSORSHIP \n1. No of staff on sponsorship (Foreign) 2 \n2. No of staff on self-sponsorship (Foreign) 320 \n3. No of staff on sponsorship (Local) 2 \n4 No of staff given final approval for further studies \n2022 \n\n85\n\nTOTAL 409\n\nTable 15: NUMBER OF DRIVING SCHOOLS ACCREDITED\n\nS/N MONTH NUMBER OF SCHOOLS\n\n1. OCTOBER 0 \n2. NOVEMBER 65 \n3. DECEMBER 0 \nTOTAL = 65 \n\nTable 16: NUMBER OF ENROLLED TRAINEES\n\n| S/N | | | MONTHS | | | | NUMBER OF ENROLLED TRAINEES | | | | |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| 1 | | | OCTOBER | | | | 41,293 | | | | |\n| 2. | | | | NOVEMBER | | | 50,467 | | | | |\n| 3. | | | DECEMBER | | | | 55,587 | | | | |\n| TOTAL =147,347 | | | | | | | | | | | |\n\nTable 17: NUMBER OF GRADUATED TRAINEES\n\n| S/N | | | MONTHS | | | NUMBER OF GRADUATED TRAINEE | | | | | |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| 1 | | | OCTOBER | | | 50,693 | | | | | |\n| 2. | | | NOVEMBER | | | 43,493 | | | | | |\n| 3. | | | DECEMBER | | | 27,895 | | | | | |\n| TOTAL =122,081 | | | | | | | | | | | |\n\nFRSC Statistical Digest 21 \nSUMMARY OF 4TH QUARTER 2022 ANALYSIS \nNo of Staff on Sponsorship (local/Foreign)………………………………………... 284 \nNo of Staff Trained on Virtual and Physical (Local)………………………………25 \nNo of Staff given final approval for Further Studies ………..…………………..271 \nNo of Driving School Registered……………………………………………………….…124 \nNo of Driving School Accredited …………………………………………………………..65 \nNo of Trainee drivers enrolled ……………………………………………………………147,347 \nNo of Trainee drivers Graduated………………………………………………………..…122,081 \nNo of Riders/Drivers Trained By FRSC ………………………………………………….444 \nNo of Drivers Trained By Field Command……………………………………………..4187 \n\n#### TECHNICAL SERVICES DEPARTMENT\n\nTable 18: Number of FRSC Vehicles\n\n| Purpose | Field | RSHQ | Total |\n| --- | --- | --- | --- |\n| Patrol | 774 | 65 | 839 |\n| Admin | 59 | 130 | 189 |\n| Ambulances | 141 | 12 | 153 |\n| Tow Trucks | 29 | 16 | 45 |\n| Total | 1003 | 223 | 1226 |\n\nTable 19: Summary of FRSC Bikes\n\nRSHQ Total\n\nField Commands\n\n204 40 244\n\nFRSC ESTATES Number of staff quarters owned- 19 Number of staff quarters rented- 114 Total number of staff quarters- 133\n\n#### Corps Legal Office\n\nTable 20: Summary of Activities\n\n2 nd\n\n3 rd\n\n4 th\n\nTotal\n\nSUBJECT MATTER 1 st Quarter\n\nS/ N\n\nQuarter\n\nQuarter\n\nQuarter\n\n5 6 9 3 23 a. Number of Mobile Court sessions conducted within the quarter\n\n| b | Number of Offenders | | | | 347 | | | | | | 312 | | 674 | | | | 542 | | 1875 |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| | Prosecuted | | | | 261 | | | | | | 286 | | 550 | | | | 413 | | |\n| | Number of Offenders | | | | 86 | | | | | | 26 | | 124 | | | | 29 | | |\n\nb Number of Offenders Prosecuted Number of Offenders convicted Number of Offenders Acquitted\n\n| c. | Number of Civil cases | | | | 96 | | | | | | 106 | | 101 | | | | 106 | | 1510 |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| | pending in regular Courts | | | | | | | | | | | | | | | | | | |\n| d. | Number of cases won/ lost | | | | 10 civil | | | | | | 3 Civil | | 3 | | wom | | 3 | | 265 |\n| | in regular courts | | | | cases | | | | | | cases | | | | | | | | |\n| | | | | | won | | | | | | won | | 3 lost | | | | 1 | | |\n| | | | | | | 6 No | | | | | No | Civil | | | | | | | |\n| | | | | | civil | | | | | | Case Lost | | | | | | | | |\n| | | | | | cases | | | | | | | | | | | | | | |\n| | | | | | lost | | | | | | | | | | | | | | |\n| e. | Number of Criminal cases | | | | 63 | | | | | | 62 | | 58 | | | | 60 | | 409 |\n| | prosecuted by the Corps | | | | | | | | | | | | | | | | | | |\n| f. | Number of Civil | cases on | | | 27 | | | | | | 27 | | 25 | | | | 28 | | 16 |\n\nf. Number of Civil cases on Appeal\n\nNil Nil 1 1 9\n\nNumber of cases settled via ADR\n\n4 1 1 2 243 g. Number of Commands that conducted auction in the quarter under review.\n\n| h. | Number of MOUs, Tenancy | | | | 43 | | | | | | 43 | | 37 | | | | 43 | | 107 |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| | Agreements and other | | | | prepared | | | | | | prepared | | prepared | | | | prepared | | |\n| | Consultancy agreements | | | | | 50 | | | | | 53 | | 36 | | | | 30 | | |\n| | prepared and executed. | | | | Executed | | | | | | Executed | | executed | | | | executed | | |\n| i. | Number Of Disciplinary | | | | 40 | | | | | | 76 | | 59 | | | | 76 | | 2 |\n| | cases reviewed for the | | | | | | | | | | | | | | | | | | |\n\nquarter.\n\nTable 21: Patients Attended to by FRSC Hospitals and Roadside Clinics Nationwide\n\n#### Corps Medical and Rescue Office\n\nS/N COMMAND\n\nSUMMARY OF \nRTC \nPATIENTS \nSUMMARY OF \nNON RTC \nPATIENTS \n1 RS1.16 KAKAU 97 108 \n2 RS1.17/BYERO 39 60 \n3 RS1.25 CHIROMAWA 5 125 \n4 RS1.33 KOZA 6 83 \n5 RS1.34 MALFASHI 24 112 \n6 RS2.2 ITORI 11 166 \n7 RS2.25 SGM 14 70 \n8 RS3.13 GIREI 18 60 \n9 RS4.13 H/KIBO 46 91 \n10 RS4.23 K/ALA 7 169 \n11 RS4.24 ALIADE 5 177 \n12 RS4.3 SHABU 12 76 \n13 RS5.12OLUKU 18 241 \n14 RS5.23 I/UKU 19 29 \n15 RS5.33 NTEJE 4 166 \n16 RS6.11 ELEME 41 165 \n17 RS7.12 ABAJI 77 65 \n18 RS7.21 MOKWA 0 155 \n19 RS8.11 B/SAADU 8 87 \n20 RS8.12 OMU ARAN 15 94 \n21 RS8.15 OLOORU 52 130 \n22 RS8.34 ZARIAGI 7 84 \n23 RS9.32 KM 78 ABA 6 195 \n24 RS10.31 T. MAFARA 0 122 \n25 RS11.12 ILESHA 38 133 \n26 RS11.13 I/IJESHA 19 115 \n27 RS12.13 ALKALERI 42 135 \n28 RS12.25 MAINOK 9 412 \n29 RS12.13 ALKALERI 6 172 \nTotal 645 3797 \n\nChart 12: RTC Cases and Medical Cases Chart illustrating Summary of Patients Attended to\n\nRS1.16 KAKAU\n\nRS1.17/BYERO\n\nRS1.25 CHIROMAWA\n\nRS1.33 KOZA\n\nRS1.34 MALFASHI\n\nRS2.2 ITORI\n\nRS2.25 SGM\n\nRS3.13 GIREI\n\nRS4.13 H/KIBO\n\nRS4.23 K/ALA\n\nRS4.24 ALIADE\n\nRS4.3 SHABU\n\nRS5.12OLUKU\n\nRS5.23 I/UKU\n\nRS5.33 NTEJE\n\nRS6.11 ELEME\n\nRS7.12 ABAJI\n\nRS7.21 MOKWA\n\nRS8.11 B/SAADU\n\nRS8.12 OMU ARAN\n\nRS8.15 OLOORU\n\nRS8.34 ZARIAGI\n\nRS9.32 KM 78 ABA\n\nRS10.31 T. MAFARA\n\nRS11.12 ILESHA\n\nRS11.13 I/IJESHA\n\nRS12.13 ALKALERI\n\nRS12.25 MAINOK\n\nRS12.13 ALKALERI\n\nSUMMARY OF NON RTC \nPATIENTS \nSUMMARY OF RTC \nPATIENTS \n\nTable 22: Status of Ambulances in Zebra Centers As At10 th Nov, 2022)\n\n| S/N | | CODE | | | COMMANDS | | | REG. | | NO | | | VEH. | TYPE | | | | | STATUS | | |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| 1 | | CODE | | | COMMANDS | | | REG. | | NO | | | VEH. | | TYPE | | | | STATUS | | |\n| 2 | | ZEBRA 1 | | | OLD PARADE GROUND AREA | | | A01 601 RS | | | | | PEUGEOT | | PARTNER | | | | NON | - | FUNCTIONAL |\n\n##### 2 ZEBRA 1 OLD PARADE GROUND AREA 10\n\n3 ZEBRA 2 CBD, ABUJA C01 14 RS NISSAN URVAN FUNCTIONAL 4 ZEBRA 3 ABUJA CITY GATE COI 117 RS NISSAN URVAN FUNCTIONAL 5 ZEBRA 4 KUBWA ROAD C01 108 RS NISSAN URVAN FUNCTIONAL 6 ZEBRA 5 KEFFI BY FLY OVER C01 13 RS NISSAN URVAN FUNCTIONAL 7 ZEBRA 6 YANGOJI A01 859 RS NISSAN URVAN FUNCTIONAL 8 ZEBRA 7 SABONWUSE A01 50 RS PGT EXPERT FUNCTIONAL 9 ZEBRA 8 GIRI AOI 44 RS PEUGEOT EXPERT FUNCTIONAL\n\nNON – FUNCTIONAL NON - FUNCTIONAL\n\n##### 10 ZEBRA 9 KOTON KARFE AO1 43 RS/ AOI 111 RS AO1 897 RS\n\nFORD TRANSIT FORD E-350 IVM (INNOSON)\n\nFUNCTIONAL 11 ZEBRA 10 BARDE JUNCTION KADUNA A01 47 RS FORD TRANSIT NON – FUNCTIONAL\n\n##### 12 ZEBRA 11 MARARABAN JAMA’A JOS A01 48 RS A01 897 RS\n\nFORD TRANSIT PGT EXPERT\n\nNON – FUNCTIONAL FUNCTIONAL 13 ZEBRA 12 KUGBO NYANYA ROAD A01 998 RS NISSAN URVAN FUNCTIONAL 14 ZEBRA 13 GEGU A01 996 RS NISSAN URVAN FUNCTIONAL\n\nA01 035 RS FORD T-350 NON – FUNCTIONAL\n\n##### 15 ZEBRA 14 AIRPORT JUNCTION, GOMBE STATE\n\nFUNCTIONAL\n\n##### 16 ZEBRA 15 KATARI A01 637 RS NISSAN URVAN NV350\n\n17 ZEBRA 16 ABAJI, A01 590 RS PGT EXPERT FUNCTIONAL 18 ZEBRA 17 KUDU, NIGER STATE A01 589 RS PGT EXPERT FUNCTIONAL 19 ZEBRA 18 OWAN ESEGIE, A01 591 RS PGT EXPERT FUNCTIONAL 20 ZEBRA 19 IPETU IJESA A01 594 RS PGT EXPERT FUNCTIONAL 21 ZEBRA 20 OWO JUNCTION A01 592 RS PGT EXPERT FUNCTIONAL 22 ZEBRA 21 ILARA MOKIN A01 593 RS PGT EXPERT FUNCTIONAL 23 ZEBRA 22 SHUWARIN JIGAWA STATE A01 120 RS TYT HIACE FUNCTIONAL\n\nA01 356 RS FORD E-350 FUNCTIONAL\n\n##### 24 ZEBRA 23 AIRPORT ROAD ILORIN, KWARA\n\nA01 895 RS PGT EXPERT TEPEE FUNCTIONAL\n\n##### 25 ZEBRA 24 MUTUM-BIYU, TARABA STATE\n\n26 ZEBRA 25 GOSHEN CO1 136 RS NISSAN URVAN FUNCTIONAL 27 ZEBRA 26 CHAM, GOMBE STATE A01 484 RS T-350 FORD FUNCTIONAL 28 ZEBRA 27 DOKA, A01 853 RS NISSAN URVAN FUNCTIONAL 29 ZEBRA 28 JOS BY-PASS A01 634 RS NISSAN FUNCTIONAL 30 ZEBRA 29 ALKALERI A01 636 RS NISSAN FUNCTIONAL 31 ZEBRA 30 TORO/MAGAMA A01 635 RS NISSAN FUNCTIONAL 32 ZEBRA 31 ABAKALIKI A01 638 RS NISSAN FUNCTIONAL 33 ZEBRA 32 ENUGU A01 642 RS NISSAN URVAN FUNCTIONAL 34 ZEBRA 33 YAHE A01 640 RS NISSAN FUNCTIONAL 35 ZEBRA 34 ZUBA A01 639 RS NISSAN FUNCTIONAL 36 ZEBRA 35 ZARIA TOLL GATE A01 641 RS NISSAN FUNCTIONAL 37 ZEBRA 36 CHIROMAWA A01 643 RS NISSAN FUNCTIONAL 38 ZEBRA 37 OGUNMAKIN A01 644 RS NISSAN FUNCTIONAL 39 ZEBRA 38 LUGBE, ABUJA A01 987 RS NISSAN URVAN FUNCTIONAL 40 ZEBRA 39 ZIPHE ALHERI CAMP - - -41 ZEBRA 40 MANDO AOI 972 RS IVM (INNOSON) FUNCTIONAL\n\nA01 230 RS NISSAN FUNCTIONAL\n\n##### 42 ZEBRA 41 STATE POLY GATE JALINGO TARABA STATE\n\n##### 43 ZEBRA 42 WAJARI, GOMBE STATE A01 857 RS NISSAN URVAN FUNCTIONAL\n\nA01 855 RS NISSAN URVAN FUNCTIONAL\n\n##### 44 ZEBRA 43 NATACO JUNCTION, LOKOJA, KOGI STATE\n\n45 ZEBRA 44 SIUN, ABEOKUTA C01 110 RS NISSAN URVAN FUNCTIONAL \n46 ZEBRA 45 DARAZO BAUCHI STATE C01 15 RS NISSAN URVAN FUNCTIONAL \n47 ZEBRA 46 IBEJU-LEKKI, LAGOS \nSTATE \n\nC01 116 RS NISSAN URVAN FUNCTIONAL\n\n48 ZEBRA 47 DUTSE ALHAJI FCT, ABUJA A01 712 RS MERCEDES BENZ FUNCTIONAL 49 ZEBRA 48 IKARE EKITI A01 858 RS NISSAN URVAN FUNCTIONAL 50 ZEBRA 49 IKOM - - -51 ZEBRA 50 IJEBU – ODE CO1 135 RS NISSAN URVAN FUNCTIONAL\n\nFUNCTIONAL - 46 \nNON FUNCTIONAL - 6 \nNO OF AMBULANCES - 52 \nFRSC Statistical Digest 26 \n\nTable 14: Emergency Ambulance Service Scheme (EASS) Zebra 1-54) Zebra Locations\n\nS/N ZEBRA ZEBRA LOCATION CUG NUMBER 1 ZEBRA 1 OLD PARADE GROUND AREA 10 GARKI, ABUJA. 08077690898 2 ZEBRA 2 CBD OPPOSITE FEDERAL SECRETARIAT, ABUJA. 08077690897 3 ZEBRA 3 CITY GATE, ABUJA. 08077690896 4 ZEBRA 4 KUBWA ROAD, ABUJA. 08077690899 5 ZEBRA 5 KEFFI BY FLY OVER, NASSARAWA STATE. 08077690126 6 ZEBRA 6 YANGOJE, ALONG LOKOJA ROAD, FCT. 08073374912 7 ZEBRA 7 SABON WUSE, ALONG KADUNA ROAD NIGER STATE.\n\n08056294319\n\n8 ZEBRA 8 GIRI JUNCTION ALONG GWAGWALADA ZUBA ROAD ABUJA.\n\n08151790087\n\n9 ZEBRA 9 KOTON KARFE, KOGI STATE. 08151790088 10 ZEBRA 10 BARDE JUNCTION ALONG JOS ROAD, KADUNA STATE.\n\n08151790089\n\n11 ZEBRA 11 MARARABAN JAMA’A ROUND ABOUT, JOS. PLATEAU STATE.\n\n08151790090\n\n12 ZEBRA 12 KUGBO, NYANYA ROAD BY KARU FLY OVER, ABUJA FCT.\n\n08151790091\n\n13 ZEBRA 13 GEGU, KOGI STATE. 08150654679 14 ZEBRA 14 AIRPORT JUNCTION ALONG BAUCHI GOMBE ROAD, GOMBE STATE.\n\n08150654680 \n15 ZEBRA 15 KATARI, KADUNA STATE. 08150654681 \n16 ZEBRA 16 ABAJI, FCT. 08150654682 \n17 ZEBRA 17 KUDU, NIGER STATE. 08150654683 \n18 ZEBRA 18 OWAN ESEGIE, EDO STATE. 08150654684 \n19 ZEBRA 19 IPETU IJESA, OSUN STATE. 08150654685 \n20 ZEBRA 20 OWO JUNCTION, ONDO STATE. 08150564686 \n21 ZEBRA 21 ILARA MOKIN, ONDO STATE. 08150564687 \n22 ZEBRA 22 SHUWARIN, JIGAWA STATE. 08058298541 \n23 ZEBRA 23 AIR PORT ROAD, ILORIN, KWARA STATE. 08058298542 \n24 ZEBRA 24 MUTUM BIYU, TARABA STATE. 08058298543 \n25 ZEBRA 25 BY WOLE SOYINKA FRSC HOUSING ESTATE SIGN \nPOST GOSHEN, NASARAWA STATE. \n09053976950 \n26 ZEBRA 26 CHAM, GOMBE SATATE. 09053976951 \n27 ZEBRA 27 DOKA, KADUNA STATE 09053976952 \n28 ZEBRA 28 JOS BY-PASS, PLATEAU STATE 09053976953 \n29 ZEBRA 29 ALKALERI, BAUCHI STATE 09053976954 \n30 ZEBRA 30 TORO/MAGAMA, BAUCHI STATE 09053976955 \n31 ZEBRA 31 ABAKALIKI, EBONYI STATE 09053976956 \n33 ZEBRA 32 ABAKALIKI ROAD, ENUGU STATE 09053976957 \n33 ZEBRA 33 YAHE, CROSS RIVER STATE 09053976958 \n34 ZEBRA34 ZUBA, FCT 09053976959 \n35 ZEBRA 35 ZARIA TOLL GATE, KADUNA STATE 09053976960 \n36 ZEBRA 36 CHIROMAWA, KANO STATE 09053976961 \n37 ZEBRA 37 OGUNMAKIN, OGUN STATE 09053976962 \n38 ZEBRA 38 IDU RAILWAY STATION, ABUJA 09058611870 \n39 ZEBRA 39 ALHERI CAMP 09058611871 \n40 ZEBRA 40 MANDO, KADUNA STATE 09058611880 \n41 ZEBRA 41 POLY GATE, TARABA STATE 08052898420 \n42 ZEBRA 42 KWADON, GOMBE STATE 08052898422 \n43 ZEBRA 43 NATACO JUNCTION, LOKOJA, KOGI STATE 08111398098 \n44 ZEBRA 44 SIUN, ABEOUKUTA, OGUN STATE 08111398107 \n45 ZEBRA 45 DARAZO, BAUCHI STATE 08111398160 \n46 ZEBRA 46 IBEJU-LEKKI, LAGOS STATE 08111398172 \n47 ZEBRA 47 DUTSE ALHAJI, FCT 08111398173 \n48 ZEBRA 48 IKARE, ONDO STATE 09154291452 \n49 ZEBRA 49 IKOM, CROSS RIVER STATE 09154291453 \n50 ZEBRA 50 IJEBU-ODE, OSUN STATE 09154291454 \n51 ZEBRA 51 HAWAN KIBO, PLATEAU STATE 09154291513 \n52 ZEBRA 52 HONG, ADAMAWA STATE - \n53 ZEBRA 53 ORE, ONDO STATE - \n54 RRS RSHQ RSHQ FCT - \n\n#### Corps Public Education Office\n\nTable 23: Details Of Public Education Activities\n\nTotal\n\nSN COMMA ND\n\nRoad Shows\n\nRadio Progra mmes\n\nTV Progra mmes\n\nMotor park Rallies\n\nVisit to TRD/ST k\n\nChurch Advocac y\n\nMosque Advocac y\n\nSchool Advocac y\n\nMarket Advocac y\n\nNewspap er Publicati ons\n\nTown \nHall \nMeeting \ns \n\nSemi nars/ work shops\n\n1 RS1HQ\n\n200 215 215 603 268 218 232 215 100 14 200 22 2502\n\n2 RS2HQ\n\n221 200 200 721 422 237 289 257 119 19 215 25 2925\n\n3 RS3HQ\n\n2058\n\n186 197 150 467 300 235 200 110 87 20 96 10\n\n4 RS4HQ\n\n2113\n\n201 214 186 425 300 241 215 121 69 17 114 10\n\n5 RS5HQ\n\n2243\n\n221 210 184 489 321 219 258 145 50 18 118 10\n\n6 RS6HQ\n\n2019\n\n215 216 160 421 258 220 189 135 64 27 100 14\n\n7 RS7HQ\n\n2413\n\n226 220 216 496 301 260 250 145 69 18 201 11\n\n8 RS8HQ\n\n2062\n\n219 213 145 422 278 262 215 134 64 14 87 9\n\n9 RS9HQ\n\n2016\n\n215 225 156 472 220 213 220 119 60 13 89 14\n\n1943\n\n10 RS10H Q\n\n200 200 171 400 243 253 218 110 50 10 78 10\n\n11 RS11HQ\n\n2094\n\n211 214 180 515 276 215 220 120 32 11 87 13\n\n2053\n\n12 RS12H Q\n\n| | | | | 215 | | 221 | | 122 | 432 | 267 | | 315 | | 198 | | | 132 | | 40 | | 12 | | | 86 | | 13 | | | |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| TOTAL | | | 2530 | | | 2545 | | 2085 | 5863 | 3454 | | 2888 | | 2704 | | 1743 | | | 804 | | 193 | | 1471 | | 161 | | | 26441 | |\n| LEGEND: | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |\n| RP: Radio Programme | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |\n\nLEGEND: \nRP: Radio Programme \nTVP: TV Programme \nNPP: News Paper Publication \nMPR: Motor Park Rallies \nNDL: Lecture on NDL \nADV: Advocacy Visit \nDPE: Domesticated Public Education \n\nTable 24: CPEO Activities comparison\n\nChart 13: Illustration of Comparative analysis of Public Education Activities\n\nSN COMMAND\n\nRadio Program mes\n\nTV \nProgram \nmes \nNewspap \ner \nPublicati \nons \n\nMotor park Rallies\n\nChurch Advocac y\n\nMosque Advocac y\n\nSchool Advocac y\n\nMarketA dvocacy\n\nRoad Shows\n\nTown \nHall \nMeetings \n\nVisit to TRD/ST k\n\nSeminars /worksho ps\n\nTotal\n\n1\n\nTOTAL\n\n1st Q\n\n1465 1624 1433 5432 2078 1856 1374 1090 920 101 1399 104 18876\n\n2\n\nTOTAL\n\n2st Q\n\n1555 1664 1544 5440 2776 2700 2455 1365 838 141 1523 170 22171\n\n3\n\nTOTAL\n\n3rd Q\n\n1849 1713 1761 5371 2893 2778 2582 1687 803 212 1813 193 23655\n\n4\n\nTOTAL\n\n4th Q\n\n2530 2545 2085 5863 3454 2888 2704 1743 804 193 1471 161 26441\n\nG TOTAL\n\n0 1000 2000 3000 4000 5000 6000 7000\n\nRadio Programmes\n\nTV Programmes\n\nNewspaper Publications\n\nMotor park Rallies\n\nChurch Advocacy\n\nMosque Advocacy\n\nSchool Advocacy\n\nMarketAdvocacy\n\nRoad Shows\n\nTown Hall Meetings\n\nVisit to TRD/STk\n\nSeminars/workshops\n\nTOTAL 4th Q\n\nTOTAL 3rd Q\n\nTOTAL 2st Q\n\nTOTAL 1st Q\n\n#### Corps Transport and Standardization Office\n\nTable 25: Comparative Summary of Fleet Operators’ Travel\n\nFleet Operators Registered\n\nLuxury Bus Travelled Advocacy Visit Kilometers Covered\n\nPassenger Travelled\n\nVehicle Travelled\n\nQuarterly\n\nFirst Quarter, 2022 15 7,917,804 996,420 9,896 7,790 226,131,810 Second Quarter, 2022 17 7,475,205 782,051 6,170 7,579 193,101,409 Third Quarter 2022 10 7,312,656 766,227 4,285 7,291 225,819,718 Forth Quarter 2022 6 8,975,066 1,017,352 36,166 40,019 291,086,068\n\nTotal 48 31,680,731 3,562,050 56,517 62,679 936,139,005\n\nTable 26:Summary Of RTC Involving Fleet Operators\n\n| Quarterly | Number Of Cases | Number Killed | Number Injured | Total Casualties | Number Of Persons Involved |\n| --- | --- | --- | --- | --- | --- |\n| First Quarter, 2022 | 82 | 116 | 353 | 469 | 1,137 |\n| Second Quarter, 2022 | 99 | 210 | 332 | 542 | 1,006 |\n| Third Quarter 2022 | 105 | 51 | 434 | 485 | 1,202 |\n| Forth Quarter 2022 | 113 | 95 | 492 | 587 | 1,357 |\n| Total | | | | | |\n\nChart 14: Summary Of RTC Involving Fleet Operators\n\nNumber Of Persons Involved\n\nTotal Casualties\n\nForth Quarter 2022\n\nThird Quarter 2022\n\nNumber Injured\n\nSecond Quarter, 2022\n\nNumber Killed\n\nFirst Quarter, 2022\n\nNumber Of Cases\n\n0 500 1000 1500\n\nTable 27: Summary Of RTC Involving Fleet Operators\n\nS/NO FLEET OPERATOR\n\nNUMBER OF CASES\n\nNUMBER KILLED\n\nNUMBER INJURED\n\nTOTAL CASUALTIES\n\nNUMBER OF PERSONS INVOLVED 1 DANGOTE 18 29 55 84 155 2 NURTW 18 12 70 82 208 3 BUA CEMENT 4 1 5 6 11 4 GOMBAWA MOTORS 3 0 12 12 29 5 NIGERIAN ARMY 4 2 14 16 42 6 NNPC 3 2 9 11 18 7 NUPENG 3 0 3 3 16 8 PEACE MASS TRANSIT 3 3 12 15 68 9 YANKARI EXPRESS 3 0 43 43 64 10 AKTC TRANSPORT COMPANY 2 0 5 5 25 11 BENUE LINKS 2 0 10 10 22 12 ABEJUKOLO LGA 1 1 8 9 19 13 ABIA LINE 1 2 8 10 10 14\n\nABU-WAY TRANS MOTORS 1\n\n0 6 6 14 15 ADAMAWA MASS TRANSIT 1 0 4 4 11 16 ALIKO /ALIKO 1 0 0 0 4 17 BORNO EXPRESS 1 0 5 5 36 18 BOVAS 1 0 1 1 34\n\n19\n\nBY HIS GRACE MASS TRANSIT,RIVERS \nJOY 1 0 2 2 43 \n20 CHISCO 1 3 12 15 15 \n21 COCA COLA 1 1 0 1 1 \n22 CONOIL 1 1 0 1 3 \n\n23\n\nDANZURU BABA BUSINESS/ CROWN \nFLOWER MILL/ ALIMANI BUSINESS \nVENTURES 1 0 7 7 15 \n24 DTM LAGOS 1 0 0 0 2 \n25 DUFIL PRIMA 1 0 2 2 3 \n\n26\n\nENUGU STATE TRANSPORT COMPANY \nLTD 1 0 5 5 18 \n27 FEDEX & NIG LIMITED 1 0 0 0 2 \n28 FLIGHT 1 0 5 5 5 \n29 INTER STATE 547447 1 0 2 2 4 \n30 KADUNA LINE 1 1 8 9 9 \n\n31\n\nKANO STATE SECONDARY MANAGEMENT BOARD 1 0 11 11 20\n\n32\n\nKATSINA STATE TRANSPORT AUTHORITY\n\n1\n\n0 19 19 20 33 KOPEK CONS COY 1 0 11 11 16 34 KSTA 1 3 21 24 26 35 LANDSTAR EXPRESS 1 0 2 2 52 36 MASS TRANSMIT 1 0 0 0 10\n\n37 MIRABEL EDUCATION CENTER 1 0 1 1 5 38 NPF 1 0 4 4 4 39 NSCDC 1 0 4 4 10 40 OBIOMA COMPANY 1 0 0 0 5 41 OSUN GOVT 1 3 10 13 15 42 PEPSI 1 0 2 2 3 43 PHCN 1 0 3 3 26\n\n44 PINNACLE DELIVERY SERVICE 1 1 1 2 1 45 QURAAN COMPETITION 1 0 2 2 12 46 QUTEN 1 0 2 2 5 47 ROYAL 1 0 7 7 7 48 SAMCHASE 1 0 2 2 3 49 SD EXPRESS 1 0 1 1 49\n\n50\n\nSHEMA PETROLEUM/CONOIL/NURTW \nABUJA 1 2 3 5 8 \n51 SILVER BIRD 1 3 7 10 10 \n52 SO TRANS 1 1 2 3 3 \n\n53 SOKOTO STATE TRANSPORT SERVICE 1 0 1 1 15 54 SUNSHINE 1 0 0 0 1 55 WAZOBIA 1 0 5 5 13 56 WINNERS FLEET 1 0 10 10 16 57 YOBE LINE 1 5 19 24 26 58 YOUNG SHALL GROW 1 0 3 3 12 59 YOUNGJET 1 3 12 15 15 60 ZAMFARA STATE 1 12 8 20 20\n\nChart 15: Summary Of RTC Involving Fleet Operators\n\nDANGOTE\n\nNURTW\n\nBUA CEMENT\n\nGOMBAWA MOTORS\n\nNIGERIAN ARMY\n\nNNPC\n\nNUPENG\n\nPEACE MASS TRANSIT\n\nYANKARI EXPRESS\n\nAKTC TRANSPORT COMPANY\n\nBENUE LINKS\n\nABEJUKOLO LGA\n\nABIA LINE\n\nABU-WAY TRANS MOTORS\n\nADAMAWA MASS TRANSIT\n\nALIKO /ALIKO\n\nBORNO EXPRESS\n\nBOVAS\n\nBY HIS GRACE MASS TRANSIT,RIVERS…\n\nCHISCO\n\nCOCA COLA\n\nCONOIL\n\nDANZURU BABA BUSINESS/ CROWN…\n\nDTM LAGOS\n\nDUFIL PRIMA\n\nENUGU STATE TRANSPORT COMPANY…\n\nFEDEX & NIG LIMITED\n\nFLIGHT\n\nINTER STATE 547447\n\nKADUNA LINE\n\nKANO STATE SECONDARY…\n\nKATSINA STATE TRANSPORT…\n\nKOPEK CONS COY\n\nKSTA\n\nLANDSTAR EXPRESS\n\nMASS TRANSMIT\n\nMIRABEL EDUCATION CENTER\n\nNPF\n\nNSCDC\n\nOBIOMA COMPANY\n\nOSUN GOVT\n\nPEPSI\n\nPHCN\n\nPINNACLE DELIVERY SERVICE\n\nQURAAN COMPETITION\n\nQUTEN\n\nROYAL\n\nSAMCHASE\n\nSD EXPRESS\n\nSHEMA PETROLEUM/CONOIL/NURTW…\n\nSILVER BIRD\n\nSO TRANS\n\nSOKOTO STATE TRANSPORT SERVICE\n\nSUNSHINE\n\nWAZOBIA\n\nWINNERS FLEET\n\nYOBE LINE\n\nYOUNG SHALL GROW\n\nYOUNGJET\n\nZAMFARA STATE\n\nNUMBER OF PERSONS INVOLVED\n\nTOTAL CASUALTIES\n\nNUMBER INJURED\n\nNUMBER KILLED\n\nNUMBER OF CASES\n\n#### Corps Secretary\n\nTable 28: Corps Secretary Activities\n\nChart 16: Corps Secretary Activities\n\nS/N ACTIVITY FREQUENCY \nQ1 Q2 Q3 Q4 Total \n1 Number of Mails Received 2200 1968 2876 1625 8669 \n2 Number of Mails \nDispatched 2309 1799 \n2789 1605 8502 \n3 Number of files Received 662 546 929 807 2944 \n4 Number of files \nDispatched 538 593 \n594 694 2419 \n\n5\n\nApplication for \nCompassionate Transfer 4 1 \n56 76 137 \n6 Number of staff retired 31 31 09 10 81 \n7 Resignation 36 39 40 40 155 \n8 Notification of Death 6 4 06 12 28 \n9 Application for Annual \nLeave 74 210 \n575 244 1103 \n10 Permission to get Married 106 132 136 46 420 \n11 Application for Change of \nName 31 \n\n23\n\n76\n\n38 168\n\n12 Application for Permission to perform Holy Pilgrimage 5 13\n\n09 03 30\n\n13 Application for Maternity Leave 2 11 07 20 40\n\n14 Number on Posting of Officers 19 17 11 12 59\n\nNumber of Mails Received\n\nNumber of Mails Dispatched\n\nNumber of files Received\n\nNumber of files Dispatched\n\nApplication for Compassionate…\n\nNumber of staff retired\n\nResignation\n\nNotification of Death\n\nApplication for Annual Leave\n\nPermission to get Married\n\nApplication for Change of Name\n\nApplication for Permission to…\n\nApplication for Maternity Leave\n\nNumber on Posting of Officers\n\nFREQUENCY Q4\n\nFREQUENCY Q3\n\nFREQUENCY Q2\n\nFREQUENCY Q1\n\n#### Corps Provost\n\nTable 29: Misconduct in Details\n\nChart 14: Misconduct\n\nS/N OFFENCES Q1 Q2 Q3 Q4 TOTAL \n1 TWO FIGHTING 0 0 1 6 7 \n2 RUDE CONDUCT 17 16 13 40 86 \n3 DERELICTION OF DUTY 16 17 13 21 67 \n4 SCANDALOUS BEHAVIOR 1 3 0 0 4 \n5 USE OF INDOLENT WORDS 0 0 0 0 0 \n6 IMPROPER DRESSING 230 282 235 270 1017 \n7 LATENESS 399 319 640 437 1795 \n8 DESERTION 9 8 13 19 49 \n9 FAILURE TO PAY LAWFUL DEBT 5 4 1 0 10 \n10 INSUBORDINATION 12 19 8 11 50 \n11 NEGLIGENCE TO DUTY 10 7 4 32 53 \n12 PATROL MISCONDUCT 42 7 0 10 59 \n13 FAILURE TO PAY COMPLIMENT 10 5 4 5 24 \n14 ABSENTEEISM 303 345 608 482 1738 \n15 DISOBEDIENCE TO PARTICULAR \nORDER \n\n127 136 200 249 712\n\n16 AWOL 15 14 22 14 65 17 ABANDONMENT OF DUTY 27 4 14 23 68 18 MALINGARING 1 0 0 1 2 19 MISSCONDUCT 0 2 0 13 15 20 DAMAGE TO FRSC PROPERTY 0 0 0 2 2 21 NDL RACKETEERING 1 0 0 0 1 22 GROSS MISCONDUCT 0 0 6 0 6 23 MISSAPPROPRIATION 0 0 1 3 4 24 FALSE ACCUSATION 0 0 1 0 1 25 ALLEGED THEFT 0 0 0 2 2 26 DESERTION 0 0 0 19 19 27 OTHERS 5 0 0 0 5 TOTAL 1263 1188 0 1659 5861\n\n0 100 200 300 400 500 600 700\n\nTWO FIGHTING\n\nDERELICTION OF DUTY\n\nUSE OF INDOLENT WORDS\n\nLATENESS\n\nFAILURE TO PAY LAWFUL DEBT\n\nNEGLIGENCE TO DUTY\n\nFAILURE TO PAY COMPLIMENT\n\nDISOBEDIENCE TO PARTICULAR ORDER\n\nABANDONMENT OF DUTY\n\nMISSCONDUCT\n\nNDL RACKETEERING\n\nMISSAPPROPRIATION\n\nALLEGED THEFT\n\nOTHERS\n\nQ4\n\nQ3\n\nQ2\n\nQ1\n\n#### SERVICOM\n\nTable 30: Complaints Received from Customers\n\nNumber of appreciation through: e-mail, calls and SMS = 290\n\nLegend: \nSCRAR Servicom Customer Relations Activities Register \nOPS Operations \nNDL National Drivers License \nOFV Other Offences Violation \n\nCOMPLAINTS RECEIVED FROM CUSTOMERS\n\nNATURE \nOF \nCOMPLAIN \nTS \n\nCOMPLAIN TS FROM COMMAND S\n\nPHONE &\n\nSMS/E MAIL\n\nLETTER SOURCES OF COMPLAINTS INTERNAL /EXTERNAL\n\nTOTAL\n\nOPS 148 20 19 187 374 NDL 70 25 15 110 220 OFL 26 10 7 43 86 Total 244 55 41 340 680\n\n#### Corps Safety Engineering\n\nTable 31: Six Death (6DT) Accident Investigated\n\n6DT For \n1 st \n\n##### Quarter\n\n2022 \n6DT For \n2 nd \n\n##### Quarter\n\n2022 \n6DT For \n3 rd \n\n##### Quarter\n\n2022 \n6DT For \n3 rd \n\n##### Quarter\n\n2022\n\n##### TOTAL\n\n32 30 34 38 134\n\n6DT For 1st Quarter 2022\n\n6DT For 2nd Quarter 2022\n\n6DT For 3rd Quarter 2022\n\n6DT For 3rd Quarter 2022 Series1 32 30 34 38\n\n0\n\n5\n\n10\n\n15\n\n20\n\n25\n\n30\n\n35\n\n40\n\nChart 17: 6DT Accident Investigated For\n\n1st Q, 2nd Q, 3rd Q and 4th Q 2022\n\nFederal Road Safety Corps, Nigeria: \nISO 9001:2015 QMS Certified Law Enforcement Agency in \nAfrica. \n\nFRSC WEBSITE : www.frsc.gov.ng\n\nE-mail: info@frsc.gov.ng Twitter handle: @FRSCNigeria\n\n## 122 - TOLL FREE\n\n##### 08056294007 SERVICOM Nodal Officer\n\n##### 08056799857 – Complaint Desk\n\n"
  },
  {
    "url": "https://zenodo.org/records/10411123",
    "text": "Transport Starter Data Kit: Historical socio-transport data for Nigeria\n\nPublished December 20, 2023 | Version 3.0\n\nDataset Open\n\n# Transport Starter Data Kit: Historical socio-transport data for Nigeria\n\nShow affiliations\n\n- 1. Loughborough University\n- 2. Imperial College London\n- 3. SLOCAT Partnership\n\nThis Transport Starter Data Kit contains historical annual data (1990–2021) on passenger and freight activity, segregated by mode and fuel. Additionally, historical data on energy intensities, load factors, vehicle stock, population (total, urban, rural, growth), and GDP (total, agriculture, construction, mining, manufacturing, service, energy, growth) are included in the kit, within the 'Data' tab. The historical data can be used as a foundation for transport-energy modelling and/or to identify areas of improvement. This data was verified through consultation with relevant stakeholders before publishing. The definition used for each vehicle mode is found in the 'Definitions' tab, and the description of each data observation status is found in the 'Notes' tab. All data sources are linked where possible.\n\n## Files\n\n### Nigeria.pdf\n\n### Files (485.5 kB)\n\nmd5:467588ae7376787280fedbe87f11cfee\n\nmd5:a065ac3a8fc2ffbb40eb2c4a659cbb58\n\n| Name | Size | Download all |\n| --- | --- | --- |\n| 309.0 kB | Preview Download |\n| 176.6 kB | Download |\n\nJump up"
  },
  {
    "url": "https://nairametrics.com/2024/03/12/nigerias-vehicle-imports-increased-by-226-4-in-2023/",
    "text": "Nigeria’s vehicle imports increased by 226.4% in 2023 - Nairametrics\n\n- Login\n- Register\n\n- Access Holdings Offer\n- Fidelity Bank Offer\n- GTCO Offer\n- Zenith Bank Offer\n\n- Company Results\n- Dividends\n- Public Offer & Right Issues\n- Stock Market News\n\n- Career tips\n- Personal Finance\n\n- Access Holdings Offer\n- Fidelity Bank Offer\n- GTCO Offer\n- Zenith Bank Offer\n\n- Company Results\n- Dividends\n- Public Offer & Right Issues\n- Stock Market News\n\n- Career tips\n- Personal Finance\n\n# Nigeria’s vehicle imports increased by 226.4% in 2023\n\nin Economy, Macros\n\nShare on Facebook Share on Twitter Share on Linkedin\n\nThe total value of Nigeria’s used vehicle import increased significantly from N325.05 billion in 2022 to N1.063 trillion in 2023- marking an increase of 226.46% over a one-year period.\n\nBetween 2022 and 2023, the value of used vehicle imported into the country jumped by N736 billion compared to what was recorded in 2022. This is according to the National Bureau of Statistics (NBS) foreign trade report for 2023.\n\nThe significant rise recorded in 2023 can be attributed to the huge increase in vehicle imports for Q2, 2023 at N733.91 billion representing about 69% of total imports for the year 2023.\n\n### MoreStories\n\n### Cheapest and most expensive states to be a worker in Nigeria\n\n### Minimum Wage 2026: What every state in Nigeria pays\n\n##### Recommended reading: Nigeria’s import bill surges 66.8% in 4th quarter of 2023\n\nFor Q1 2022, Nigeria total used vehicle import stood at N72.32 billion, this figure decreased to N69.48 billion in the year under review.\n\nHowever, in the second quarter of the 2023, there was a hike in vehicle exports from N96.76 billion recorded in 2022 to N733.91 billion.\n\nIn the third quarter of 2023, this figure dropped to N138.50 billion while that of the corresponding quarter of 2022 stood at N90.77 billion.\n\nFor the last three months of 2023, Nigeria imported used vehicles valued at N121.82 billion. This was almost double the value of vehicle import recorded in the same period of 2022 at N65.19 billion.\n\n#### What you should know\n\nThe automotive sector in Nigeria has seen notable development recently, yet it remains behind those in developed countries. The U.S. International Trade Administration reports that Nigeria requires 720,000 vehicles annually, but domestic production is limited to 14,000 units, necessitating the import of the remaining vehicles to meet demand.\n\n- Nigeria has seen increased local production of automobiles in the past few years with Innoson and Nord motors but increased cost of production stifles local demand with patronage mostly from governments and institutions.\n- For most Nigerians, used car imports from the United States presents a more affordable alternative. However, elevated import duties and depreciation of the naira have kept prices almost above the reach of the average Nigerian. Nigeria mostly imports used vehicles from the United States, Qatar and Europe.\n- The Director-General of the Nigerian Automotive Council recently stated that the federal government is planning a ban on the importation of used vehicles of year 2000 to 2007 model cars into the country.\n\n---\n\nAdd Nairametrics on Google News\n\nFollow us for Breaking News and Market Intelligence.\n\nTags: NBS Vehicle importation\n\n### Aghogho Udi\n\nMy name is Aghogho Udi, a writer, journalist, and researcher, deeply intrigued by the political economy of Nigeria and the broader African context. My focus lies in shedding light on the intricate connections between macroeconomics and politics, offering valuable insights that foster comprehension of Africa's prevailing economic landscape and the world in general.\n\nNext Post\n\n### Food Security: FG signs €995 million deal, to create Agric mechanization hubs in 774 LGAs\n\n### Leave a Reply Cancel reply\n\nYour email address will not be published. Required fields are marked *\n\nComment *\n\nName *\n\nEmail *\n\nWebsite\n\nSave my name, email, and website in this browser for the next time I comment.\n\nΔ\n\nFollow us on social media:\n\n### Welcome Back!\n\nLogin to your account below\n\nRemember Me\n\nForgotten Password? Sign Up\n\n### Create New Account!\n\nFill the forms below to register\n\nAll fields are required. Log In\n\n### Retrieve your password\n\nPlease enter your username or email address to reset your password.\n\nLog In\n\nSocial Media Auto Publish Powered By : XYZScripts.com\n\nNo Result\n\nView All Result\n\n- Access Holdings Offer\n- Fidelity Bank Offer\n- GTCO Offer\n- Zenith Bank Offer\n\n- Company Results\n- Dividends\n- Public Offer & Right Issues\n- Stock Market News\n\n- Career tips\n- Personal Finance\n\n- Login\n- Sign Up"
  },
  {
    "url": "https://africon.de/en/slide-of-the-month-sotm-july-the-age-of-vehicles-in-operation-in-nigeria/",
    "text": "Slide of the month (SOTM) July. The age of vehicles in operation in Nigeria - africon\n\nSearch\n\nSearch\n\n- africon, Automotive & Mobility, Slide of the month\n\n# Slide of the month (SOTM) July. The age of vehicles in operation in Nigeria\n\nWhile Africa’s automotive market contains great opportunities, numerous international automotive firms face challenges when doing business on the continent. A key reason is the very significant differences in the business environments in Africa, vis-à-vis those in other global markets.\n\nafricon recently completed a project on the Nigeria automotive industry, commissioned by a multilateral organization. A critical question that emerged was the age of vehicles in Nigeria. As many large component producers do not provide coverage for cars beyond a certain age, this metric is a crucial market determinant for many international companies. With an average age of 16 years, a large portion of the Nigerian vehicle fleet is “too old.” This highlights the need for holistic data on African markets to sufficiently understand market potentials.\n\nAfter putting these market forces into consideration, africon provided the client with a comprehensive overview of the market and a list of potential opportunities locally.\n\nIf you have any questions or concerns, you can contact us at info@africon.de\n\n### Read previous SOTMs (Slides of the month) here.\n\nSlide of the month (SOTM) June. Labtech imports in East Africa\n\nSlide of the month (SOTM) May: The logistics sector in Rwanda and Ethiopia\n\nSlide of the month (SOTM) April. Overview of the tech scene in select countries in Africa\n\nSlide of the month (SOTM) March. Operational and upcoming railway lines in Nigeria\n\nSlide of the month (SOTM) February. Import statistics of medical equipment in East African Community (EAC)\n\n- News\n\n### How automotive spare parts actually reach end customers in Africa\n\nJune 11, 2026 10:38 am\n\n### africon joins the VDMA Beratungsnetzwerk: a vetted route to Africa for Machinery and Equipment Manufacturers\n\nJune 8, 2026 8:24 am\n\n### Watch: Africa’s Raw Materials and Infrastructure Panel from the 9th German-African Economic Forum\n\nJune 5, 2026 11:05 am\n\n### Slide of the month (SOTM) June: How much potential does Morocco’s modular container market hold?\n\nJune 2, 2026 3:43 am\n\n### Which African automotive markets are worth a serious look for European suppliers?\n\nMay 27, 2026 4:44 pm\n\n### Cosmetic or medicine? The question that decides your South African launch\n\nMay 22, 2026 7:12 pm\n\n### Why local presence makes the difference: Inside africon’s East Africa team meeting in Dar es Salaam\n\nMay 12, 2026 4:14 pm\n\n### Slide of the month (SOTM) May: Italy leads the supply of packaging machinery to Africa\n\nMay 5, 2026 4:01 pm\n\n### africon welcomes Kholoud Saeed as an Analyst in Egypt\n\nApril 29, 2026 1:48 pm\n\n### africon welcomes Taha Laouni as a Senior Analyst in Morocco\n\nApril 16, 2026 7:37 pm\n\n- Latest Post\n\n- June 11, 2026\n- Lukas Bauer\n\nHow automotive spare parts actually reach end customers in Africa\n\nOne of the most common requests European parts manufacturers bring to africon is a clean [...]\n\n### Read more slides of the month here:\n\n- June 8, 2026\n- Lukas Bauer\n\nafricon joins the VDMA Beratungsnetzwerk: a vetted route to Africa for Machinery and Equipment Manufacturers\n\nafricon has been admitted to the VDMA Beratungsnetzwerk, the consulting network of Germany’s mechanical and [...]\n\n- June 5, 2026\n- Lukas Bauer\n\nWatch: Africa’s Raw Materials and Infrastructure Panel from the 9th German-African Economic Forum\n\nAfrica is no longer a future market, it is a present-day one. That was the [...]\n\n- africon, Building Materials & Construction, Slide of the month\n\n- June 2, 2026\n- Lukas Bauer\n\nSlide of the month (SOTM) June: How much potential does Morocco’s modular container market hold?\n\nThe modular container market in Morocco is a niche but steadily growing segment within the [...]\n\ntop\n\nZustimmung verwalten\n\nWHAT WE'RE THINKING\n\nHow automotive spare parts actually reach end customers in Africa\n\nafricon joins the VDMA Beratungsnetzwerk: a vetted route to Africa for Machinery and Equipment Manufacturers\n\n###### Insights\n\nValuable insights that empower your decision-making,\n\n###### Case Studies\n\nInspiring examples of financial tailored solutions.\n\n###### Media Mentions\n\nRecognizing our expertise and client success.\n\n##### Stay ahead in a rapidly changing world\n\nOur monthly insights for strategic business perspectives.\n\nSubscribe\n\nFINANCIAL\n\n###### Investment planning\n\nTailored investment strategies to help clients grow their wealth.\n\n###### Retirement planning\n\nComprehensive plans designed to secure a comfortable future.\n\n###### Education planning\n\nGuidance on saving and investing for educational expenses.\n\n###### Portfolio management\n\nActive management to optimize returns while managing risk.\n\n###### Asset allocation\n\nMaximize growth potential via asset diversification.\n\n###### Risk management\n\nManaging financial risks with insurance and other measures.\n\nTAX\n\n###### Tax planning\n\nOptimize tax through services like deductions and strategies.\n\n###### Estate planning\n\nEffective estate planning for taxes and wealth transfer.\n\n###### Wealth preservation\n\nPreserve wealth for future while reducing taxes.\n\nFEATURED\n\nAdapting to the digital era\n\nSearch\n\nSearch"
  },
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    "url": "https://theicct.org/wp-content/uploads/2021/06/ICCT_Sootfree_transport_20190826.pdf",
    "text": "COSTS AND BENEFITS OF SOOT-FREE \nROAD TRANSPORT IN NIGERIA\nJoshua Miller\nWHITE PAPER AUGUST 2019\nBEIJING | BERLIN | BRUSSELS | SAN FRANCISCO | WASHINGTON\nwww.theicct.org\ncommunications@theicct.org \nACKNOWLEDGMENTS\nThis study was sponsored by the Climate and Clean Air Coalition to Reduce Short-Lived \nClimate Pollutants (CCAC) and its Initiative on Reducing Emissions from Heavy-Duty \nVehicles and Fuels.\nABOUT THE CCAC\nThe CCAC is a voluntary global partnership of governments, intergovernmental \norganizations, businesses, scientific institutions, and civil society organizations \ncommitted to catalyzing concrete, substantial action to reduce short-lived climate \npollutants, including methane, black carbon, and many hydrofluorocarbons. The \nCoalition works through collaborative initiatives to raise awareness, mobilize resources, \nand lead transformative actions in key emitting sectors.\nABOUT THE CCAC HEAVY-DUTY VEHICLES INITIATIVE\nThe Coalition’s Heavy-Duty Vehicles and Fuels Initiative works to virtually eliminate \nfine particle and black carbon emissions from new and existing heavy-duty vehicles \nand engines. The Initiative supports its international partners to implement a sustained \ntechnology modernization pathway toward soot-free and low-carbon solutions. \nWe define “soot-free” technologies as those capable of meeting Euro 6/VI-equivalent \nstandards and reducing exhaust emissions of black carbon up to 99% compared with \nuncontrolled levels. For resources on soot-free transport, please visit https://theicct.org/\nsoot-free-transport-resources.\nInternational Council on Clean Transportation \n1500 K Street NW, Suite 650\nWashington, DC 20005 USA\ncommunications@theicct.org | www.theicct.org | @TheICCT\n© 2019 International Council on Clean Transportation\nii\nCOSTS AND BENEFITS OF SOOT-FREE ROAD TRANSPORT IN NIGERIA\nTABLE OF CONTENTS\n1. INTRODUCTION .....................................................................................................................1\n2. DATA AND OUTLOOK FOR NIGERIA ................................................................................3\n2.1 Population, urbanization, and motorization........................................................................3\n2.2 Air pollution, health, and climate impacts of vehicle exhaust......................................3\n2.3 Fuel market characteristics........................................................................................................3\n2.3.1 Domestic fuel production and imports...................................................................3\n2.3.2 Fuel quality of gasoline and diesel............................................................................4\n2.3.3 Road transport energy demand.................................................................................5\n2.4 Vehicle market characteristics..................................................................................................5\n2.4.1 Domestic vehicle production.......................................................................................5\n2.4.2 New and second-hand vehicle sales ........................................................................5\n2.4.3 In-use vehicle stock .........................................................................................................6\n2.5 Policy and regulatory environment.........................................................................................6\n3. POLICY OPTIONS ................................................................................................................8\n3.1 Scenario definitions.......................................................................................................................8\n4. COSTS AND BENEFITS ..................................................................................................... 10\n4.1 Energy consumption and vehicle emissions .....................................................................10\n4.2 Vehicle technology and operating costs ..............................................................................11\n4.3 Value of health and climate benefits.....................................................................................13\n4.4 Comparison of costs and benefits .........................................................................................15\n5. CONCLUSIONS AND RECOMMENDATIONS................................................................... 16\nReferences.................................................................................................................................18\n1\nICCT WHITE PAPER\n1. INTRODUCTION\nNigeria is the seventh most populated country in the world and its population is \nprojected to double over the next 30 years. It is the largest vehicle market of the \nEconomic Community of West African States (ECOWAS) and accounts for roughly two\u0002thirds of the region’s vehicle fleet.1\n Nigeria also has a high and growing health burden \nfrom air pollution. Ambient particulate matter imposes a societal cost equivalent to more \nthan 3% of Nigeria’s gross domestic product (OECD.Stat, 2018). Road transport accounts \nfor three quarters of transportation-related pollution, and from 2010 to 2015, the health \nburden associated with road transport emissions in Nigeria grew by 25% (Anenberg, \nMiller, Henze, Minjares, & Achakulwisut, 2019).\nRoad transport fuels in Nigeria are permitted to contain approximately 100 times the sulfur \nlevels permitted in Europe, and poor fuel quality has prevented the implementation of \nmandatory vehicle emission standards. Approximately 90% of vehicles entering Nigeria are \nsecond-hand vehicles. Imported passenger cars are permitted to be up to 15 years old, and \nno age limits are applied to imported commercial vehicles. \nOver the past several years, ECOWAS member states have been working to update \nand harmonize fuel quality and vehicle emission regulations across the region. On \nDecember 1, 2016, Ministers of ECOWAS member states met in Abuja, Nigeria and \nresolved to import only low-sulfur—50 parts per million (ppm) sulfur—fuels starting in \nmid-2017 (UN Environment Programme, 2016). Additionally, the eight refineries in the \nECOWAS region, including Nigerian refineries, were expected to upgrade their facilities \nto produce low-sulfur fuels by 2020. Nigeria, however, failed to meet its July 2017 target \nto start importing only low-sulfur fuels. As of August 2018, fuel specifications still varied \nsignificantly among ECOWAS member states, ranging from a maximum of 50 ppm sulfur \nin Ghana to 1,000 ppm or greater in Nigeria (Minjares, Miller, & Nare, 2018).\nIn late June 2018, representatives of 12 ECOWAS member states met in Abidjan, \nCote d’Ivoire to continue working toward regionally harmonized specifications for \nfuels and vehicles (UN Environment Programme, 2018b). To support this process, \nECOWAS commissioned a study for the development of a regional framework \nfor updated fuels and vehicles specifications. CITAC Africa Ltd. was selected to \nprovide recommendations for fuels specifications, and the ICCT was tasked with \nmaking recommendations for vehicles specifications. These recommendations were \npresented at a regional workshop in Abidjan in December 2018. The workshop \nincluded representatives of 14 ECOWAS member states, including representatives of \nthe ministries responsible for hydrocarbon fuels, environment, and transport. CITAC \nAfrica Ltd. and the ICCT presented recommendations including a 50 ppm sulfur limit \nfor fuel imports starting on January 1, 2020; requiring domestic refineries to meet 50 \nppm sulfur levels by January 1, 2024; and applying Euro 4/IV-equivalent standards for \nnew vehicles starting on January 1, 2020 (Meeting Report for Regional Workshop for \nValidation of the Provisional Final Report, 2018).\nConsidering the size of Nigeria’s population, economy, vehicle market, fuels market, and \nrefineries, its actions will have substantial influence on the ECOWAS region’s aggregate \nprogress toward updated and harmonized specifications for fuels and vehicles. This \nstudy aims to provide information to policymakers in Nigeria to support the transition \n1 ECOWAS is a regional economic union of 15 countries in West Africa: Benin, Burkina Faso, Cape Verde, \nCôte d’Ivoire, The Gambia, Ghana, Guinea, Guinea Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra \nLeone, and Togo.\n2\nCOSTS AND BENEFITS OF SOOT-FREE ROAD TRANSPORT IN NIGERIA\nto soot-free road transport and maximize the net societal benefits of this transition.2 It \nstarts with a review of relevant data and trends related to Nigeria and then evaluates the \ncosts and benefits of two different policy scenarios. Finally, it provides recommendations \nfor a path forward, including considerations for implementation.\n2 We define “soot-free” technologies as those capable of meeting Euro 6/VI-equivalent standards and \nreducing exhaust emissions of black carbon up to 99% compared with uncontrolled levels. For resources on \nsoot-free transport, please visit https://theicct.org/soot-free-transport-resources.\n3\nICCT WHITE PAPER\n2. DATA AND OUTLOOK FOR NIGERIA\n2.1 POPULATION, URBANIZATION, AND MOTORIZATION\nMore than half of Nigeria’s people currently live in urban areas, and the urban population \nis growing at an estimated rate of 4.23% for 2015–2020, faster than the country’s total \npopulation growth of 2.6% in 2018 (The World Bank, 2019). Motorization rates in Nigeria \nare still relatively low at less than 60 vehicles per 1,000 population; this is approximately \none-third the global average of 180 vehicles per 1,000 population (International \nOrganization of Motor Vehicle Manufacturers, 2015; The World Bank, 2019). Future \nincreases in motorization are expected to result from growing demand for freight \ntransport, growing population, and rising per-capita incomes. Although not directly \nconsidered in this analysis, these factors are expected to drive the projected growth in \nroad transport energy demand (see Section 2.3.3).\n2.2 AIR POLLUTION, HEALTH, AND CLIMATE IMPACTS OF \nVEHICLE EXHAUST\nVehicle exhaust contributes to elevated levels of ambient fine particles (PM2.5) and \nground-level ozone, among other pollutants. These have distinct and harmful effects \non the health of exposed populations (The Institute for Health Metrics and Evaluation & \nHealth Effects Institute, 2019). In 2015, three-quarters of premature deaths attributable \nto PM2.5 and ozone from transportation sources in the ECOWAS region were in Nigeria \n(Anenberg et al., 2019). Transportation-attributable health impacts in Nigeria in 2015 \nhave been conservatively estimated at 1,500 premature deaths, 81,000 years of life lost \nper year, and $710 million (2015 U.S.$) in welfare costs, and these estimates represent a \n25% increase in premature deaths from 2010 levels.3 Among transportation sources in \n2015, on-road diesel vehicles were the leading contributor to health impacts in Nigeria \n(38%), followed by on-road non-diesel vehicles (37%), international shipping (19%), \nand non-road mobile sources (7%). The Organisation for Economic Co-operation and \nDevelopment (2016) estimates the societal cost of ambient air pollution from all sources \nin Nigeria totaled $42 billion (2005 U.S.$) in 2015. Other estimates of the annual cost of \nair pollution in Nigeria range from $24 billion to $87 billion (2015 U.S.$).4\nTransportation sources also emit black carbon (BC), a component of PM2.5 exhaust \nthat is not only harmful to health, but also a potent short-lived climate pollutant. \nSimultaneously reducing short-lived climate pollutants and long-lived greenhouse gases \nis critical to achieving global climate goals (Shindell et al., 2017). Older technology diesel \nengines emit more than 90% of transportation-related BC globally (Minjares, Wagner, & \nAkbar, 2014), including engines in on-road vehicles, non-road equipment, and ships. The \nClimate and Clean Air Coalition’s Scientific Advisory Panel has set a target to reduce BC \nfrom all sources to 75% below 2010 levels by 2030 (Shindell et al., 2017). A global study \nconducted by the ICCT in 2018 found that this target is achievable for on-road diesel \nvehicles, provided all countries adopt filter-forcing Euro 6/VI-equivalent standards and \napply them to new and second-hand vehicle sales no later than 2025 (Miller & Jin, 2018).\n2.3 FUEL MARKET CHARACTERISTICS\n2.3.1 Domestic fuel production and imports\nNigeria is a major producer and exporter of crude oil. The country produces \napproximately 2 million barrels of oil (bbl) per day (Central Intelligence Agency, n.d.) \n3 The 95% confidence intervals reflecting uncertainty in the concentration-response functions are 500 to \n3,100 premature deaths and 29,000 to 165,000 years of life lost. See Anenberg et al. (2019)\n4 Based on the estimated number of premature deaths in Lelieveld et al. (2019) and the estimated value of a \nstatistical life in Anenberg et al. (2019).\n4\nCOSTS AND BENEFITS OF SOOT-FREE ROAD TRANSPORT IN NIGERIA\nand has three crude oil refineries, all of which are state-owned by the Nigerian National \nPetroleum Corporation (NNPC; CITAC Africa Ltd., 2019). Although its refineries have a \ntotal nameplate capacity of 445,000 bbl/day, disruptions to production have resulted \nin refineries processing only a fraction of this capacity. Nigeria’s refined petroleum \nproduction totaled approximately 35,000 bbl/day in 2017, and this would have met \na small fraction of its domestic consumption, which was 325,000 bbl/day in 2016 \n(Central Intelligence Agency, n.d.). While the NNPC aims to increase refined petroleum \nproduction to 60% of capacity by 2020, some analysts forecast that production will be \ncloser to about 48% of capacity by 2020 (CITAC Africa Ltd., 2019). To close the current \ngap between production and consumption, Nigeria imports roughly $6 billion to $8 \nbillion worth of refined petroleum products each year (Figure 1). Approximately 80% of \nNigeria’s refined petroleum product imports are from Europe, and the remainder come \nfrom the United States, Asia, and other regions (Observatory of Economic Complexity \n[OEC], 2016). A new refinery is currently under construction in Nigeria and is expected \nto open sometime between 2020 and 2022 (De Beaupuy & Wallace, 2019). The refinery \nwill have a much larger capacity than the existing refineries, 650,000 bbl/day, and will \nexport about 35% of its product and deliver 65% to the local market.\n0\n1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n2013 2014 2015 2016 2017\nValue of imports (billion US$)\nOthers\nAsia\nAfrica\nUnited States\nEurope\nFigure 1. Value of imports of refined petroleum products to Nigeria by exporting region from \n2013–2017, in billions of U.S. dollars (OEC, 2016).\n2.3.2 Fuel quality of gasoline and diesel\nSince 2006, the maximum fuel sulfur content in Nigeria has been limited to 1,000 ppm \nfor gasoline and 3,000 ppm for diesel (George, 2018). In 2017, Nigeria issued national \ncleaner fuels specifications that would have limited gasoline sulfur to 150 ppm and diesel \nsulfur to 50 ppm (Africa Network for Environment & Economic Justice Nigeria, 2017); \nhowever, these standards were suspended in 2017 before taking effect (UN Environment \nProgramme, 2018a). In December 2018, representatives of 14 ECOWAS member states, \nincluding Nigeria, met to review recommendations to implement 50 ppm sulfur limits for \ngasoline and diesel imports by January 1, 2020 and require domestic refineries to meet \n50 ppm sulfur levels by January 1, 2024.\n5\nICCT WHITE PAPER\n2.3.3 Road transport energy demand\nA direct consumer subsidy system for gasoline was in place until it was replaced in 2016 \nwith a price cap (CITAC Africa Ltd., 2019). Today, the NNPC sells imported gasoline at \na loss relative to the international market price, a form of indirect subsidy. As a result, \nNigeria’s vehicle market is dominated by demand for gasoline, which accounts for \nfour-fifths of combined gasoline and diesel demand. From 2020 to 2040, Nigeria’s road \ntransport energy demand is projected to grow at an annual rate of 3.8% for gasoline and \n2.4% for diesel.\n2.4 VEHICLE MARKET CHARACTERISTICS\n2.4.1 Domestic vehicle production\nIn the 1970s and 1980s, Nigeria’s federal government partnered with international \nautomakers to establish six local vehicle plants with a combined production capacity of \n149,000 vehicles per year (Deloitte, 2018). In the decades that followed, deteriorating \neconomic and policy conditions led to a decline in local vehicle production. Estimates \nof domestic vehicle production in 2015 range from 1,000 passenger vehicles to 4,000 \npassenger and commercial vehicles combined; the lower estimate is equivalent to about \n10%–15% of new vehicle sales (Deloitte, 2018).\n2.4.2 New and second-hand vehicle sales\nFrom 2012 to 2014, approximately 50,000 new vehicles were sold in Nigeria annually. \nAfter an economic slowdown and the introduction of substantial duties on vehicle \nimports, new vehicle sales fell to less than 10,000 vehicles annually in 2017 (Figure 2). \nLow per-capita incomes, high interest rates, and high vehicle depreciation contribute \nto the demand for inexpensive second-hand vehicles (Deloitte, 2018). Second-hand \nvehicles are estimated to account for approximately 90% of vehicles entering Nigeria. \nThe major originating countries of second-hand vehicles in the Nigerian market are \nestimated to be the United States, Japan, Germany, and Belgium. Legal imports of \nsecond-hand vehicles are restricted to shipping ports, including Premiere Port (Lagos), \nthe Tin Can Island Port (Lagos), and the Onne Port in Rivers State (Minjares et al., 2018). \nThe fixed number of legal ports of entry and that most second-hand vehicles originate \nfrom countries with well-established emission-control regimes implies that targeted \nefforts to verify and improve the emissions performance of second-hand vehicles are \nfeasible. Nigeria’s Ministry of Transport already prohibits the import of second-hand \npassenger vehicles older than 15 years; however, the current lack of a comprehensive, \nnationwide vehicle registration system and smuggling and corruption activities pose \na challenge to enforcement efforts. Estimates of total vehicle sales, including all new \nand second-hand vehicles, range from approximately 500,000 to 1,000,000 vehicles \nannually (Deloitte, 2018).\n6\nCOSTS AND BENEFITS OF SOOT-FREE ROAD TRANSPORT IN NIGERIA\n0\n10,000\n20,000\n30,000\n40,000\n50,000\n60,000\n2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017\nNew vehicle sales\nPassenger cars\nCommercial vehicles\nFigure 2. New vehicle sales in Nigeria from 2005–2017 (International Organization of Motor Vehicle \nManufacturers, n.d.).\n2.4.3 In-use vehicle stock\nEstimates of Nigeria’s in-use vehicle stock vary widely, from 1.6 million (BMI, 2015) to \n11.76 million vehicles (Nigeria National Bureau of Statistics, 2018). Comparison of top\u0002down fuel consumption data (International Energy Agency, 2017) with bottom-up fleet \nmodel estimates (Miller & Jin, 2018) produces an estimate of around 10.5 million vehicles \noperating on Nigeria’s roads in 2019. Consistent with projected increases in gasoline \nand diesel demand, from 2019 to 2030, Nigeria’s vehicle stock is projected to grow at \nan average rate of 3%–4% per year (Minjares et al., 2018). Other estimates of short-term \nstock growth rates are slightly higher, at 4.5%–5.5% per year (Deloitte, 2018).\n2.5 POLICY AND REGULATORY ENVIRONMENT\nAuthority to regulate vehicle emissions, fuel quality, and used vehicle imports is divided \namong several different agencies in Nigeria. The National Environmental Standards \nand Regulations Enforcements Agency is the regulatory body responsible for vehicle \nemissions control. Mandatory, nationwide fuel-quality specifications are set by the \nDepartment of Petroleum Resources, and the Standards Organization of Nigeria \nis tasked with setting requirements for imported goods. The Ministry of Transport \nhas authority to set age limits for second-hand vehicle imports. This distribution of \nregulatory authority across multiple agencies highlights the importance of inter-agency \ncollaboration for planning, implementation, monitoring, and enforcement of current and \nfuture regulations. Recommendations for policy development and implementation in \nNigeria, and in the broader ECOWAS region, are provided in Boxes 1 and 2.\n7\nICCT WHITE PAPER\nBOX 1. REGULATORY PATHWAYS FOR WEST AFRICA\nBased on experiences with new vehicle regulations and used import restrictions \nin major markets, ECOWAS countries are advised to consider the following: \nNEW VEHICLE REGULATIONS\nAll major vehicle markets apply mandatory new-vehicle emission standards as \na core component of their motor vehicle emission control programs. In order \nto sell a new vehicle—defined as an individual vehicle, vehicle model, or engine \nfamily—manufacturers must first obtain certification that the vehicle meets all \napplicable requirements, e.g., maximum emissions limits, on-board diagnostics \nsystems, durability, etc. In all major markets, the same emission standards apply \nregardless of whether the new vehicle is produced domestically or imported. \nAdditionally, in the United States and Canada, any second-hand vehicle imports \nmust be certified to the same standards that were in place domestically at the \ntime of their manufacture.\nUSED IMPORT RESTRICTIONS\nThe vast majority of vehicles entering the ECOWAS region are second-hand \nvehicles. ECOWAS countries have the opportunity to discriminate from a vast \nglobal supply chain to allow only those vehicles with the cleanest emission \ncontrol systems. The aim of vehicle emission standards in the region should be \nto deliver the maximum achievable reduction in emissions in the most cost\u0002effective manner, limited only by fuel quality.\n8\nCOSTS AND BENEFITS OF SOOT-FREE ROAD TRANSPORT IN NIGERIA\n3. POLICY OPTIONS \nFuels and vehicles operate as a single system. Fuels specifications apply to numerous \nfuel properties, and among the most important for vehicle emissions performance \nare the content of sulfur, lead, manganese, and other metal additives. Modern vehicle \nemissions aftertreatment technologies such as diesel particulate filters (DPF) and \ngasoline particulate filters are designed to operate with low-sulfur fuel (< 50 ppm sulfur). \nSelective catalytic reduction systems (SCR), which are used for control of nitrogen \noxides (NOx), are designed to operate with ultralow-sulfur fuel (< 10-15 ppm sulfur). \nIn gasoline, the presence of metallic additives such as tetraethyl lead and manganese \nimpedes the function of catalytic converters and undermines the effectiveness of \ninvestments in desulfurization needed for these emission control technologies to \nfunction properly (Minjares et al., 2018).\nCountries usually introduce cleaner fuels either in conjunction with, or a few months \nbefore, cleaner vehicle standards. Table 1 shows the progression of fuel sulfur limits \nand corresponding vehicle emission standards in the European Union. Fuels limited \nto 50 ppm sulfur for gasoline and diesel permit the introduction of cleaner engine \ntechnologies with Euro 4/IV emission performance. Fuels limited to 10 ppm sulfur for \ngasoline and diesel permit the introduction of Euro 6 for light-duty vehicles, Euro VI \nfor heavy-duty vehicles, and Euro 5 for two- and three-wheelers. The time between \nimplementation of each standard was historically defined in Europe by the limitations \nof technology availability. Now that Euro 6/VI technologies are widely commercialized, \ncountries like Nigeria can leapfrog to the most advanced vehicles standards, as long as \nfuel-quality standards are appropriately aligned. \nTable 1. Timeline of fuel sulfur limits and equivalent vehicle emission standards in the European Union \nVehicle \nStandard\nFuel \nStandard\nFuel Standard\nEU Directive\nFuel Standard \nImplementation Date\nFuel Sulfur Limit \n(ppm)\nEuro 1/I n/a — October 1994 2,000\nEuro 2/II Euro 2 93/12/EEC October 1996 500 (diesel)\nEuro 3/III Euro 3 93/12/EEC January 2000 350 (diesel);\n150 (gasoline)\nEuro 4/IV Euro 4 98/70/EC January 2005 50*\nEuro 5/V Euro 5 2003/17/EC January 2009 10\nEuro 6/VI Same sulfur limit as Euro 5 fuel standard\n* 10 ppm fuel must be available\nSource: TransportPolicy.net\n3.1 SCENARIO DEFINITIONS\nWe evaluated the costs and benefits of updating fuels and vehicles standards in \nNigeria for two different scenarios and compared them to a baseline without changes \nto current standards.\n» Baseline: Counterfactual scenario that assumes continuation of 3,000 ppm sulfur \ndiesel and 1,000 ppm sulfur gasoline, a 15-year age limit for second-hand passenger \ncar imports, and no mandatory vehicle emission standards.5\n» Euro 4/IV: Reflects recommendations put forward by CITAC Africa Ltd. and the \nICCT to ECOWAS member state representatives in December 2018. This scenario \nincludes 50 ppm sulfur limits for fuel imports by 2020 and for domestic refineries \n5 Euro 3 standards for light-duty vehicles have been adopted but not implemented due to high fuel sulfur levels.\n9\nICCT WHITE PAPER\nby 2024, and Euro 4/IV standards for all new and second-hand vehicle sales in \n2020. Assumes unleaded gasoline and a limit of 2 milligram (mg)/liter for metallic \nadditives including manganese.\n» Euro 6/VI: Assumes that after implementing 50 ppm fuels and Euro 4/IV, Nigeria \nimplements a limit of 10 ppm sulfur in gasoline and diesel by 2025; this is in \ncombination with Euro 6/VI standards for all new and second-hand light-duty and \nheavy-duty vehicles, and Euro 5 standards for two- and three-wheelers. Assumes \nunleaded gasoline and a limit of 2 mg/liter for metallic additives.\n10\nCOSTS AND BENEFITS OF SOOT-FREE ROAD TRANSPORT IN NIGERIA\n4. COSTS AND BENEFITS \n4.1 ENERGY CONSUMPTION AND VEHICLE EMISSIONS\nRoad transport energy consumption in Nigeria is projected to increase by a factor of \n2.3 for diesel and 3.2 for gasoline from 2020 to 2050 (Figure 3), and this is the context \nfor all three scenarios. In the Baseline scenario, absent improvements to fuel quality and \nvehicle emission controls, vehicle exhaust emissions of PM2.5, BC, NOx, sulfur dioxide \n(SO2), and other pollutants are, unsurprisingly, projected to increase to a similar extent. \nIn the Euro 4/IV scenario, the introduction of 50 ppm sulfur limits for imports and \ndomestic production would reduce SO2 emissions by greater than 95%, corresponding to \nthe reduction in fuel sulfur content. Additionally, the introduction of Euro 4/IV emission \nstandards for new and used vehicles in 2020 would reduce emissions of PM2.5, BC, and \nNOx by 80%–90% compared with the Baseline scenario in 2050, with the exception of \nNOx from diesel vehicles, which would be reduced by approximately 50%. \nIn the Euro 6/VI scenario, further reduction of fuel sulfur content to a maximum of 10 \nppm would nearly eliminate SO2 emissions. Introduction of Euro 6/VI emission standards \nfor new and used vehicles and Euro 5 standards for two- and three-wheelers in 2025 \nwould further reduce PM2.5 and BC by approximately 80% compared with the Euro 4/IV \nscenario in 2050. The difference between the Euro 4/IV and Euro 6/VI scenarios is most \napparent for diesel NOx emissions: In the Euro 4/IV scenario, diesel NOx emissions are \ngreater in 2050 than in 2019, whereas in the Euro 6/VI scenario, diesel NOx emissions are \none quarter of 2019 levels in 2050.\nDiesel Gasoline\n2020 2030 2040 2050\nYear\n2020 2030 2040 2050\nYear\nEnergy\nPM2.5\nBC\nNOX\nSO2\n0\n1,000\n2,000\n0\n10\n20\n30\n0\n10\n20\n0\n200\n400\n600\n800\n0\n50\n153\n345\n623\n2,008\n14\n1\n5\n31\n4\n2\n9\n29\n9\n0\n4\n20\n1\n4\n1 1\n169\n44\n188\n369\n84\n261\n805\n45\n1\n20\n0\n46\n4\n28\n1\n90\nScenario\nBaseline\nEuro 4/IV\nEuro 6/VI\nFigure 3. Road transport energy consumption and exhaust emissions by fuel type and scenario, \n2019–2050. Energy units are petajoules (1 petajoule [PJ] = 10^15 joules and 1 million tonnes of oil \nequivalent = 42 PJ). Emissions units are thousand metric tons per year. Data labels are shown for \ncalendar years 2019 and 2050. \n11\nICCT WHITE PAPER\n4.2 VEHICLE TECHNOLOGY AND OPERATING COSTS\nThe introduction of new vehicle emission standards compels new vehicle manufacturers \nto utilize commercially available emission control technologies to comply with the \nstandards. The incremental costs of these new vehicle technologies, such as SCR \nsystems for NOx control and DPFs for particulate matter control, are shown in Figure 4.\nThe implementation of cleaner fuels specifications would compel a combination of \ncleaner fuels imports and upgrades to Nigeria’s domestic refineries. The incremental \ncosts of importing or refining cleaner fuels are estimated to be 2.1 U.S. cents (¢) per liter \nfor 50 ppm gasoline, 1¢/liter for 50 ppm diesel (CITAC Africa Ltd., 2019), and 2.9¢/liter \nfor 10 ppm gasoline and diesel (MathPro, 2015).\nThe incremental costs of diesel exhaust fluid for Euro IV–VI diesel vehicles with SCR \nsystems and filter maintenance for Euro 6/VI diesel vehicles with DPFs were also \nevaluated. These costs are relatively small compared with the incremental costs of new \nvehicle technologies and cleaner fuels (Miller & Façanha, 2016).\n0\n1000\n2000\n3000\n4000\n5000\n6000\nDiesel Gasoline Diesel Gasoline Gasoline\nHDV HDV LDV LDV MC\nEuro 1 Euro 2 Euro 3 Euro 4 Euro 5 Euro 6 Euro 6d\nFigure 4. Estimated technology costs for new vehicles by emission standard. These costs are based \non three ICCT studies (Sanchez, Bandivadekar, & German, 2012; Posada, Chambliss, & Blumberg, \n2016; and Miller & Minjares, 2013) and have been adjusted to account for differences in average \nvehicle engine sizes and recent changes in the prices of platinum group metals.\nFigure 5 shows how the estimated costs of the Euro 4/IV and Euro 6/VI scenarios \ncompare with the Baseline scenario. Incremental technology costs are a combination \nof the estimated costs of new vehicle emission control technologies and projected new \nvehicle sales.6 Incremental operating costs include the costs of cleaner fuels, diesel \nexhaust fluid, and filter maintenance. The values in Figure 5 are not discounted, in order \nto isolate the effects of growth in new vehicle sales and fuel consumption on incremental \ntechnology and operating costs. For the comparison of cumulative costs and benefits \nover time in Section 4.4, below, costs are discounted to present value terms using a 7% \nsocial discount rate (Minjares et al., 2014; U.S. Environmental Protection Agency, 2010).7\nIn the Euro 4/IV scenario, incremental vehicle technology costs in Nigeria are estimated \nto be approximately $107 million in 2020. Undiscounted vehicle technology costs are \n6 Because second-hand imported vehicles are already heavily depreciated, the incremental costs to \nconsumers may be positive, negative, or negligible.\n7 A 7% real discount rate is chosen to approximate the social discount rate in developing economies. It is by \ndesign lower than market interest rates, which include inflation, risk premiums, and taxation effects.\n12\nCOSTS AND BENEFITS OF SOOT-FREE ROAD TRANSPORT IN NIGERIA\nprojected to grow in subsequent years in line with projected growth in new vehicle \nsales. Incremental operating costs, primarily to import cleaner fuels, are estimated to \nbe roughly $356 million in Nigeria in 2020, assuming fuel imports meet approximately \n90% of demand. The stepwise increase in 2024 reflects the estimated incremental costs \nincurred by domestic refineries to produce 50 ppm fuels (MathPro, 2015).8\nIn the Euro 6/VI scenario, incremental vehicle technology costs would be \napproximately one-third greater in 2025 than in the Euro 4/IV scenario. Estimated \nincremental operating costs likewise increase starting in 2025 with the introduction of \n10 ppm sulfur fuels.\n8 These costs reflect the amortized per-liter costs to produce cleaner fuels, including any capital charges for \nrefinery investments, operational costs to replace lost product yield, and any costs to replace lost gasoline \noctane.\n13\nICCT WHITE PAPER 2020 2022 2024 2026 2028 2030\n2032\n2034\n2036\n2038\n2040\n2042\n2044\n2046\n2048\n0\n500\n1000\n1500\n2000\nIncremental technology costs (million U.S.$)\n2020\n2022\n2024\n2026\n2028\n2030\n2032\n2034\n2036\n2038\n2040\n2042\n2044\n2046\n2048\n0\n500\n1000\n1500\n2000\nIncremental operating costs (million U.S.) \nScenario\nBaseline\nEuro 4/IV\nEuro 6/VI\nIncremental Technology Costs Incremental Operating Costs\nFigure 5. Incremental vehicle technology and operating costs compared with the Baseline scenario, \n2020–2050. Values are in 2019 U.S.$ and are not discounted (see section 4.4 for discounted results).\n4.3 VALUE OF HEALTH AND CLIMATE BENEFITS\nThe social costs of vehicle emissions and the benefits of emission reductions are \nevaluated using the economic valuation framework given in Shindell (2015). This \nframework considers pollutant-specific and time-dependent damages associated \nwith emissions, including direct climate and health impacts, climate-related health \ndamages, and the effects of ozone on reduced agricultural productivity. A limitation \nof this approach is that it does not account for conditions specific to Nigeria, such as \nthe location of emissions, their proximity to population, population age distribution, \nprojected population growth, urbanization, baseline disease rates, and meteorology. \nFurther research efforts could improve the characterization of societal costs by explicitly \naccounting for these factors. Nevertheless, when taken in context with the consistent \nconclusions of more-detailed studies conducted in other countries, the results of this \nanalysis can contribute to understanding the societal implications of transitioning to \ncleaner fuels and vehicles in Nigeria. \nThe global mean values for each pollutant from Shindell (2015) were adjusted to account \nfor lower per-capita incomes in Nigeria.9 The social costs of vehicle emissions are the \nproduct of the cost-per-tonne values for pollutants, estimated emissions, and discount \nfactors. For consistency with the calculation methods for these cost-per-tonne values \nin Shindell (2015), we apply a 5% social discount rate to convert these climate, health, \nand agricultural damages to present value terms. The values shown in Figure 6 are not \ndiscounted, so as to show the trends over time as opposed to their present discounted \nvalue. The lines show the central damage estimates, whereas the shaded areas \ncorrespond to uncertainty estimates.10\n9 Specifically, the cost-per-tonne values for the 5% discount rate scenario in Table S2 were converted from \n2007$ to 2019$ (factor = 1.24), adjusted to U.S.-specific values (factor = 1.15, using the mean value of the \n10%–20% range cited for U.S.-specific values), and then adjusted to Nigeria using the ratio of per-capita \nincomes in Nigeria and the United States (factor = 0.1). Year-specific values were interpolated using the \n2010, 2030, and 2050 estimates.\n10 5th and 95th percentile estimates for years after 2010 apply the proportional range of uncertainty in Table \nS4 in Shindell (2015).\n14\nCOSTS AND BENEFITS OF SOOT-FREE ROAD TRANSPORT IN NIGERIA\nAccording to this approach, the social costs of 2019 vehicle emissions in Nigeria are \nestimated to be approximately $5.6 billion ($2.5 billion–$9.3 billion).11 Compared with the \nBaseline scenario, the Euro 4/IV scenario would reduce the societal damages of 2050 \nemissions by approximately 78%. The Euro 6/VI scenario would reduce the damages \nof 2050 emissions by 93% compared with the Baseline scenario, and by two-thirds \ncompared with the Euro 4/IV scenario.\n11 These estimates are higher than the results of a 2019 study which applied Global Burden of Disease (GBD) \n2017 methods to evaluate direct health damages of vehicle emissions in 2010 and 2015. The estimates \nderived from Shindell (2015) are higher partly due to the inclusion of climate damages and climate-related \nhealth damages, the use of a global average population for evaluating health effects, and the use of \nGBD 2010 methods. GBD 2017 methods likely still undercount the health effects of ambient air pollution, \nparticularly for countries with high pollutant exposures such as Nigeria. For further discussion, see section 4 \nof Anenberg et al. (2019).\n15\nICCT WHITE PAPER\n20202023202620292032203520382041204420472050\n0\n5\n10\n15\n20\n25\nSocial cost of vehicle emissions (billion U.S.$)\n20202023202620292032203520382041204420472050\n0\n5\n10\n15\n20\n25\nSocial cost of vehicle emissions (billion U.S.$)\n20202023202620292032203520382041204420472050\n0\n5\n10\n15\n20\n25\nSocial cost of vehicle emissions (billion U.S.$)\nBaseline Euro 4/IV Euro 6/VI\nFigure 6. Social cost of vehicle emissions in Nigeria, 2020–2050. Lines indicate central estimates. \nShaded areas indicate 5th and 95th percentile values. Values are in 2019 U.S.$ and are not \ndiscounted (see the next section for discounted results).\n4.4 COMPARISON OF COSTS AND BENEFITS\nThe cumulative present discounted costs and benefits of the Euro 4/IV and Euro 6/\nVI scenarios from 2020 to 2050 are compared with the Baseline scenario in Table 2. \nFor each dollar invested in cleaner vehicles and fuels, the Euro 4/IV scenario would \nyield an estimated 10.1 U.S. dollars ($) ($4.4–$16.7) in societal benefits. Over the period \nfrom 2020 to 2050, the Euro 4/IV scenario would yield net societal benefits of $91.9 \nbillion. The Euro 6/VI scenario would yield even greater net societal benefits of $101.9 \nbillion, equivalent to $7.9 in benefits for each $1 invested. Compared with the Euro 4/IV \nscenario, the Euro 6/VI scenario would incur higher costs, $4.7 billion, but it would also \nyield higher benefits, $14.7 billion. Compared with the Euro 4/IV scenario, the marginal \nnet benefits of the Euro 6/VI scenario are $10 billion ($1.6 billion–$19.9 billion), equivalent \nto a marginal benefit-cost ratio of 3.1. The Euro 6/VI scenario is preferred because it \nproduces consistently higher net benefits than the Euro 4/IV scenario. This conclusion is \nconsistent for the central, 5th percentile, and 95th percentile estimates.\nTable 2. Present discounted value of costs and benefits from 2020 to 2050 compared with the \nBaseline scenario, in billion U.S.$. Parentheses indicate 5th and 95th percentile estimates.\nVariable Euro 4/IV Euro 6/VI\nIncremental Technology Costs 2.1 2.6\nIncremental Operating Costs 8.0 12.1\nTotal Incremental Costs 10.1 14.8\nSocietal Benefits 102 (44.9, 169.3) 116.7 (51.2, 193.9)\nNet Benefits 91.9 (34.8, 159.2) 101.9 (36.4, 179.1)\nBenefit-Cost Ratio 10.1 (4.4, 16.7) 7.9 (3.5, 13.1)\nNote: Incremental technology and operating costs are discounted using a rate of 7%; societal benefits are \ndiscounted using a rate of 5%. For details, see Section 4.2 and Section 4.3.\n16\nCOSTS AND BENEFITS OF SOOT-FREE ROAD TRANSPORT IN NIGERIA\n5. CONCLUSIONS AND RECOMMENDATIONS\nThis study evaluated the costs and benefits of two scenarios for transitioning to cleaner \nfuels and vehicles in Nigeria. We estimated that annual vehicle emissions in Nigeria \nimpose a substantial societal cost, $5.6 billion, roughly equivalent to the cost of Nigeria’s \nannual petroleum product imports, $6 billion–$8 billion. Absent the introduction of \ncleaner fuels and vehicles, vehicle emissions in Nigeria are likely to increase in line \nwith growing motorization and freight activity. From 2010 to 2015, the health impacts \nof vehicle emissions in Nigeria are estimated to have increased 25%, driven largely \nby population growth. Therefore, over the next several decades, the societal costs of \nvehicle emissions are likely to continue to increase as a result of population growth, \nurbanization, an aging population, and other factors.\nFortunately, Nigeria can take advantage of readily available technologies to cost\u0002effectively reduce vehicle emissions. This analysis demonstrates the importance and \ncost-effectiveness of requiring 50 ppm sulfur fuel imports by 2020 and domestic \nproduction of these fuels no later than 2024, in conjunction with Euro 4/IV standards \napplied to all new and second-hand vehicle sales. Since second-hand vehicles account \nfor approximately 90% of vehicles imported to Nigeria, these benefits can only be \nachieved by applying the same emissions performance standards to second-hand \nvehicles as well as new vehicles.\nThe analysis also demonstrates that Euro 4/IV and 50 ppm sulfur fuels should only \nbe an intermediate step on the path to Euro 6/VI standards and 10 ppm sulfur fuels. \nConsidering the importance of steep BC mitigation to meet global climate goals and the \nair quality benefits these controls will deliver (Shindell et al., 2017), we recommend that \nNigeria transition to Euro 6/VI vehicles and fuels specifications no later than 2025, in line \nwith the pathway identified to meet that target (Miller & Jin, 2018). We also recommend \nincluding these actions in Nigeria’s Nationally Determined Contribution (Minjares, \n2018), since they would yield climate benefits internationally in addition to local climate \nand health benefits. These actions would also contribute to meeting Sustainable \nDevelopment Goals 3.2, 3.9, 7.a, and 11.6.12\n12 These Sustainable Development Goals are: 3.2: Reducing infant mortality from exposure to ambient PM2.5; \n3.9: Reducing premature deaths from air pollution; 7.a: Expanding access to advanced and cleaner fossil-fuel \ntechnology; 11.6: Reducing adverse per capita impacts on air pollution in cities.\n17\nICCT WHITE PAPER\nBOX 2. IMPLEMENTATION STRATEGIES FOR CLEANER VEHICLES \nAND FUELS\nAfter enacting vehicles and fuels policies, Nigeria and other ECOWAS member states \nshould consider adopting supporting procedures:\nEMISSION CONTROLS ON NEWLY REGISTERED VEHICLES\n» A type-approval certificate provided by the vehicle importer and drawn from the \noriginal vehicle manufacturer, or produced directly by the vehicle manufacturer, \nwould demonstrate compliance with emission standards at the time of production.\n» A vehicle without evidence of type approval should undergo an emissions test \nprocedure at the expense of the vehicle importer to demonstrate emissions \nperformance equivalent to the established emission requirements.\n» A screening procedure should apply to all previously used vehicles consisting of \nan on-board diagnostics check and visual inspection to ensure no malfunction of \nthe vehicle.\n» Information (such as that contained in the vehicle identification number) indicating \nwhere the vehicle was built, the manufacturer, the vehicle brand, engine, and size, \nand the model year of the vehicle should be provided by the vehicle importer or \nvehicle manufacturer.\nREGIONAL COMPLIANCE AND ENFORCEMENT\n» Implement a common set of vehicle registration and reporting guidelines to be \ndefined by ECOWAS for tracking vehicles entering the ECOWAS region.\n» Guarantee access to fuel that meets the proposed fuel specifications at the retail \npump.\n» Require any vehicle importers or producers in the region to demonstrate \ncompliance with emission standards, in accordance with licensing guidelines to be \nprepared by ECOWAS.\n» Establish, with the support of ECOWAS, certain enforcement procedures whereby \nvehicle importers are subject to penalties and revocation of their license upon \nviolations of vehicle emission control regulations.\n» Establish, with the support of ECOWAS, a system of independent vehicle testing at \nports of entry to conduct random audits of vehicles for compliance with emission \nstandards.\nCOMPLEMENTARY ACTIONS TO ACCELERATE EMISSION \nREDUCTIONS\n» Establish remote sensing programs to screen for high emitters.\n» Create scrappage, inspection, and maintenance programs to either repair vehicles \nor de-register them, including payment of residual value to the vehicle owner, \nsubject to funding availability.\n18\nCOSTS AND BENEFITS OF SOOT-FREE ROAD TRANSPORT IN NIGERIA\nREFERENCES\nAfrica Network for Environment & Economic Justice Nigeria. 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Retrieved \nfrom https://www.unenvironment.org/news-and-stories/blogpost/ecowas-countries\u0002develop-harmonized-fuel-and-vehicle-emission-standards\nUN Environment Programme. (2016, December 1). West African countries adopt low \nsulphur diesel standards. Retrieved from https://www.unenvironment.org/news\u0002and-stories/blogpost/ecowas-countries-develop-harmonized-fuel-and-vehicle\u0002emission-standards\nU.S. Environmental Protection Agency. (2010). Guidelines for preparing economic \nanalyses: Discounting future benefits and costs. Retrieved from https://www.epa.gov/\nsites/production/files/2017-09/documents/ee-0568-06.pdf"
  },
  {
    "url": "https://www.nigerianstat.gov.ng/pdfuploads/Road_Transport_Data_-_Q2_2018.pdf",
    "text": "(Q2 2018)\nRoad Transport \nData\nData Source: National Bureau of Statistics / Federal Road Safety Corps (FRSC)\nReport Date: August 2018\nContents\nExecutive Summary\nAbia\nAdamawa\nAkwa ibom\nAnambra\nBauchi\nBayelsa\nBenue\nBorno\nCross river\nDelta\nEbonyi\nEdo\nEkiti\nEnugu\nGombe\nImo\nJigawa\nKaduna\nKano\nKatsina Kebbi Kogi Kwara\nLagos\nNassarawa\nNiger\nOgun\nOndo\nOsun\nOyo\nPlateau\nRivers\nSokoto Taraba Yobe\nZamfara\nFCT, Abuja\n3\nMethodology\nAppendix\nAcknowledgements/Contacts\n1\n83\n84\n91\nEstimated Vehicle Population in Nigeria\nAll States Data\nSex Distribution of Persons Injured In Road Traffic Crashes\nSex Distribution of Persons Killed In Road Traffic Crashes\nNumber of Vehicles Involved In Road Traffic Crashes\nCategory of Vehicle Involved In Road Traffic Crashes \n59\n11\n13\n15\n17\n19\n21\n23\n25\n27\n29\n31\n33\n35\n37\n39\n41\n43\n45\n47\n49\n51\n53\n55\n57\n59\n61\n63\n65\n67\n69\n71\n73\n757\n79\n80\n812\nTotal 77\n82\n1\nExecutive Summary\nThe Q2 2018 road transport data reflected that 2,608 road crashes occurred within the quarter under review. Speed \nviolation is reported as the major cause of road crashes in Q2 and it accounted for 50.65% of the total road crashes \nreported. Tyre burst and dangerous driving followed closely as they both accounted for 8.59% and 8.40% of the total \nroad crashes recorded.\nA total of 8,437 Nigerians got injured in the road traffic crashes recorded. 7,946 of the 8,437 Nigerians that got \ninjured, representing 94% of the figure, are adults while the remaining 491 Nigerians, representing 6% of the figure \nare children. 6,415 male Nigerians, representing 76%, got injured in road crashes in Q2 while 2,022 female Nigerians, \nrepresenting 24% got injured.\nSimilarly, a total of 1,331 Nigerians got killed in the road traffic crashes recorded in Q2 2018. 1,257 of the 1,331 \nNigerians that got killed, representing 94% of the figure, are adults while the remaining 74 Nigerians, representing 6% \nof the figure are children. 1,047 male Nigerians, representing 79%, got killed in road crashes in Q2 while 284 female \nNigerians, representing 21% got killed.\nEstimated vehicle population in Nigeria as at Q2 2018 was put at 11,760,871 with the total estimated population of \nthe country puts at 198,000,000 in 2018. Nigeria's vehicle per population ratio is put at 0.06. Data on the category of \nvehicles involved in road crashes in Q2 2018 reflected that 60.29% of vehicles are commercial (2,447), 38.63% are \nprivate (1,568), 1.08% are government (44) and the diplomat with zero (0) vehicle involved.\nA total of 221,878 national drivers licenses were produced in Q2 2018. Lagos and FCT produced the highest number \nof drivers' licenses while Zamfara and Taraba States produced the least numbers of national drivers' license.\nRoad Transport Data - Q2 2018\n2\nEstimated Vehicle Population in Nigeria\nEstimated Population \n198,000,000\nVehicle per Population\n0.06\nVehicle Population \n11,760,871\nCommercial\n57.70%\nPrivate\n40.98%\nGovernment\n1.27%\nDiplomatic\n0.05%\nRoad Transport Data - Q2 2018\n6,785,956 4,819,251 149,470 6,194\nRoad Transport Data - Q2 2018\n3\nABIA STATE\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n3 11 0 14\n64 12 76 171\nVEHICLE PLATE NUMBER PRODUCTION \nTotal Production Total Registration\n8,001 1,034\nNATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 4,428 TOTAL\nPRODUCTION\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n4\nABIA STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n3 0 2 1\nBrake\nFailure\n1\nOverloading\n0 0 1 2\nWorn Out Tyre\n0\n3 0 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 0\nSign Light \nViolation OTHERS Total\nRoad tr\n13\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n5\nABUJA FCT\nNATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 25,415 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n45 196 48 289\n634 66 700 1737\nVEHICLE PLATE NUMBER PRODUCTION \nTotal Production Total Registration\n123 776\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n6\nABUJA FCT CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n187 1 23 4\nBrake\nFailure\n9\nOverloading\n1 2 10 44\nWorn Out Tyre\n0\n24 10 0 2\nSleeping on \nSteering\n2\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n2 5 3\nSign Light \nViolation OTHERS Total\n329\nRoad traffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n7\nADAMAWA STATE\nNATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 1,327 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n6 20 0 26\n103 13 116 173\nVEHICLE PLATE NUMBER PRODUCTION \nTotal Production Total Registration\n4 394\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n8\nADAMAWA\nSTATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n18 0 1 0\nBrake\nFailure\n0\nOverloading\n1 0 1 0\nWorn Out Tyre\n0\n1 7 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 1 0\nSign Light \nViolation OTHERS Total\nRoad tr\n30\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n9\nAKWA IBOM STATE\nNATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 3,743\nTOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n9 9 3 21\n31 12 45 96\nVEHICLE PLATE NUMBER PRODUCTION \nTotal Production Total Registration\n0 592\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n10\nAKWA IBOM\nSTATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n13 0 1 0\nBrake\nFailure\n0\nOverloading\n0 0 0 0\nWorn Out Tyre\n0\n3 2 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 1 0\nSign Light \nViolation OTHERS Total\nRoad tr\n20\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n11\nANAMBRA STATE\nNATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 7,723 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n13 27 3 43\n107 17 124 230\nVEHICLE PLATE NUMBER PRODUCTION \nTotal Production Total Registration\n8,492 3,797\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n12\nANAMBRA\nSTATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n16 0 2 1\nBrake\nFailure\n7\nOverloading\n0 0 0 6\nWorn Out Tyre\n0\n2 1 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 1 0\nSign Light \nViolation OTHERS Total\nRoad tr\n36\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n13\nBAUCHI STATE\nNATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 2,236 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n28 67 1 96\n494 59 553 816\nVEHICLE PLATE NUMBER PRODUCTION \nTotal Production Total Registration\n3,800 404\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n14\nBAUCHI STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n28 0 20 2\nBrake\nFailure\n2\nOverloading\n0 4 13 6\nWorn Out Tyre\n7\n0 2 3 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n2 1 0\nSign Light \nViolation OTHERS Total\nRoad tr\n90\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n15\nBAYELSA STATE\nNATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 2,482 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n4 11 8 23\n40 6 46 123\nVEHICLE PLATE NUMBER PRODUCTION \nTotal Production Total Registration\n700 538\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n16\nBAYELSA STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n7 0 1 2\nBrake\nFailure\n2\nOverloading\n0 0 4 0\nWorn Out Tyre\n0\n0 0 0 0\nSleeping on \nSteering\n5\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 2\nSign Light \nViolation OTHERS Total\nRoad tr\n23\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n17\nBENUE STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 2,273\nTOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n15 26 21 62\n186 22 208 296\nTotal Production Total Registration\n0 300\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n18\nBENUE STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n62 0 0 0\nBrake\nFailure\n0\nOverloading\n0 0 1 0\nWorn Out Tyre\n0\n0 0 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 0\nSign Light \nViolation OTHERS Total\nRoad tr\n63\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n19\nBORNO STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 1,394 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n5 8 0 13\n72 20 92 120\nTotal Production Total Registration\n2,703 199\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n20\nBORNO STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n7 0 1 0\nBrake\nFailure\n0\nOverloading\n1 0 1 0\nWorn Out Tyre\n0\n0 1 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 0\nSign Light \nViolation OTHERS Total\nRoad tr\n11\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n21\nCROSS RIVER STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 1,294 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n4 9 1 14\n32 5 37 77\nTotal Production Total Registration\n0 487\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n22\nCROSS RIVER\nSTATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n6 0 1 0\nBrake\nFailure\n1\nOverloading\n0 0 2 1\nWorn Out Tyre\n0\n2 0 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 3 0\nSign Light \nViolation OTHERS Total\nRoad tr\n16\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n23\nDELTA STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 10,338 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n18 13 0 31\n141 36 177 307\nTotal Production Total Registration\n6,609 3,421\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n24\nDELTA STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n17 0 7 1\nBrake\nFailure\n0\nOverloading\n2 0 0 0\nWorn Out Tyre\n0\n2 2 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 3 0\nSign Light \nViolation OTHERS Total\nRoad tr\n34\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n25\nEBONYI STATE\nNATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 1,635 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n5 20 0 25\n113 14 127 238\nVEHICLE PLATE NUMBER PRODUCTION \nTotal Production Total Registration\n9,500 1,100\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n26\nEBONYI STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n6 0 2 0\nBrake\nFailure\n0\nOverloading\n0 1 5 5\nWorn Out Tyre\n0\n0 0 4 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 0\nSign Light \nViolation OTHERS Total\nRoad tr\n23\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n27\nEDO STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 6,974 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n18 27 2 47\n197 40 237 447\nTotal Production Total Registration\n4,004 3,858\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n28\nEDO STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n26 0 1 0\nBrake\nFailure\n10\nOverloading\n0 0 2 1\nWorn Out Tyre\n0\n2 1 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 0\nSign Light \nViolation OTHERS Total\nRoad tr\n43\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n29\nEKITI STATE\nNATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 1,764 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n3 14 0 17\n44 3 47 79\nVEHICLE PLATE NUMBER PRODUCTION \nTotal Production Total Registration\n0 1,235\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n30\nEKITI STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n7 0 0 0\nBrake\nFailure\n3\nOverloading\n0 0 3 1\nWorn Out Tyre\n0\n0 0 0 1\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 1\nSign Light \nViolation OTHERS Total\nRoad tr\n16\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n31\nENUGU STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 9,073 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n15 25 8 48\n173 28 201 394\nTotal Production Total Registration\n6,703 2,439\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n32\nENUGU STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n24 0 5 2\nBrake\nFailure\n12\nOverloading\n0 3 4 3\nWorn Out Tyre\n0\n0 1 0 1\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 0\nSign Light \nViolation OTHERS Total\nRoad tr\n55\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n33\nGOMBE STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 1,247 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n7 49 1 57\n172 17 189 322\nTotal Production Total Registration\n2,555 400\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n34\nGOMBE STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n22 0 3 1\nBrake\nFailure\n2\nOverloading\n2 0 0 12\nWorn Out Tyre\n0\n3 1 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 8 0\nSign Light \nViolation OTHERS Total\nRoad tr\n54\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n35\nIMO STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 2871 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n11 23 2 36\n117 23 140 317\nTotal Production Total Registration\n4,602 1,337\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n36\nIMO STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n3 0 3 1\nBrake\nFailure\n2\nOverloading\n0 1 7 2\nWorn Out Tyre\n0\n2 2 4 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 0\nSign Light \nViolation OTHERS Total\nRoad tr\n27\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n37\nJIGAWA STATE\nNATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 1,046 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n38 42 1 81\n145 74 219 459\nVEHICLE PLATE NUMBER PRODUCTION \nTotal Production Total Registration\n0 393\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n38\nJIGAWA STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n22 0 5 3\nBrake\nFailure\n0\nOverloading\n2 0 1 1\nWorn Out Tyre\n0\n2 2 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 12\nSign Light \nViolation OTHERS Total\nRoad tr\n50\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n39\nKADUNA STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 7,651 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n74 159 5 238\n995 180 1175 1983\nTotal Production Total Registration\n1,798 6,278\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n40\nKADUNA STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n139 1 30 16\nBrake\nFailure\n0\nOverloading\n3 6 11 18\nWorn Out Tyre\n11\n7 10 0 0\nSleeping on \nSteering\n1\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n1 4 0\nSign Light \nViolation OTHERS Total\n258\nRoad traffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n41\nKANO STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 4,998 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n28 85 2 115\n384 47 431 763\nTotal Production Total Registration\n3,012 479\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n42\nKANO STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n61 0 8 1\nBrake\nFailure\n7\nOverloading\n0 2 10 7\nWorn Out Tyre\n2\n1 4 2 0\nSleeping on \nSteering\n1\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n2 5 0\nSign Light \nViolation OTHERS Total\n113\nRoad traffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n43\nKATSINA STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2017 1,877 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n18 53 0 71\n361 57 418 566\nTotal Production Total Registration\n15 415\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n44\nKATSINA STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n28 1 7 0\nBrake\nFailure\n0\nOverloading\n1 4 7 9\nWorn Out Tyre\n0\n15 1 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 0\nSign Light \nViolation OTHERS Total\nRoad tr\n73\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n45\nKEBBI STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 782\nTOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n4 28 6 38\n82 5 87 124\nTotal Production Total Registration\n2,910 454\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n46\nKEBBI STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n24 0 2 0\nBrake\nFailure\n0\nOverloading\n0 0 3 4\nWorn Out Tyre\n0\n2 0 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 0\nSign Light \nViolation OTHERS Total\nRoad tr\n35\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n47\nKOGI STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 1,959\nTOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n19 57 10 86\n301 51 352 778\nTotal Production Total Registration\n3,848 325\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n48\nKOGI STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n53 0 4 3\nBrake\nFailure\n10\nOverloading\n1 1 15 3\nWorn Out Tyre\n0\n3 0 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 1\nSign Light \nViolation OTHERS Total\nRoad tr\n94\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n49\nKWARA STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 3,493\nTOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n25 46 5 76\n252 53 305 566\nTotal Production Total Registration\n2 1,204\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n50\nKWARA STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n27 0 6 3\nBrake\nFailure\n11\nOverloading\n0 1 8 2\nWorn Out Tyre\n0\n0 3 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 0\nSign Light \nViolation OTHERS Total\nRoad tr\n61\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n51\nLAGOS STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 55,386 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n22 39 28 89\n241 30 271 872\nTotal Production Total Registration\n48,131 65,136\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n52\nLAGOS STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n39 0 8 9\nBrake\nFailure\n7\nOverloading\n1 0 2 8\nWorn Out Tyre\n0\n9 0 2 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 4\nSign Light \nViolation OTHERS Total\nRoad tr\n89\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n53\nNASARAWA STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 2,819 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n22 66 40 128\n358 36 394 636\nTotal Production Total Registration\n1,886 396\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n54\nNASARAWA\nSTATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n69 0 7 0\nBrake\nFailure\n2\nOverloading\n1 2 18 20\nWorn Out Tyre\n0\n2 1 1 2\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 8\nSign Light \nViolation OTHERS Total\n133\nRoad traffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n55\nNIGER STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 2,540 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n42 79 5 126\n385 75 460 847\nTotal Production Total Registration\n12,109 422\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n56\nNIGER STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n71 1 9 4\nBrake\nFailure\n3\nOverloading\n2 5 7 6\nWorn Out Tyre\n1\n7 4 3 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n1 0 1\nSign Light \nViolation OTHERS Total\n125\nRoad traffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n57\nOGUN STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 13,187\nTOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n42 89 18 640\n485 72 557 1262\nTotal Production Total Registration\n2,813 640\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n58\nOGUN STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n87 0 8 2\nBrake\nFailure\n16\nOverloading\n2 0 16 10\nWorn Out Tyre\n2\n5 6 1 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 0\nSign Light \nViolation OTHERS Total\n167\nRoad traffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n59\nONDO STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 4,493 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n41 61 12 114\n323 74 397 825\n6,101\nTotal Production Total Registration\n5,304 515\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n60\nONDO STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n72 0 8 2\nBrake\nFailure\n17\nOverloading\n1 4 9 4\nWorn Out Tyre\n5\n2 5 1 1\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 0\nSign Light \nViolation OTHERS Total\n131\nRoad traffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n61\nOSUN STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 3,858 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n1 31 1 43\n215 22 237 442\n6,101\nTotal Production Total Registration\n1 592\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n62\nOSUN STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n24 0 14 1\nBrake\nFailure\n1\nOverloading\n0 0 3 1\nWorn Out Tyre\n0\n5 3 0 1\nSleeping on \nSteering\n1\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n1 0 0\nSign Light \nViolation OTHERS Total\nRoad tr\n55\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n63\nOYO STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 14,004 TOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n42 59 4 105\n308 66 374 732\n6,101\nTotal Production Total Registration\n13,202 2,040\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n64\nOYO STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n63 0 8 7\nBrake\nFailure\n2\nOverloading\n0 0 15 13\nWorn Out Tyre\n0\n6 4 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n4 1 0\nSign Light \nViolation OTHERS Total\n123\nRoad traffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n65\nPLATEAU STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2017 2,883\nTOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n6 22 35 63\n225 6 231 454\n6,101\nTotal Production Total Registration\n4,377 858\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n66\nPLATEAU STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n31 0 0 0\nBrake\nFailure\n13\nOverloading\n0 0 11 1\nWorn Out Tyre\n0\n0 2 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n3 1 0\nSign Light \nViolation OTHERS Total\nRoad tr\n62\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n67\nRIVERS STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 11,862\nTOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n5 14 12 31\n50 5 55 169\n6,101\nTotal Production Total Registration\n4 2,815\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n68\nRIVERS STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n13 0 2 4\nBrake\nFailure\n3\nOverloading\n0 2 2 2\nWorn Out Tyre\n0\n1 0 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 0\nSign Light \nViolation OTHERS Total\nRoad tr\n29\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n69\nSOKOTO STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 967\nTOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n9 23 0 32\n153 22 175 186\n6,101\nTotal Production Total Registration\n4 459\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n70\nSOKOTO STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n13 0 2 1\nBrake\nFailure\n1\nOverloading\n3 3 0 4\nWorn Out Tyre\n0\n2 0 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 0\nSign Light \nViolation OTHERS Total\nRoad tr\n29\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n71\nTARABA STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 593\nTOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n4 22 2 28\n76 9 85 123\n6,101\nTotal Production Total Registration\n805 967\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n72\nTARABA STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n3 0 0 0\nBrake\nFailure\n0\nOverloading\n0 0 8 13\nWorn Out Tyre\n0\n2 0 0 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 0\nSign Light \nViolation OTHERS Total\nRoad tr\n26\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n73\nYOBE STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 915\nTOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n8 22 0 30\n220 16 236 289\n6,101\nTotal Production Total Registration\n409 188\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n74\nYOBE STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n14 0 3 2\nBrake\nFailure\n0\nOverloading\n0 0 6 1\nWorn Out Tyre\n0\n0 0 1 1\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 0 0\nSign Light \nViolation OTHERS Total\nRoad tr\n28\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n75\nZAMFARA STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 420\nTOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n13 26 1 40\n156 38 194 301\n6,101\nTotal Production Total Registration\n2,477 113\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n76\nZAMFARA STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n16 0 8 0\nBrake\nFailure\n0\nOverloading\n1 1 2 9\nWorn Out Tyre\n0\n3 0 3 0\nSleeping on \nSteering\n0\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n0 1 3\nSign Light \nViolation OTHERS Total\nRoad tr\n44\naffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n77\nTOTAL STATE\nVEHICLE PLATE NUMBER PRODUCTION NATIONAL DRIVER LICENSE PRODUCTION \nQ2, 2018 221,878\nTOTAL\nPRODUCTION\nROAD TRAFFIC CASHES\nFatal Serious Minor Total Cases\nNumber Injured Number Killed Total Casualty People Involved\n682 1,578 285 2,545\n8,437 1,331 9,768 18,320\n6,101\nTotal Production Total Registration\n160,903 107,000\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n78\nTOTAL STATE CAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES\nSpeed Violation Use of Phone\n While Driving Tyre Burst Mechanically \nDeficient Vehicle\n1,321 4 224 73\nBrake\nFailure\n144\nOverloading\n25 42 208 219\nWorn Out Tyre\n28\n118 75 22 9\nSleeping on \nSteering\n10\nDriving Under\n Alcohol/Drug \nInfluence Poor Weather\nFatigue\n13 38 35\nSign Light \nViolation OTHERS Total\n2,608 Road traffic crashes\nDangerous \nOvertaking\nDangerous \nDriving Bad Road\nRoute Violation Road Obstruction \nViolation\nRoad Transport Data - Q2 2018\n79\nSex Distribution of Persons Injured \nIn Road Traffic Crashes \nMALE ADULT\nFREQUENCY\n6,151\nPERCENTAGE\n73%\nMALE CHILD\nFREQUENCY\n264\nPERCENTAGE\n3%\nFEMALE ADULT\nFREQUENCY\n1,795\nPERCENTAGE\n21%\nFEMALE CHILD\nFREQUENCY\n227\nPERCENTAGE\n3%\nTotal 8,437\nFREQUENCY\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n80\nSex Distribution of Persons Killed\nIn Road Traffic Crashes \nMALE ADULT\nFREQUENCY\n 1,007\nPERCENTAGE\n76%\nMALE CHILD\nFREQUENCY\n40\nPERCENTAGE\n3%\nFEMALE ADULT\nFREQUENCY\n250\nPERCENTAGE\n19%\nFEMALE CHILD\nFREQUENCY\n34\nPERCENTAGE\n3%\nTotal 1,331\nFREQUENCY\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n81\nNumber of Vehicles Involved In \nRoad Traffic Crashes \nVEHICLES INVOLVED\nBicycle Motorcycle Tricycle Car\nSuv(jeep) Van Minibus Luxury Bus\n6 730 107 1,331\n100 7 848 14\nPick-up Truck Tanker Trailer\n117 480 71 181\nOthers\n15\nTotal 4,059\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n82\nCategory of Vehicle Involved \nIn Road Traffic Crashes \nTotal\n4,059\nTotal\n2,447\nTotal\n1,568\nTotal\n44\nTotal\n0\nCommercial Private Government Diplomat\nPercentage %\n60.29% 38.63% 1.08% 0.00%\nPercentage % Percentage % Percentage %\nRoad Transport Data - Q2 2018\nRoad Transport Data - Q2 2018\n83\nMethodology\nData is supplied administratively by the Federal Road Safety Corps (FRSC) and verified and validated by the \nNational Bureau of Statistics, Nigeria (NBS).\nRoad Transport Data - Q2 2018\n84\nAppendix\nQ1 2018\nQ3 2017 % Share Q4 2017 % Share Q1 2018 % Share Q2 2018 % Share\nPRIVATE 4,656,725\n \n40.33\n \n4,682,309\n \n40.42\n \n4,739,939 40.67 4,819,251 40.98\nCOMMERCIAL 6,749,461\n \n58.45\n \n6,756,372\n \n58.33\n \n6,768,756 58.08 6,785,956 57.70\nGOVERNMENT 135,216\n \n1.17\n \n138,761\n \n1.20\n \n139,264 1.19 149,470 1.27\nDIPLOMATIC 5,834\n \n0.05\n \n5,889\n \n0.05\n \n5,912 0.05 6,194 0.05\nTOTAL 11,547,236\n \n100.00 11,583,331.00\n \n100.00 11,653,871 100.00 11,760,871 100.00\n2018 Estimated Population* \nNational Poulation Commision\nestimatee\n198,000,000\nVehicle per population \nQ1 2018 using 2016 \nestimated population\n0.06\nRoad Transport Data - Q2 2018\n85\nAppendix\nSTATE FATAL SERIOUS MINOR TOTAL CASES NUMBER INJURED NUMBER KILLED TOTAL CASUALTY PEOPLE INVOLVED\nAbia 3 11 0 14 64 12 76 171\nAdamawa 6 20 0 26 103 13 116 173\nAkwa Ibom 9 9 3 21 33 12 45 96\nAnambra 13 27 3 43 107 17 124 230\nBauchi 28 67 1 96 494 59 553 816\nBayelsa 4 11 8 23 40 6 46 123\nBenue 15 26 21 62 186 22 208 296\nBorno 5 8 0 13 72 20 92 120\nCross River 4 9 1 14 32 5 37 77\nDelta 18 13 0 31 141 36 177 307\nEbonyi 5 20 0 25 113 14 127 238\nEdo 18 27 2 47 197 40 237 447\nEkiti 3 14 0 17 44 3 47 79\nEnugu 15 25 8 48 173 28 201 394\nFCT 45 196 48 289 634 66 700 1737\nGombe 7 49 1 57 172 17 189 322\nImo 11 23 2 36 117 23 140 317\nJigawa 38 42 1 81 145 74 219 459\nKaduna 74 159 5 238 995 180 1175 1983\nKano 28 85 2 115 384 47 431 763\nKatsina 18 53 0 71 361 57 418 566\nKebbi 4 28 6 38 82 5 87 124\nKogi 19 57 10 86 301 51 352 778\nKwara 25 46 5 76 252 53 305 566\nLagos 22 39 28 89 241 30 271 872\nNasarawa 22 66 40 128 358 36 394 636\nNiger 42 79 5 126 385 75 460 847\nOgun 42 89 18 149 485 72 557 1262\nOndo 41 61 12 114 323 74 397 825\nOsun 11 31 1 43 215 22 237 442\nOyo 42 59 4 105 308 66 374 732\nPlateau 6 22 35 63 225 6 231 454\nRivers 5 14 12 31 50 5 55 169\nSokoto 9 23 0 32 153 22 175 186\nTaraba 4 22 2 28 76 9 85 123\nYobe 8 22 0 30 220 16 236 289\nZamfara 13 26 1 40 156 38 194 301\nTOTAL 682 1578 285 2545 8437 1331 9768 18320\nROAD TRAFFIC CRASHES ON STATE BASIS (2ND QUARTER 2018)\nRoad Transport Data - Q2 2018\n86\nAppendix\nFrequency %\nMALE ADULT 6151 73\nFEMALE ADULT 1795 21\nMALE CHILD 264 3\nFEMALE CHILD 227 3\nTOTAL 8437 100\nSource: Federal Road Safety Corps (FRSC)\nSEX DISTRIBUTION OF PERSONS INJURED IN RTC \n(2ND QUARTER 2018)\nSEX Persons Injured\nFrequency %\nMALE ADULT 1007 76\nFEMALE ADULT 250 19\nMALE CHILD 40 3\nFEMALE CHILD 34 3\nTOTAL 1331 100\nSEX DISTRIBUTION OF PERSONS KILLED IN RTC \n(2ND QUARTER 2018)\nSEX Persons Killed\nRoad Transport Data - Q2 2018\n87\nAppendix\nBIC\nYCLE\nM\nO\nT\nO\nR\nC\nYCLE\nT\nRIC\nYCLE\nC\nA\nR\nSU\nV(JEEP)\nVA\nN\nMINIB\nUS\nLU\nX\nU\nR\nY\nB\nUS\nPIC\nK-U\nP\nT\nR\nU\nC\nK\nTA\nN\nKER\nT\nR\nAILER\nO\nT\nHERS\nT\nO\nTAL\nTOTAL No 6 730 107 1383 100 7 848 14 117 480 71 181 15 4059\n% 0.15 17.98 2.64 34.07 2.46 0.17 20.89 0.34 2.88 11.83 1.75 4.46 0.37 100.00\nSource: Federal Road Safety Corps (FRSC)\nNUMBER OF VEHICLES INVOLVED IN ROAD TRAFFIC CRASHES (2ND QUARTER 2018)\n2ND \nQUARTER \n2018\nVEHICLE INVOLVED\nP\nRIVAT\nE\nC\nO\nM\nM\nE\nR\nCIA\nL\nG\nO\nV\nE\nR\nN\nM\nE\nN\nT\nDIP\nL\nO\nM\nAT\nT\nO\nTA\nL\nTOTAL 1568 2447 44 0 4059\n% 38.63 60.29 1.08 0.00 100.00\nCATEGORY OF VEHICLES INVOLVED IN ROAD \nTRAFFIC CRASHES (2ND QUARTER 2018)\n2ND \nQUARTER \n2018\nVEHICLE CATEGORY\nRoad Transport Data - Q2 2018\n88\nAppendix\nS/N State CMC PRDC CMC REGD CMV PRDC CMV \nREGD PMC PRDC PMC REGD PMV PRDC PMV REGD Total PRDC Total REGD\n1 ABIA 1,400 0 1,900 363 1,000 0 3,701 671 8,001 1,034\n2 ADAMAWA 0 2 0 68 0 44 4 280 4 394\n3 AKWA IBOM 0 0 0 222 0 0 0 370 0 592\n4 ANAMBRA 1,600 1 2,100 1,560 3,300 12 1,492 2,224 8,492 3,797\n5 BAUCHI 1,000 92 450 61 1,450 153 900 98 3,800 404\n6 BAYELSA 0 5 0 85 0 0 700 448 700 538\n7 BENUE 0 1 0 67 0 0 0 232 0 300\n8 BORNO 2,200 0 150 43 0 0 353 156 2,703 199\n9 CROSS RIVER 0 0 0 30 0 0 0 457 0 487\n10 DELTA 3,900 1 700 716 0 0 2,009 2,704 6,609 3,421\n11 EBONYI 4,400 0 1,700 708 1,700 0 1,700 392 9,500 1,100\n12 EDO 250 0 500 857 750 5 2,504 2,996 4,004 3,858\n13 EKITI 0 323 0 115 0 430 0 367 0 1,235\n14 ENUGU 1,500 2 1,499 724 1,500 0 2,204 1,713 6,703 2,439\n15 FCT 0 0 0 21 0 2 123 753 123 776\n16 FEDERAL \nGOVERNMENT 0 0 0 0 0 0 0 0 0 0\n17 GOMBE 1,300 95 250 96 900 47 105 162 2,555 400\n18 IMO 0 0 1,000 193 0 0 3,602 1,144 4,602 1,337\n19 JIGAWA 0 30 0 163 0 12 0 188 0 393\n20 KADUNA 0 159 0 852 0 68 1,798 5,199 1,798 6,278\n21 KANO 400 2 350 77 100 1 2,162 399 3,012 479\n22 KATSINA 0 4 0 91 2 12 13 308 15 415\n23 KEBBI 0 20 550 89 1,300 115 1,060 230 2,910 454\n24 KOGI 785 30 325 51 1,290 28 1,448 216 3,848 325\n25 KWARA 0 2 0 325 0 7 2 870 2 1,204\n26 LAGOS 5,115 5,966 5,000 8,107 18 64 37,998 50,999 48,131 65,136\n27 NASSARAWA 758 2 125 149 837 30 166 215 1,886 396\n28 NIGER 4,400 0 500 46 6,201 0 1,008 376 12,109 422\n29 OGUN 0 102 700 109 1,000 235 1,113 194 2,813 640\n30 ONDO 1,600 2 800 88 1,201 3 1,703 422 5,304 515\n31 OSUN 0 0 0 204 0 1 1 387 1 592\n32 OYO 0 0 800 254 10,000 1,277 2,402 509 13,202 2,040\n33 PLATEAU 0 0 1,398 235 0 0 2,979 623 4,377 858\n34 RIVERS 0 0 0 311 0 0 4 2,504 4 2,815\n35 SOKOTO 0 11 0 35 0 187 4 226 4 459\n36 TARABA 450 479 0 27 350 404 5 57 805 967\n37 YOBE 0 19 0 37 0 0 409 132 409 188\n38 ZAMFARA 600 0 335 21 1,000 1 542 91 2,477 113\nTOTAL 31,658 7,350 21,332 17,200 33,899 3,138 74,214 79,312 160,903 107,000\nNUMBER PLATE PRODUCED VS REGISTERED SUMMARY BETWEEN 01-Apr-2018 AND 30-Jun-2018\nRoad Transport Data - Q2 2018\n89\nAppendix\nS/N State No Issued %\n1 Abia 4,428 2.00\n2 Adamawa 1,327 0.60\n3 Akwa-Ibom 3,743 1.69\n4 Anambra 7,723 3.48\n5 Bauchi 2,236 1.01\n6 Bayelsa 2,482 1.12\n7 Benue 2,273 1.02\n8 Borno 1,394 0.63\n9 Cross-River 1294 0.58\n10 Delta 10,338 4.66\n11 Ebonyi 1,635 0.74\n12 Edo 6,974 3.14\n13 Ekiti 1764 0.80\n14 Enugu 9,073 4.09\n15 FCT 25,415 11.45\n16 Gombe 1,247 0.56\n17 Imo 2871 1.29\n18 Jigawa 1,046 0.47\n19 Kaduna 7,651 3.45\n20 Kano 4,998 2.25\n21 Katsina 1877 0.85\n22 Kebbi 782 0.35\n23 Kogi 1,959 0.88\n24 Kwara 3,493 1.57\n25 Lagos 55,386 24.96\n26 Nasarawa 2,819 1.27\n27 Niger 2,491 1.12\n28 Ogun 12,682 5.72\n29 Ondo 4,493 2.02\n30 Osun 4,340 1.96\n31 Oyo 14,004 6.31\n32 Plateau 2,883 1.30\n33 Rivers 11862 5.35\n34 Sokoto 967 0.44\n35 Taraba 593 0.27\n36 Yobe 915 0.41\n37 Zamfara 420 0.19\nTotal 221,878 100.00\nNATIONAL DRIVER LICENSE PRODUCTION ON STATE BASIS (2ND \nQUARTER 2018)\nRoad Transport Data - Q2 2018\nSTATE\nS\nP\nV\nU\nP\nD\nT\nB\nT\nM\nD\nV\nBFL\nO\nVL\nD\nO\nT\nW\nO\nT\nD\nG\nD\nB\nR\nD\nRT\nV\nO\nB\nS\nS\nO\nS\nD\nA\nD\nP\nW\nR\nFT\nQ\nSLV\nO\nT\nHER\nS\nT\nO\nTAL\nAbia 3 0 2 1 1 0 0 1 2 0 3 0 0 0 0 0 0 0 13\nAdamawa 18 0 1 0 0 1 0 1 0 0 1 7 0 0 0 0 1 0 30\nAkwa Ibom 13 0 1 0 0 0 0 0 0 0 3 2 0 0 0 0 1 0 20\nAnambra 16 0 2 1 7 0 0 0 6 0 2 1 0 0 0 0 1 0 36\nBauchi 28 0 20 2 2 0 4 13 6 7 0 2 3 0 0 2 1 0 90\nBayelsa 7 0 1 2 2 0 0 4 0 0 0 0 0 0 5 0 0 2 23\nBenue 62 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 63\nBorno 7 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 11\nCross River 6 0 1 0 1 0 0 2 1 0 2 0 0 0 0 0 3 0 16\nDelta 17 0 7 1 0 2 0 0 0 0 2 2 0 0 0 0 3 0 34\nEbonyi 6 0 2 0 0 0 1 5 5 0 0 0 4 0 0 0 0 0 23\nEdo 26 0 1 0 10 0 0 2 1 0 2 1 0 0 0 0 0 0 43\nEkiti 7 0 0 0 3 0 0 3 1 0 0 0 0 1 0 0 0 1 16\nEnugu 24 0 5 2 12 0 3 4 3 0 0 1 0 1 0 0 0 0 55\nFCT 187 1 23 4 9 1 2 10 44 0 24 10 0 2 2 2 5 3 329\nGombe 22 0 3 1 2 2 0 0 12 0 3 1 0 0 0 0 8 0 54\nImo 3 0 3 1 2 0 1 7 2 0 2 2 4 0 0 0 0 0 27\nJigawa 22 0 5 3 0 2 0 1 1 0 2 2 0 0 0 0 0 12 50\nKaduna 139 1 30 16 0 3 6 11 18 11 7 10 0 0 1 1 4 0 258\nKano 61 0 8 1 7 0 2 10 7 2 1 4 2 0 1 2 5 0 113\nKatsina 28 1 7 0 0 1 4 7 9 0 15 1 0 0 0 0 0 0 73\nKebbi 24 0 2 0 0 0 0 3 4 0 2 0 0 0 0 0 0 0 35\nKogi 53 0 4 3 10 1 1 15 3 0 3 0 0 0 0 0 0 1 94\nKwara 27 0 6 3 11 0 1 8 2 0 0 3 0 0 0 0 0 0 61\nLagos 39 0 8 9 7 1 0 2 8 0 9 0 2 0 0 0 0 4 89\nNasarawa 69 0 7 0 2 1 2 18 20 0 2 1 1 2 0 0 0 8 133\nNiger 71 1 9 4 3 2 5 7 6 1 7 4 3 0 0 1 0 1 125\nOgun 87 0 20 2 16 2 0 16 10 2 5 6 1 0 0 0 0 0 167\nOndo 72 0 8 2 17 1 4 9 4 5 2 5 1 1 0 0 0 0 131\nOsun 24 0 14 1 1 0 0 3 1 0 5 3 0 1 1 1 0 0 55\nOyo 63 0 8 7 2 0 0 15 13 0 6 4 0 0 0 1 4 0 123\nPlateau 31 0 0 0 13 0 0 11 1 0 0 2 0 0 0 3 1 0 62\nRivers 13 0 2 4 3 0 2 2 2 0 1 0 0 0 0 0 0 0 29\nSokoto 13 0 2 1 1 3 3 0 4 0 2 0 0 0 0 0 0 0 29\nTaraba 3 0 0 0 0 0 0 8 13 0 2 0 0 0 0 0 0 0 26\nYobe 14 0 3 2 0 0 0 6 1 0 0 0 1 1 0 0 0 0 28\nZamfara 16 0 8 0 0 1 1 2 9 0 3 0 0 0 0 0 1 3 44\nTotal 1321 4 224 73 144 25 42 208 219 28 118 75 22 9 10 13 38 35 2608\n% of Causes 50.65 0.15 8.59 2.80 5.52 0.96 1.61 7.98 8.40 1.07 4.52 2.88 0.84 0.35 0.38 0.50 1.46 1.34 100.00\nCAUSATIVE FACTORS OF ROAD TRAFFIC CRASHES (2ND QUARTER 2018)\n89\nAppendix\nCAUSATIVE FACTORS CODE\nSpeed Violation (SPV) SPV\nUse of Phone While Driving (UPWD) UPWD\nTyre Burst (TBT) TBT\nMechanically Deficient Vehice (MDV) MDV\nBrake Failure (BFL) BFL\nOverloading (OVL) OVL\nDangerous Overtaking (DOT) DOT\nWrongful Overtaking (WOT) WOT\nDangerous Driving (DGD) DGD\nBad Road (BRD) BRD\nRoute Violation (RTV) RTV\nRoad Obstruction Violation (OBS) OBS\nSleeping on Steering (SOS) SOS\nDriving Under Alcohol/Drug Influence (DAD) DAD\nPoor Weather (PWR) PWR\nFatique (FTQ) FTQ\nSign Light Violation (SLV) SLV\nOthers OTH\nLEGEND OF CAUSATIVE FACTORS CODE\nRoad Transport Data - Q2 2018\n91\nAcknowledgements/Contacts \nWe acknowledge the contributions of our strategic partner Federal Road Safety Corps (FRSC) and our \ntechnical partner, Proshare in the design, concept and production of this publication.\nAcknowledgements\nContact Us\nHead Office Address\nPlot 762,IndependenceAvenue,Central\nBusiness District,FCT, Abuja Nigeria.\nfeedback@nigerianstat.gov.ng +234 803 386 5388\n@nigerianstat NBSNigeria www.nigerianstat.gov.ng \nRoad Transport Data - Q2 2018"
  },
  {
    "url": "https://nvis.frsc.gov.ng/VehicleManagement/RegisterVehicle",
    "text": "Vehicle Registration\n\n# Vehicle Registration\n\n#### Vehicle Information\n\nVehicle Category\n\n-- Select Vehicle Category -- Commercial Government Private\n\nVehicle Sub-Category\n\n-- Select Sub Vehicle Category --\n\nOld Plate Number\n\nVehicle Make -- Select Vehicle Make -- 100 100 1617 190 420 5 SERIES ACCENT ACURA ACURA LEGEND ACURA TI 3.2 AERONAUTIC HAICE BUS ALLOY RIM LIFAN ALTIMA AM GENERAL APOLLO ARMADA JEEP ASCONA ASCONAL OPEL ASHOK LEYLAND 1618 ASTON MARTIN ATUL 3WHEELER AUDI AUDI 200 AUDI 400 AUDI 80 AUDI A6 AUDI Q7 AULTIMA AUSTIN AUSTIN HEALEY B M W 3 SERIES B M W 520 B M W 525I B M W 730 B M W 735 SERIES B M W HIACE BUS B M W JEEP B M W530 B MW 316 B. M. W 316 S/CAR B.M.C. B.M.W 318 B.M.W 7351 B.M.W X 5 BAIC BAJAJ BAJAJ BM100 BAOLONG BASCO BAW BAW HIACE BC125-7 BEDFORD BEDFORD MIDI BEDFORD RASCAL BEDFORD TIPPER BEDFORD TIPPER LORRY BEDFORD/TIM/LORRY BEETTLE BENTLEY BENZ ML BENZ200 BLUE BIRD REGULAR BLUE/BIRD BLUE/BIRD BLUE/BIRD BLUE/BIRD BLUE/BIRD BLUE/BIRD BMW BMW 225 BMW 3 SERIES BMW 320 BMW 325 BMW 328 BMW 5 SERIES BMW 5251 BMW 7 SERIES BMW 7304 BMW 740 IL BMW BUS BMW SERIES 7 BMW320 BORD SEIRA BORD SEIRA BORD SEIRA BORD SEIRA BORD SEIRA BORD SEIRA BOXER BOXER B M BOXERS BAJAJ BRILLIANCE BRILLIANCE SPLENDOR BUGATTI BUICK BYD BYD F3 GLX-I CABSTER CADILLAC CANTER PICK UP CARGO SUPER CARINA E CARTER CY 90 CERATO CG 100 CARTER CGL 125 CHANA CHANA LORRY CHANG AN CHANGHE CHANLIN CHENGLONG BRAND DUMP CHERROLET AREOLT CHERRY TIGGO CHERY CHEV PRISM CHEVROLET CHEVROLET ASTRO BUS CHEVROLET BLAZER CHEVROLET CAVALIER CHEVROLET OPTRA 5 CHEVY 20 CHEVY P UP CHEVY VAN CHEVYVENTURE CHEVROLET CHINDADI P/UP CHROOKE JEEP CHRYSLER CHRYSLER CONCORD CHRYSLER NEON CIELO DAEWOO CITROEN CITY CIVIC 1.7 VTISE CNHTC COMBO CORDOBA COROLLA COURIER CRUISER CARGO CRV DADDY P/UP DADI DADI P/UP DAEWOO DAEWOO ESPERO DAEWOO NUBIRA DAEWOO LEGANZA DAEWOO LUBLIN VAN DAEWOO RACER DAF DAF 1100 DAF 1300 P/UP DAF 1700 TRUCK DAF 2100 DAF 45 TRUCK DAF 95 TRUCK DAF LORRY DAF TIPPER LORRY DAF TRACTOR DAF XF 430 DAIHATSU DAIHATSU APPLAUSE DAIMLER BENZ 308 DAIMLER CHRYSLER JEEP DANCIA LOGAN DASHATSU APPLAUSE DASTUN DATSUN DATSUN 140J DATSUN 1500 DATSUN 160J DATSUN 180 DATSUN B/BIRD DATSUN C20 DATSUN CABSTAR DATSUN CHERRY DATSUN LAUREL DATSUN PANEL VANN DATSUN STANZA DATSUN SUNNY DATSUN SUNNY DATSUN SUNNY DATSUN SUNNY DATSUN SUNNY DATSUN SUNNY DATSUN SUNNY DATSUN SUNNY DATSUN SUNNY DATSUN SUNNY DATSUN SUNNY DEER DIAHAZU JEEP DODGE CARAVAN DODGE DURANGO DODGE GRAND DODGE GRAND CARAVAN DODGE RAM DOGER CARVAN DONGFENG DOUBLE CABIN DUCAR 110-2 DURANGO JEEP EAGLE TALON EBRO ELANTRA EURO TECH EVERUS FABIA SEDAN FANAGON VOLK FAW FERRARI FIAT FIAT CUTROEN FIAT PUNTO FIAT TRUCK FIAT TRUCK FIAT TRUCK FIAT TRUCK FIAT TRUCK FIAT TRUCK FOED IVECO FOED SIERRA FOED SIERRA FOED SIERRA FOED SIERRA FOED SIERRA FOED SIERRA FORD FORD AEROSTAR BUS FORD AEROSTAR S/BUS FORD AEROSTAR XLT FORD BUS FORD ECONO FORD ECONOLINE FORD ECONOMOVAN BUS FORD ECONOVAN FORD ESCORT FORD ESCORT WAGON FORD ESCOSPORT FORD EXPLORER FORD F150 JEEP FORD FIESTA FORD FOCUS FORD GOSWORTH FORD ISUZU FORD MONDEO FORD MUSTANG FORD P/UP FORD PROBE FORD RANGER FORD SCORPIO FORD SILHOUTTE FORD TAURUS FORD TIPPER FORD TRANSIT FORD TRUCK FORD WINDSTAR FORD WINSTAR FORTA FRAGEND FRAJEND FUKANG FUQI FUXING G M C SAVANA G M C VAN GAC GAIG GALLARD GEELY GEWLY MK GEWLY MK GEWLY MK GEWLY MK GEWLY MK GEWLY MK GEWLY MK GEWLY MK GEWLY MK GEWLY MK GEWLY MK GEWLY MK GEWLY MK GEWLY MK GK 125 GMC GMC TRUCK GO WELL GO WELL M/C GOLDEN GOLDEN DRAGON BUS GOLF 3 GOLF 3 STATION WAGON GOLF VOLKSWAGEN GONOW GRAND CHEROKEE GREAT WALL DEER GREEN FIELD MOTOR GUIZHOU / YUNQUE HAFEI HAICE JUMBER BUS HAIER THERMOCOOL HAIMA HAISE HAOJUE HAWTAI (HUATAI) HIAB PICK-UP HIAB PICK-UP HIAB PICK-UP HIAB PICK-UP HIAB PICK-UP HIAB PICK-UP HILUX HOLSTENE HONDA HONDA ACCENT HONDA ACCORD HONDA ALLA HONDA BULLET HONDA BULLET HONDA BULLET HONDA BULLET HONDA BULLET HONDA BULLET HONDA CIVIC HONDA CR-V HONDA CRV HONDA DEL SOL HONDA ELEMENT HONDA I. V TECH HONDA INTERGRA HONDA LEGEND HONDA ODYSSEY HONDA ODYSSEY SPACE BUS HONDA PILOT HONDA QUITENT HOUSTON TRUCK HOVAN BUS HOVER HOWO HUALI HUAYANG HUMMER HUMMER H2 HUNTER HUNTER CG 125 HWANGHAI HYAUNDA HYDUNAI JEEP HYUNDA HYUNDAI HYUNDAI ACCENT HYUNDAI ACCENT GLS HYUNDAI BUS HYUNDAI H100 HYUNDAI LANTRA HYUNDAI PICK UP HYUNDAI SANTAMO HYUNDAI SONATA HYUNDAI TUCSON INFINITI INFINITY INFINITY CAR INFINITY G20 INFINITY JEEP INFINITY Q45 INFINITY QX56 INTER TIPPER INTER TRUCK ISUZU ASCENDER ISUZU BULLION VAN ISUZU CAMPOCREW ISUZU P/UO ISUZU P/UP ISUZU TROOPER IVECO IVECO EUROSTAR TRACTOR HEAD IVECO FORD IVECO LORRY IVECO SEDDON ATKINSON TANKER IVECO TIPPER IVECO TRUCK IVEKO TRUCK IZUZU TROPPER JAC JAC BUS JAC LORRY JAC PICK UP JAC TRUCK JAGUAR JEEP JEEP PARTFINDER JETTA JETTA PASSAT JETTA SALOON JETTE SALOON JIN BEI BUS JINCHENG JMC JONWAY KAMA P/UP KIA KIA BUS KIA CAPITAL KIA CAR KIA KIA KIA LORRY K2700SC KIA OPIRUS KIA PICK UP KIA PREGIO KIA RIO KIA SEDONA KIA SPORT KIA SPORTAGE KINETIC WING KING LONG KINGCHENG KOREA KYMCO KYMCO MC LACROSSE LAMBORGHINI LANCIA LANCIA KAPPA LAND ROVER LAND ROVER FREELANDER LANDWIND LANTRA HYUNDAI LARADO JEEP LAREDO JEEP LAUREL LDB MINI BUS LDV 400 LDV CONVOLT LDV CONVOY LDV PILOT VAN LEXUS LEXUS LX 400 LEXUS RX 330 LEYLAND LEYLAND DAF 85 LEYLAND SCRAB LORRY LEYLAND TIPPER LIBERTY LIBERTY SPORT JEEP LICON ALOY LICON NAVIGATOR JEEP LIFT BACK LIFTAN LIFTAN MOTORCYCLE LINCOLN LINCON NAVIGATOR JEEP LITE ACE BUS LOTUS LOWBED M /BENZ 280 M BENZ TANKER M BENZ TRACTOR M/B M/B ML 430 M/BENS 430 M/BENS 500 M/BENZ 200T M/BENZ 508 BUS M/BENZ 100 D BUS M/BENZ 1114 M/BENZ 1314 M/BENZ 1414 M/BENZ 180 M/BENZ 190 M/BENZ 190 V/BOOT M/BENZ 200 TE M/BENZ 200 V.BOOT M/BENZ 208 BUS M/BENZ 208D M/BENZ 230 M/BENZ 240 M/BENZ 250 M/BENZ 260 M/BENZ 260E M/BENZ 300 M/BENZ 300 V/BOOTS M/BENZ 300T M/BENZ 408 M/BENZ 422 M/BENZ 601 M/BENZ 809 M/BENZ 914 M/BENZ C 180 M/BENZ C CLASS M/BENZ C200 M/BENZ C220 M/BENZ C230 M/BENZ C320 M/BENZ CONCORD M/BENZ DIESEL M/BENZ E-CLASS M/BENZ E220 M/BENZ E290 M/BENZ E320 M/BENZ E420 M/BENZ G500 M/BENZ JEEP M/BENZ LORRY M/BENZ MI 350/R M/BENZ ML 350 M/BENZ ML 430 M/BENZ ML300 M/BENZ ML320 M/BENZ R350 M/BENZ S/WAGON M/BENZ TANKER TRUCK M/BENZ TRUCK M/BENZ V BOOT M/BENZ V-BOOT M/BENZ VAKA 208 M/BENZ VAN M/BENZ VITO M/BENZ-PICKUP M/BENZE ML 430 MACK MACK CH612 MAGIRUS MAGIRUS MAGIRUS MAGIRUS MAGIRUS MAGIRUS MAGRUIS DEUTZ MAGRUS MAN MAN DIESEL MAN DIESEL TIPPER MAN TRUCK TIPPER MARCU VILL MARWA M/C MASERATI MASSEY FERGUSON U.K MATES 90 MAXIMA MAYBACH MAZ 626 MAZDA MAZDA PICK-UP MAZDA 2000 MAZDA 2200 BUS MAZDA 2600 MAZDA 322 MAZDA 323 MAZDA 323 MAZDA 323 MAZDA 323 MAZDA 323 MAZDA 323 MAZDA 323 F MAZDA 6 MAZDA 626 MAZDA 626 MAZDA 626 MAZDA 626 MAZDA 626 MAZDA 626 MAZDA B 3000 MAZDA B2200 MAZDA E20 BUS MAZDA E2200 MAZDA HILUX MAZDA MX-6 MAZDA VAN MAZDA XEDOS MAZDA XEDOS-6 MERCEDES BENZ E 420 MERCEDES- BENZ E 200 MERCEDES-BENZ MERCEDES-BENZ 190 MERCEDES-BENZ 200 MERCEDES-BENZ 200E MERCEDES-BENZ 230 MERCEDES-BENZ 230 E MERCEDES-BENZ 280SE MERCEDES-BENZ 300 V/BOOT MERCEDES-BENZ 310 MERCEDES-BENZ 520 MERCEDES-BENZ C 200 MERCEDES-BENZ ML500 MERCEDES-BENZ S 550 MERCEDES-BENZ V/BOOT MERCEDES-BENZ V/BOOT 200 MERCURY MERCURY TRANSPORTER MERCURY VILLAGE MI 350 JEEP MINI COOPER MISTUBA MIT BUS MIT CABSTER MIT CANTER MIT ECLIPSE MIT GALLANT MIT L200 PICK UP MIT OUTLANDER MIT PAJERO MIT SPACE BUS MIT VALLEY MIT VALLEY BUS MITS CANTER MITSUBISHI MITSUBISHI CANTER P/UP MITSUBISHI CANTER P/UP MITSUBISHI CANTER P/UP MITSUBISHI CANTER P/UP MITSUBISHI CANTER P/UP MITSUBISHI CANTER P/UP MITSUBISHI CARISMA MITSUBISHI COLT MITSUBISHI CORROLA MITSUBISHI GALLANT MITSUBISHI L200 MITSUBISHI LANCER MITSUBISHI MONTERO MITSUBISHI PAJERO MITSUBISHI SAPPORO MITSUBISHI SIGMA MITSUBISHI SPACE BUS MITSUBISHI SPACE GEAR MITSUBISHI SPACE RUNNER MITSUBISHI SPACEWAGON MITSUBISHI STANCER MITSUBUSHI MIRAGE MITSUBUSHI PICK UP Montero J MONTERO JEEP MORRIS MORRIS TIMBER MORTOR CYCLE MOTOR CYCLE MOTOR-CYCLE N/C123 NAC NANFANG NANFANG NANFANG NANFANG NANFANG NAPEP BAGGIO NIPPO SUPRA NIPPON SUPRA NISSAN NISSAN NISSAN 200LX NISSAN 200SX NISSAN ALTIMA NISSAN ALTIMA NISSAN ALTIMA NISSAN ALTIMA NISSAN ALTIMA NISSAN ALTIMA NISSAN ARMADA JEEP NISSAN ATLAS NISSAN BUS NISSAN BUS NISSAN BUS NISSAN BUS NISSAN BUS NISSAN BUS NISSAN CABSTAR PLUP NISSAN CAPSTAR NISSAN CARAVAN BUS NISSAN CELICA NISSAN CHERRY NISSAN CIVILIAN NISSAN COASTER NISSAN COMB NISSAN DATSUN NISSAN E20 NISSAN E20 BUS NISSAN FRONTER P/UP NISSAN FRONTIER NISSAN GLORIA NISSAN JEEP NISSAN LAUREL NISSAN LAUREL NISSAN LAUREL NISSAN LAUREL NISSAN LAUREL NISSAN LAUREL NISSAN MAIMA QX NISSAN MAXIINIA NISSAN MAXIMA NISSAN MAXIMA NISSAN MAXIMA NISSAN MAXIMA NISSAN MAXIMA NISSAN MAXIMA NISSAN MAXIMA QX NISSAN MICRA NISSAN MINI BUS NISSAN PATROL NISSAN PATROL NISSAN PATROL NISSAN PATROL NISSAN PATROL NISSAN PATROL NISSAN PICK UP NISSAN PRAIRE NISSAN PRAIRIE NISSAN PRARIE NISSAN PREMERA NISSAN PREMERA NISSAN PREMERA NISSAN PREMERA NISSAN PREMERA NISSAN PREMERA NISSAN PREMERA NISSAN PREMERA NISSAN PREMERA NISSAN PREMERA NISSAN PREMERA NISSAN PREMERA NISSAN PREMERA NISSAN PREMERA NISSAN PREMIRE NISSAN PREVIA NISSAN PRIMERA NISSAN PRIMERA NISSAN PRIMERA NISSAN PRIMERA NISSAN PRIMERA NISSAN PRIMERA NISSAN PRIMERA NISSAN PRIMERA NISSAN PRIMERA NISSAN PRIMERA NISSAN PRIMERA NISSAN PRIMERA NISSAN PRIMERA NISSAN PRIMERA NISSAN SANTRA NISSAN SANZA NISSAN SENROL NISSAN SENTRA NISSAN SERRENA NISSAN SPACE BUS NISSAN STANZA NISSAN STANZA NISSAN STANZA NISSAN STANZA NISSAN STANZA NISSAN STANZA NISSAN STARLET NISSAN SUNNY NISSAN SUNNY NISSAN SUNNY NISSAN SUNNY NISSAN SUNNY NISSAN SUNNY NISSAN SUNNY L/BACK NISSAN SUNNY LIFT BLACK NISSAN SUPER NISSAN TEANA NISSAN TERANO NISSAN TIIDA NISSAN TILDA NISSAN TRADE NISSAN UD NISSAN VANETTE NISSAN VANETTE NISSAN VANETTE NISSAN VANETTE NISSAN VANETTE NISSAN VANETTE NISSAN VILLAGER NIVA 1600 NX 125 NX 125 M/C OCTONIA OGT EXPERT VAN OJAKI OKA OPEL ASCONA OPEL ASTRA OPEL ASTRA OPEL ASTRA OPEL ASTRA OPEL ASTRA OPEL ASTRA OPEL CALIBRA OPEL CORSA OPEL CORSA 10 OPEL FRONTERA OPEL FROTERA OPEL KADET OPEL KADET OPEL KADET OPEL KADET OPEL KADET OPEL KADET OPEL KADET OPEL KADET OPEL KADET OPEL KADET OPEL KADET OPEL KADET S/W OPEL OMEGA OPEL OMEGA OPEL OMEGA OPEL OMEGA OPEL OMEGA OPEL OMEGA OPEL SANITRA OPEL SENATOR OPEL SERATO OPEL VANXHEULT OPEL VAVXHEULT OPEL VECTRA OPEL/ASTRA ORRON FORD OSAKI PACKARD PACKARD BUS PAJERO MITS PASSAT PASSAT VENTO PATHFINDER PE P/UP PEUGEOT PEUGEOT 104 PEUGEOT 306 PEUGEOT 307 PEUGEOT 309 PEUGEOT 404 PEUGEOT 405 PEUGEOT 406 PEUGEOT 504 PEUGEOT 504 PEUGEOT 504 PEUGEOT 504 PEUGEOT 504 PEUGEOT 504 PEUGEOT 504 SR PEUGEOT 505 PEUGEOT 505 PEUGEOT 505 PEUGEOT 505 PEUGEOT 505 PEUGEOT 505 PEUGEOT 605 PEUGEOT EXPERT PEUGEOT EXPERT PEUGEOT EXPERT PEUGEOT EXPERT PEUGEOT EXPERT PEUGEOT EXPERT PEUGEOT P/UP PEUGEOT TALBOT PGT EXPERS VAN PIAGIO APE 3 WHEELER PIAGIO APE 3 WHEELER PIAGIO APE 3 WHEELER PIAGIO APE 3 WHEELER PIAGIO APE 3 WHEELER PIAGIO APE 3 WHEELER PICANTO PICANTO KIA PICANTO S/CAR PICK UP PICK- UP VAN PLYMOUNT PLYMOUTH POLARSUN AUTOMOBILE POLARSUN BUS PONTIAC PORCHE PRADO JEEP PREMIER PREMIER PREMIER PREMIER PREMIER PREMIER PREMIER PREMIER PREMIER PREMIER PREMIER PREMIER PREMIER PREMIER PREVIA PRIMERAL S/C PRIZM GEO PRIZM LSI PRIZM S/CAR QINGQI QINGQI QINGQI QINGQI QINGQI QINGQI QINGQI INNOSON QINOI QOROS QUEST QUINGUN RANGE ROVER JEEP RANGE ROVER VOGUE RAV 4 RAV 4 RAV 4 RAV 4 RAV 4 RAV 4 REGAL REGALS RENAULT RENAULT BUS RENAULT ESPACE RENAULT LAGUNA LIFT BACK RENAULT MEGANE RENAULT RAPID RENAULT TRAFIC RENAULT TWINGO RENULT CLIO RIO RIO SC ROCK MOTORCYCLE ROEWE ROLLS-ROYCE ROVER ROVER 216 GST ROVER 618 ROVER 827 S ROVER 827SI SAAB SAIC MOTOR SALEEN SALOON WAGON SAMYANG SANGYONG REXTON JEEP SANTANA SATURN SAVANA BUS SCANIA LORRY SCANIA TRUCK SCION SEAT -TOLEDO SEAT -TOLEDO SEAT -TOLEDO SEAT -TOLEDO SEAT -TOLEDO SEAT -TOLEDO SEAT CAR SEDDON ATKINSON SEPHIA II SEPHIRE S/C SERENA 1 SHAANXI SHARPER CARAVAN SHUANGHUAN SICHUAN TENGZHONG SIENNA BUS SIGMA SIMBA SIMBA SIMBA SIMBA SIMBA SIMBA SIMBA TRICYCLE SINO SINOKI SUPRA SKODA SKODA OCTAVIA SMA SNOKY SIGMA SNOKY SIGMA SNOKY SIGMA SNOKY SIGMA SNOKY SIGMA SNOKY SIGMA SNUBA SORENTO SORENTO SORENTO SORENTO SORENTO SORENTO SOUEAST MOTORS / DONGNAN SSANG YOUNG REXTON STALLION STERLING SUNNY STERY STEYER STEYR TRUCK STUDEBAKER STYER TIPPER SUBARU SUBARU FORESTER SUBARU LEGACY SUBARU PROTON SUNNY SUPERBIKE SUPRA SUPRA SUPRA SUPRA SUPRA SUPRA SUSAN SUZI MIDI SUZIKI SWIFT SUZUKI SUZUKI AH 14E SUZUKI BALENO S/C SUZUKI ESCUDO SUZUKI GRAND VITARA SUZUKI RODEO SUZUKI SIDE KICK SUZUKI VICTARA JEEP SUZUKI VITARA T.V.S. TACOMA TATA BUS TATA P/UP TATA SUMO JEEP TATA TRUCK TATA VAN TEC 123 M/C TEC MC TEC SPEED TEC SPEED I TEE CYCLE TELECOLINE TERANO TERANO JEEP TERRACAN THOMAS FLAT FACE THOMAS REGULAR TIANMA TIMBER LORRY TINK TIPPER TRAILER TITAN AX100 TOHEDO TONGTIAN TOTOTA TOYOTA TOYOTA LE TOYOTA 4 RUNNER TOYOTA 4 RUNNER JEEP TOYOTA ARISTO TOYOTA AVANZA TOYOTA AVENSUS TOYOTA CAMRY TOYOTA CAMRY LE TOYOTA CAMRY S/C TOYOTA CANTER TOYOTA CARINA 11 TOYOTA CARINA 11 L/BACK TOYOTA CARINA II TOYOTA CARINA S/CAR TOYOTA CARMRY TOYOTA CELICA TOYOTA COASTER TOYOTA CONDOR TOYOTA COPERATION S/CAR TOYOTA COROLLA L/BACK TOYOTA CORROLA TOYOTA CORROLA TOYOTA CORROLA TOYOTA CORROLA TOYOTA CORROLA TOYOTA CORROLA TOYOTA CRESSIDA TOYOTA D/CUBIU TOYOTA DYME P/UP TOYOTA DYNA 200 PICK UP TOYOTA DYNA P/UP TOYOTA HILUS TOYOTA HILUS BUS TOYOTA HILUX TOYOTA HILUX BUS TOYOTA LAND CRUISER TOYOTA LEXUS TOYOTA LEXUS LS 470 TOYOTA LIMITED JEEP TOYOTA LIYEACE TOYOTA MATRIX TOYOTA MICRA TOYOTA MINI BUS TOYOTA NISSAN TOYOTA PANEL TOYOTA PANEL VAN TOYOTA PICK UP TOYOTA PRADO TOYOTA PREDO JEEP TOYOTA PREVIA TOYOTA SAFE TOYOTA SEQUOIA TOYOTA SEQVA TOYOTA SERENA TOYOTA SOLARA CAMRY TOYOTA STARLET TOYOTA T100 TOYOTA T100 P/UP TOYOTA TERCEL TOYOTA TOWN ACE TOYOTA TOYOACE TOYOTA VANNETTE TRACTOR TRANSIT BUS TRIUMPH TROOPER JEEP TUYOTA TVR UNDER ACCORD UNDER ACCORD UNDER ACCORD UNDER ACCORD UNDER ACCORD UNDER ACCORD UNIT L200 V WAGEN AUDI V-WAGEN SATANNA V/GOLF V/W BEETLE V/W COMODO V/W EUROVAN V/W GOLF V/W PARSSENT V/W PARSSENTS V/W PASSAT V/W SURAN V/W T4 TRANSPORTER BUS V/W TOUARAG V/WAGEN 80 V/WAGEN AMAZON V/WAGEN AUDI V/WAGEN BETTLE V/WAGEN BORA V/WAGEN L 48 V/WAGEN LT 28 BUS V/WAGEN LT 28 P/UP V/WAGEN LT 31 V/WAGEN LT 40 V/WAGEN LT 45 V/WAGEN MINITRUCK V/WAGEN P/UP V/WAGEN PASSAT V/WAGEN POLO V/WAGEN SHAROON V/WAGEN TOUAREG JEEP V/WAGEN TRANSPORTER V/WAGEN VERAGON V/WAGON VAN DAF VANXALL ASTRAL VANXHALL MIDI VAUXHALL VAUXHALL ESTATE VECTRA VENTRO VENUS VILLAGER MERCURY VOLKS WAGEN LT35 VOLKS WAGEN PASSAT VOLKS WAGEN POLO VOLKSWAGEN VOLKSWAGEN BUS VOLKSWAGEN JETTA VOLKSWAGEN SCHIROCO VOLKSWAGEN TRANSPORTER VOLKSWAGOON VOLKWAGEN BEATLE VOLVO 200 VOLVO 240 VOLVO 240 DL VOLVO 240 GL VOLVO 244 VOLVO 245 VOLVO 440 VOLVO 580 VOLVO 740 VOLVO 746 VOLVO 940 S/CAR VOLVO 960 S/C VOLVO B10M70 VOLVO BOX VOLVO BUS VOLVO LORRY FI6 VOLVO S/ WAGON VOLVO S60 VOLVO S70 VOLVO S80 VOLVO TRUCK VOLVO V40 VOLVO V70XCS/WAGON WAGON WALL DEER WALL DEER WALL DEER WALL DEER WALL DEER WALL DEER WANHU KARY GO WINDSTAR FORD X`S XIN KIA YINHE AX100 YOKOMATO YUGO YUTONG ZHONGYU ZONG CHING ZONGMACE ZONGZHEN ZONGZHEN ZONGZHEN ZONGZHEN ZONGZHEN ZONGZHEN ZOTYE ZOTYE NOMAD ZXAUTO . 121 3SERIES ALFA ROMEO AUDI 100 AUDI 90 AURORA JEEP AUSTIN TIPPER B M W 528 BENZ BOXER BM100 BOXERS BRICKLIN BUSTER BUS CERATO 1.61 S/C CHANA CHEROKEE JEEP CHEROOKE CHERRY CHEVROLET AVEO CHEVROLET TRUCK CHRYSLER VOYAGER CUSTOM CY 90 SPOKE DAEWO LEGANZA DAF 1700 DAKOTA DODGE DATSUN 120Y DATSUN 160B DATSUN 180B S/W DATSUN BUS DATSUN NISSAN DATSUN PANEL VAN DATSUN PANEL VAN DATSUN PICK UP DAYANG DAYLONG DE-DAMAK DEER 4X2 SINGLE CABIN P/UP DODGE DODGE NEON DUKAR 3 WHEELER DUKAR 3WHEELERS DYNA EXPERT VAN ORDINA FAKA MERCEDES FENCO FIAT CROMA FOED CLUB FORD CLUBWAGEN FORD COUGAR FORD GALAXY FORD JEEP FORD SIERRA FORD SIERRA FORD SIERRA FORD SIERRA FORD SIERRA FORD SIERRA FORLAND FOTON FREDERICH FREIGHTLINER GEELY MK GLS GLS JEEP GMC JEEP GMC P/UP GOLF GOLF GOLF GOLF GOLF GOLF GOLF 1 GOLF 11 GOLF P/UP GOLF P/UP GOLF P/UP GOLF P/UP GOLF P/UP GOLF P/UP GOLF PICK-UP GOLF PICK-UP GOLF PICK-UP GOLF PICK-UP GOLF PICK-UP GOLF PICK-UP GREAT WALL HONDA ACURA HONDA CGL 125 HONDA CITY HONDA CIVIC SHUTTLE HONDA CONCERTO HONDA CONSERTO HONDA CRX HONDA DIANG HONDA HALLA HONDA INTEGRA HONDA ODDYSSEY HONDA ODDYSSEY HONDA ODDYSSEY HONDA ODDYSSEY HONDA ODDYSSEY HONDA ODDYSSEY HONDA PASSPORT HONDA PRELUDE HONDA SUPER HONGQI HYUNDAI EXCEL HYUNDAI TERRACAN INDIGO GLX INNOSON ISUZU ISUZU BEDFORD BUS ISUZU BUS ISUZU JEEP JINSSUN KANCHEN KANCHEN KANCHEN KANCHEN KANCHEN KANCHEN KARIGO TRICYCLE KASEA KASEA KASEA KASEA KASEA KASEA KASEA KASEA KASEA KASEA KASEA KASEA KASEA KASEA KIA OPTIMA KIA OPTIMC KIA PICANTO KIA SHUMA 11 LADA NIVA LAND MASTER LIFAN LONCIN LORRY M/BENZ M/BENZ S500 M/BENZ C240 M/BENZ E 200 M/BENZ E200 M/BENZ S550 M/BENZ V/BOOT M/BENZ V/BOOT 230 M/BENZE ML430 M/DIESEL TRUCK MAC CHUKS MALIBU CHEVROLET MAN TIPPER MAN TIPPER MAN TIPPER MAN TIPPER MAN TIPPER MAN TIPPER MAN TRUCK MARCHETTI TERRIN MASOBI MAZDA BUS MAZDA CABIN MAZDA CABIN P/UP MAZDA XEDO 36 MERCEDE 1017 TRUCK MERCEDES 1017 TUCK MERCEDES TRUCK MERCEDES- BENZ 280 MERCEDES-BENZ 230T MERCEDES-BENZ 300 MERCEDES-BENZ 320 MERCEDES-BENZ C220 MERCEDES-BENZ E230 MERCEDES-BENZ E320 MERCURY COMM VAN MIT LANCER MIT LANCER MIT LANCER MIT LANCER MIT LANCER MIT LANCER MIT SPACE RUNNER MIT TREDIA MITS P/UP MITSUBISHI BUS MITSUBISHI ECLIPSE MITSUBISHI L200 PICK-UP VAN MITSUBISHI L300 MITSUBISHI MINI BUS ML 320 JEEP NANJING SOYAT NISSAN ALMERA NISSAN B/BIRD NISSAN B/BIRD NISSAN B/BIRD NISSAN B/BIRD NISSAN B/BIRD NISSAN B/BIRD NISSAN CABIN NISSAN CABSTAR NISSAN CABSTAR NISSAN CABSTAR NISSAN CABSTAR NISSAN CABSTAR NISSAN CABSTAR NISSAN CABSTAR P/UP NISSAN CARBALL NISSAN CENTRAL NISSAN DIESEL NISSAN INFINITY NISSAN MAXINIA NISSAN MURANO NISSAN PATHFINDER NISSAN PATHFINDER NISSAN PATHFINDER NISSAN PATHFINDER NISSAN PATHFINDER NISSAN PATHFINDER NISSAN PATHFINDER NISSAN PATHFINDER NISSAN PATHFINDER NISSAN PATHFINDER NISSAN PATHFINDER NISSAN PATHFINDER NISSAN PATHFINDER NISSAN PATHFINDER NISSAN QUEST NISSAN QUEST NISSAN QUEST NISSAN QUEST NISSAN QUEST NISSAN QUEST NISSAN QUEST NISSAN QUEST NISSAN QUEST NISSAN QUEST NISSAN QUEST NISSAN QUEST NISSAN QUEST NISSAN QUEST NISSAN SECTRA NISSAN SERENA NISSAN XTERRA OLDSMOBILE OPEL OTHERS PASSAT V/WAGEN PEUGEOT 305 PGT 504 PGT 505 PRIMERA QLINK RENAULT LAGUNA ROVEER 75 ROVER 620 SCODA SCODA OCTAVIA SEAT TOLEDO SHACMAN SHARKMAN SIMBA KARY-GO SINOKI SKYGO SPEED11 TEC STEYR STYRE SUZIKI SUPRA SUZIKI SUPRA SUZIKI SUPRA SUZIKI SUPRA SUZIKI SUPRA SUZIKI SUPRA SUZUKI HAOJUE TEC MOTORCYCLE TEC SPEED TEREX TOYOTA BUS TOYOTA AVALON TOYOTA AVENSIS TOYOTA CABSTER TOYOTA CARINA TOYOTA CARINA E TOYOTA COROLLA TOYOTA DYNA TOYOTA HIACE TOYOTA HYLANDER TOYOTA LITEACE TOYOTA PICKUP TOYOTA PICNIC TOYOTA RAV 4 TOYOTA RAV 4 TOYOTA RAV 4 TOYOTA RAV 4 TOYOTA RAV 4 TOYOTA RAV 4 TOYOTA SEQUA TOYOTA SIENNA TOYOTA SIENNA TOYOTA SIENNA TOYOTA SIENNA TOYOTA SIENNA TOYOTA SIENNA TOYOTA SOLARA TOYOTA SUPRA TOYOTA VENTURE TRONG TRUCK V/BOOT V/WAGEN BUS (MINI) V/WAGEN SANTANA VESPER VILLAGER VOLKSWAGEN GOLF VOLKWAGEN PARATI HL VOLVO VOLVO 240 GLT VOLVO 460 VOLVO 760 VOLVO 850 XINKAI XTERRA YAMAHA YAMAHA YAMAHA YAMAHA YAMAHA YAMAHA YAMAHA YAMAHA YAMAHA YAMAHA YOUNGMAN ZHONEGYU\n\nOthers\n\nColor\n\nFuel Type -- Select Fuel Type -- Petrol Diesel Bio-Fuel Others\n\nYear of Manufacture -- Select Year Manufacture -- 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027\n\nModel\n\nEngine Number\n\nPolicy Number\n\nVehicle Type/Group -- Select Vehicle Type -- Bus Crane Motor cycle Pick up Salon SUV Tractor Tricycle Truck Van Wagon\n\nChassis No\n\nEngine Capacity -- Select Engine Type -- Above 3.0 Below 1.6 Between 1.6 and 2.0 Between 2.1 and 3.0\n\nTank Capacity\n\nOdometer\n\n#### Owner Information\n\nOwner Identification\n\n-- Select Owner Type -- Company RC Number Driver's License International Passport National ID Card Tax Identification Number\n\nFirst Name\n\nLast Name\n\nCompany Name\n\nIdentification No\n\nEmail Address\n\nAddress\n\nCity\n\nMobile Number\n\nState -- Select State -- ABIA ADAMAWA AKWA IBOM ANAMBRA BAUCHI BAYELSA BENUE BORNO CROSS RIVER DELTA EBONYI EDO EKITI ENUGU FCT FEDERAL GOVERNMENT GOMBE IMO JIGAWA KADUNA KANO KATSINA KEBBI KOGI KWARA LAGOS NASSARAWA NIGER OGUN ONDO OSUN OYO PLATEAU RIVERS SOKOTO TARABA YOBE ZAMFARA\n\nLocal Government -- Select State -- ABA NORTH ABA SOUTH AROCHUKWU BENDE IKWUANO ISIALA NGWA NORTH ISIALA NGWA SOUTH ISUIKWUATO UMUNNEOCHI OBIOMA NGWA OHAFIA OSISIOMA UGWUNAGBO UKWA EAST UKWA WEST UMUAHIA NORTH UMUAHIA SOUTH DEMSA FUFORE GANYE GIREI GOMBI GUYUK HONG JADA YOLA NORTH LAMURDE MADAGALI MAIHA MAYO-BELWA MICHIKA MUBI MUBI SOUTH NUMAN SHELLENG SONG TOUNGO YOLA SOUTH ABAK EASTERN OBOLO EKET EKPE-ATAI ESSIEN UDIM ETIM EKPO ETINAN IBENO IBESIKPO ASUTAN IBIONO IBOM IKA IKONO IKOT ABASI IKOT EKPENE INI ITU MBO MKPAT ENIN NSIT IBOM NSIT UBIUM OBAT AKARA OKOBO ONNA ORON ORUK ANAM UNDUNG UKO UKANAFUN UQUO-IBENO URUAN URUE OFFONG/ORUKO UYO AGUATA ANAMBRA EAST ANAMBRA WEST AWKA NORTH AWKA SOUTH AGHAMELUM DUNUKOFIA EKWUSIGO IDEMILI NORTH IDEMILI SOUTH IHIALA NJIKOKA NNEWI NORTH NNEWI SOUTH OGBARU ONITSHA NORTH ONITSHA SOUTH ORUMBA NORTH ORUMBA SOUTH OYI ALKALERI BAUCHI BOGORO DAMBAM DARAZO DASS GAMAWA GANJUWA GIADE ITAS/GADAU JAMA'ARE KATAGUM KIRFI MISAU NINGI SHIRA TAFAWA-BALEWA TORO WARJI ZAKI BRASS EKEREMOR KOLOKUMA/OPOKUMA NEMBE OGBIA SAGBAMA SOUTHERN IJAW YENAGOA ADO AGATU APA BUKURU GBOKO GUMA GWER GWER WEST KATSINA-ALA KONSHISHA KWANDE LOGO MAKURDI OBI OGBADIBO OHIMINI OJU OKPOKWU OTUKPO TARKA UKUM USHONGO VANDEIKYA ABADAM ASKIRA UBA BAMA BAYO BIU CHIBOK DAMBOA DIKWA GUBIO GUZAMALA GWOZA HAWUL JERE KAGA KALA/BALGE KONDUGA KUKAWA KWAYA MAFA MAGUMERI MAIDUGURI MARTE MOBBAR MONGUNO NGALA NGANZAI SHANI ABI AKAMKPA AKPABUYO BAKASSI BEKWARA BIASE BOKI CALABAR MUNICIPAL CALABAR SOUTH ETUNG IKOM OBANLIKU OBUBRA OBUDU ODUKPANI OGOJA YAKURR YALA ANIOCHA NORTH ANIOCHA SOUTH BOMADI BURUTU ETHIOPE EAST ETHIOPE WEST IKA NORTH IKA NORTH EAST ISOKO ISOKO SOUTH NDOKWA EAST NDOKWA WEST OKPE OSHIMILI NORTH OSHIMILI SOUTH PATANI SAPELE UDU UGHELLI NORTH UGHELLI SOUTH UKWUANI UVWIE WARRI EAST WARRI NORTH WARRI SOUTH-WEST ABAKALIKI AFIKPO NORTH AFIKPO SOUTH EBONYI EZZA NORTH EZZA SOUTH IKWO ISHIELU IVO IZZI OHAOZARA OHAUKWU ONICHA AKOKO EDO EGOR ESAN CENTRAL ESAN NORTH EAST ESAN SOUTH EAST ESAN WEST ETSAKO CENTRAL ETSAKO EAST ETSAKO WEST IGUEBEN IKPOBA OKHA OREDO ORHIONWON OVIA NORTH EAST OVIA SOUTH WEST OWAN EAST OWAN WEST UHUNMWONDE ADO-EKITI GBONYIN EFON-ALAAYE EKITI EAST EKITI SOUTH WEST EKITI WEST EMURE IDO-OSI IJERO IKERE IKOLE ILEJE MEJI IREPODUN/IFELODUN ISE ORUN MOBA OYE AWGU ANINRI ENUGU EAST ENUGU NORTH ENUGU SOUTH EZEAGU IGBO ETITI IGBO EZE NORTH IGBO EZE SOUTH ISI UZO NKANU NKANU EAST NSUKKA OJI RIVER UDENU UDI UZO UWANI AKKO BALANGA BILLIRI DUKKU FUNAKAYE GOMBE KALTUNGO KWANI NAFADA SHOMGOM YAMALTU/DEBA ABOH-NBAISE AHIAZU MBAISE EHIME MBANO EZINIHITE-MBAISE IDEATO NORTH IDEATO SOUTH IHITTE UBOMA IKEDURU ISIALA MBANO ISU MBAITOLI NGOR-OKPUALA NJABA NWANGELE NKWERRE OBOWO OGUTA OHAJI/EGBEMA OKIGWE ONUIMO ORLU ORSU ORU EAST ORU WEST OWERRI OWERRI NORTH OWERRI WEST AUYO BABURA BIRNIN KUDU BIRNIWA GAGARAWA BUJI DUTSE GARKI GUMEL GURI GWARAM GWIWA HADEJIA JAHUN KAFIN HAUSA KAUGAMA KAZAURE KIRI KASAMMA KIYAWA MAIGATARI MALAM MADORI MIGA RINGIM RONI SULE TAKARKAR TAURA YANKWASHI BIRNIN GWARI CHIKUN GIWA KAJURU IGABI IKARA JABA JEMA'A KACHIA KADUNA NORTH KADUNA SOUTH KAGARKO KAURU KUBAU KUDAN LERE MAKARFI SABON GARI SANGA SOBA ZANGON KATAF ZARIA AJINGI ALBASU BAGWAI BEBEJI BICHI BUNKURE DALA DAMBATTA DAWAKIN KUDU DAWAKIN TOFA DOGUWA FAGGE GABASAWA GARKO GARUN MALLAM GAYA GEZAWA GWALE GWARZO KABO KANO MUNICIPAL KARAYE KIBIYA KIRU KUMBOTSO KUNCHI KURA MADOBI MAKODA MINJIBIR NASARAWA RANO RIMIN GADO ROGO SHANONO SUMAILA TAKAI TARAUNI TOFA TSANYAWA TUDUN WADA UNGOGO WARAWA WUDIL BAKORI BATAGARAWA BATSARI BAURE BINDAWA CHARANCHI DAN-MUSA DANDUME DANJA DAURA DUTSI DUTSIN MA FASKARI FUNTUA INGAWA JIBIA KAFUR KAITA KANKARA KANKIA KATSINA KURFI KUSADA MAI'ADUWA MALUMFASHI MANI MASHI MATAZU MUSAWA RIMI SABUWA SAFANA SANAMU ZANGO ALIERO AREWA DANDI ARGUNGU AUGIE BAGUDO BIRNIN -KEBBI BUNZA DANDI WASAGU/DANKO PAKAL GWANDU JEGA KALGO KOKO/BESSE MAIYAMA NGASKI SAKABA SHANGA SURU YAURI ZURU ADAVI AJAOKUTA ANKPA BASSA DEKINA IBAJI IDAH IGALA MELA IJUMU KABBA/BUNU LOKOJA/KOGI KOTONKARFE/KOGI MOPA-MURO OFU OGORI/MAGONGO OKEHI OKENE OLAMABORO OMALA YAGBA EAST YAGBA WEST ASA BARUTEN EDU EKITI IFELODUN ILORIN EAST ILORIN SOUTH ILORIN WEST IREPODUN KAIAMA MORO OFFA OKE-ERO OSIN OYUN PATEGI AGEGE AJEROMI/IFELODUN ALIMOSHO AMUWO-ODOFIN APAPA BADAGRY EPE ETI-OSA IBEJU-LEKKI IFAKO/IJAYE IKEJA IKORODU KOSOFE LAGOS ISLAND LAGOS MAINLAND MUSHIN OJO OSHODI/ISOLO SOMOLU SURULERE AKWANGA AWE DOMA KARU KEANA KEFFI KOKONA LAFIA NASARAWA NASARAWA-EGGON OBI TOTO WAMBA AGAIE AGWARA BIDA BORGU BOSSO EDATI GBAKO GURARA KATCHA KONTAGORA LAPAI LAVUN MGAMA MARIGA MASHEGU CHANCHAGA MOKWA MUYA PAIKORO RAFI RIJAU SHIRORO SULEJA TAFA WUSHISHI OGUN WATERSIDE ADO ODO/OTA ABEOKUTA SOUTH ABEOKUTA NORTH ILUGUN ALARO EGBADO NORTH IFO IJEBU NORTH IJEBU-ODE IKENNE EGBADO SOUTH IMEKO AFON IPOKIA IDARAPO EWEKORO OBAFEMI-OWODE ODEDA ODOGBOLU IJEBU EAST SHAGAMU AKOKO NORTH EAST AKOKO NORTH WEST AKOKO SOUTH WEST AKOKO SOUTH EAST AKURE NORTH AKURE IDANRE IFEDORE OKITIPUPA ILAJE ESE ODO ILE-OLUJI/OKEIGBO IRELE ODIGBO ONDO ONDO EAST OSE OWO ATAKUMOSA EAST ATAKUMOSA WEST AYEDAADE AYEDIRE BOLAWADURO BORIPE EDE NORTH EDE EGBEDORE EJIGBO IFE CENTRAL IFE EAST IFE NORTH IFE SOUTH IFEDAYO IFELODUN ILA ILESA EAST ILESA WEST IREPODUN IREWOLE ISOKAN IWO OBOKUM ODO-OTIN OLA-OLUWA OLORUNDA ORIADE OROLU OSHOGBO AFIJIO AKINYELE ATIBA ATIGBO EGBEDA IBADAN NORTH EAST IBADAN NORTH IBADAN NORTH WEST IBADAN SOUTH WEST IBADAN SOUTH EAST IBARAPA IBARAPA IDDO IFEDAPO IFELOJU IREPO ISEYIN ITESIWAJU IWAJOWA IYAMAPO/OLORUNSOGO KAJOLA LAGELU OGBOMOSHO NORTH OGBOMOSHO SOUTH OGO OLUWA OLUYOLE ONA ARA ORELOPE ORIRE OYO OYO WEST SAKI EAST SURULERE BARAKIN LADI BASSA BOKKOS JOS EAST JOS NORTH JOS SOUTH KANAM KANKE LANGTANG NORTH LANGTANG SOUTH MANGU MIKANG PANKSHIN QUA'AN-PAN RIYOM SHENDAM WASE AHOADA EAST AHOADA WEST AKUKU-TORU ANDONI ASARI-TORU BONNY DEGEMA ELEME EMUOHA ETCHE GOKANA IKWERRE KHANA OBIO AKPOR ABUA/ODUAL OGBA/EGBEMA OGU/BOLO OKRIKA OMUMMA OPOBO/NKORO OYIGBO PORT HARCOURT TAI BINJI BODINGA DANGE SHUNI GADA GORONYO GUDU GWADABAWA ILLELA ISA KEBBE KWARE RABAH SABON-BIRNIN SHAGARI SILAME SOKOTO NORTH SOKOTO SOUTH TAMBAWAL TANGAZA TURETA WAMAKKO WURNO YABO ARDO BALI DONGA GASHAKA GASSOL IBI JALINGO KARIM-LAMIDO KURMI LAU SARDAUNA TAKUM USSA WUKARI YORRO ZING BADE BURSARI DAMATURU FIKA FUNE GEIDAM GUJBA GULANI JAKUSKO KARASUWA MACHINA NANGERE NGURU POTISKUM TARMUA YUNUSARI YUSUFARI ANKA BAKURA BUKKUYUM BUNGUDU GUMMI GUSAU KAURA-NAMODA BIRNIN MAGAJI MARADUN MARU SHINKAFI TALATA MAFARA TSAFE ZURMI ABAJI MUNICIPAL BWARI GWAGWALADA KUJE KWALI ANAUCHA RUBOCHI KARSHI YABA DIOBU GUDUMBALI KAURA\n\n#### Other Information\n\nDrivers License Number\n\nLicense Bearer Name\n\nState Of Plate Number Allocation -- Select State -- ABIA ADAMAWA AKWA IBOM ANAMBRA BAUCHI BAYELSA BENUE BORNO CROSS RIVER DELTA EBONYI EDO EKITI ENUGU FCT FEDERAL GOVERNMENT GOMBE IMO JIGAWA KADUNA KANO KATSINA KEBBI KOGI KWARA LAGOS NASSARAWA NIGER OGUN ONDO OSUN OYO PLATEAU 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  },
  {
    "url": "https://newtelegraphng.com/nigerians-spent-n1-05trn-on-used-vehicles-import-in-2024-us-customs/",
    "text": "Nigerians Spent N1.05trn On Used Vehicles' Import In 2024 – US Customs - New Telegraph\n\n# Nigerians Spent N1.05trn On Used Vehicles’ Import In 2024 – US Customs\n\n- Bayo Akomolafe\n- April 3, 2025\n- 4 minute read\n\nWeak naira exchange to dollar and high tariffs in the seaport has further pushed cost of used vehicle imports from United States to N1.05 trillion ($675.48 million) in one year. Also, imports of vehicles to the country was $22.3 million in January 2025.\n\nThe United States exported 83 per cent of the total imports in 2024 as National Bureau of Statistics (NBS) put the total amount spent on importation at N1.26 trillion last year. Findings from United States Customs and Border Protection (CBP) revealed that motor cars and vehicles for transporting persons was 80 per cent or $537.50 million of the imports from United States, while parts and accessories of the motor vehicles was 18.15 per cent or $122.62 million in 2024.\n\nAlso, motor vehicles for transport of goods was 0.87 per cent or $5.92 million; tractors, 0.60 per cent or $4.08 million; trailers and semi-trailers and other vehicles not mechanically propelled, 0.33 per cent or $2.24 million; special purpose motor vehicles not specified elsewhere, 0.32 per cent or $2.17 million and works trucks, selfpropelled tractors of railway station platform type, 0.22 per cent or $1.55 million.\n\nAs foreign exchange crisis is driving up the cost of imports, shipments of used vehicles have dropped to 4,818 units in the first three months of 2025. However, data by Nigerian Ports Authority (NPA) also revealed that about 2,250 units of used vehicles were ferried to the port in March 2025.\n\nThe data explained that at the Port and Terminal Multiservices Limited (PTML) in Tincan Island, Great Abidjan discharged 500 units; Great Lagos, 500 units; Repubblica del Brasile, 350 units; Grande Lagos, 500 units and Grande Cotonou, 400 units. Also, in February 2025 PTML, two vessels called to offload 1,000 units as Great Antwerp ferried in 500 units and Great Casablanca, 500 units as 1,560 units were offloaded at PTML and Five Stars in January.\n\nThe data noted that Sunrise Ace brought 460 units, while Glovis Spirit brought 350 units to Five Stars Logistics. Also, at PTML, Repubblica Del Brasile discharged 350 units, while Grande Argentina and Grande Cotonou offloaded 400 units each.\n\nIt learnt that decline in the volume of used vehicles importation into Nigeria was attributed to high import duty and taxes for used vehicles, imposition of import levy on used vehicles, restriction of rebate on ex-factory prices used for assessment of import duty to 10 years whereas the law allows importation of 12-year old vehicles.\n\nPresently, vehicle importers are paying one per cent Comprehensive Import Supervision Scheme (CISS) fee, 15 per cent National Automotive Council (NAC) levy on used vehicles, Nigerian Automotive Industry Development Plan (NAIDP) 35 per cent levy on automobile imports, 35 per cent tariff, making a total duty of 70 per cent.\n\nRecall that the General Manager of PTML, Mr Tunde Keshinro had explained that vehicles above 10 years of age were forced to pay higher import duties and high exchange rates, leading to high landing costs above the affordable level for the majority of Nigerians, who depend on private vehicles for private and commercial transportation.\n\nAlso, a former Acting President of the Association of Nigerian Licensed Customs Agents (ANLCA), Dr. Kayode Farinto, stressed the need for the abolition of the one per cent CISS charge, noting that it had become redundant since the government had introduced other fees to cover destination inspection costs. He entertained the fear whether the government was deliberately trying to discourage Nigerians from importing used vehicles.\n\nAccording to him, removing the NAC levy and replacing it with the new customs charge would ease the financial burden on importers while still generating revenue for the government. In 2024, few passenger cars were shipped to the ports as due to the inflation and naira depreciation as naira was trading at N1,660/ dollar, while official exchange rate stood at N1,535/dollar.\n\nThe latest data from the foreign trade report of the National Bureau of Statistics showed that the total value of passenger car imports fell by 14.3 per cent to N1.26 trillion in 2024 from N1.47 trillion recorded in 2023, just as import was N655.69 billion in 2022; N695.40 billion in 2021 and N546.79 billion in 2020.\n\nPlease follow and like us:"
  },
  {
    "url": "https://naddc.gov.ng/wp-content/uploads/2023/06/Nigerian-Automotive-Industry-Development-Plan-2023.pdf",
    "text": "Nigerian Automotive Industry Development Plan 1\nNigerian \nAutomotive\nIndustry \nDevelopment \nPlan\nMay 2023\nNigerian Automotive Industry Development Plan 2\nContent\nGlossary\nA Statement from the Honorable Minister\nForeword\n1. Introduction\n2. Global automotive industry review\n2.1. Overview\n2.2. Global Automotive Market\n2.3 . Future global trends\n2.4. Emerging Trends and Africa\n2.5. Key Takeaways\n3. Nigerian Automotive Industry Review\n3.1. Brief History of the Automotive Industry in Nigeria\n3.2. Current Status of the Industry and the NAIDP 2014\n4. Establishing a vision and associated objectives for the Nigerian \n auto industry\n4.1. Industry vision\n4.2. Key industry development objectives by 2033\n4.2.1. Growth of vehicle production to 200,000 units\n4.2.2. Transition from SKD to CKD\n4.2.3. Increase local content of assembled vehicles to 40%\n4.2.4. Increase in employment in the automotive value chain\n4.2.5. Attain Electric Vehicle Production of 30% of local production\n4.3. Duration and Review of the NAIDP 2023\n5. Master Plan - Strategic pillars & enablers\n5.1. Strategic Pillars\n5.1.1. Investment promotion & Fiscal Incentives\n5.1.2. Local Auto-Component Capacity Building\n5.1.3. Market Expansion & Trade Facilitation\n5.1.4. Cost Competitiveness Promotion\n5.1.5. Skills Acquisition & Development\n5.1.6. Technology Development & Innovation\n5.1.7. Standards & Safety Enforcement\n5.2. Enablers\n5.2.1. Implementation & Governance Framework\n5.2.2. Enable Sector linkages\n5.2.3. Sector specific funding\n6. References\n7. Appendix\n03 \n05\n06\n10\n13\n14\n15\n18\n20\n21\n22\n23\n23\n28\n29\n29\n30\n30\n30\n30\n30\n31\n32\n33\n33\n38\n40\n43\n44\n46\n47\n48\n48\n49\n50\n52\n55\nNigerian Automotive Industry Development Plan 3\nGlossary\nAAAM African Association of Automotive Manufacturers\nADAS Advanced Driving Assistance Systems\nAI Artificial Intelligence\nAfCFTA African Continental Free Trade Area \nBEV Battery Electric Vehicles \nCAGR Compound Annual Growth Rate\nCBN Central Bank of Nigeria\nCBU Completely Built Up\nCET Common External Tariff\nCIF Cost, Insurance, and Freight\nCKD Completely-Knocked-Down\nCOP26 Conference of the Parties\nCVs Commercial vehicles \nDKD Disassembled Knocked Down\nDPD Direct Port Delivery \nECOWAS Economic Community of West African States\nETLS ECOWAS Trade Liberalization Scheme\nEVs Electric Vehicles\nFBU Fully Built Unit\nFIRS Federal Inland Revenue Service \nFMITI Federal Ministry of Industry, Trade, and Investment\nFRSC Federal Road Safety Corps (Nigeria)\nGDP Gross Domestic Product\nNigerian Automotive Industry Development Plan 4\nICE Internal Combustion Engine\nID Import Duty \nIL Import Levy\nIoT Internet of Things\nMAN Manufacturers Association of Nigeria \nNADDC Nigerian Automotive Design and Development Council\nNAC National Automotive Council \nNAIDP Nigerian Automotive Industry Development Plan\nNCS Nigeria Custom Service \nNESREA National Environmental Standards and Regulations Enforcement Agency\nNIPC Nigerian Investment Promotion Commission \nNIRP Nigerian Industrial Revolution Plan\nOEM Original Equipment Manufacturer\nOICA Organisation Internationale de Constructeurs d’Automobiles\nPPP Public Private Partnership \nR&D Research and Development\nSKD Semi-Knocked-Down\nSON Standards Organisation of Nigeria\nSUV Sport Utility Vehicle \nTRIMS Trade Related Investment Measures \nVAT Value Added Tax\nWACIP West Africa Common Industrial Policy \nGlossary\nNigerian Automotive Industry Development Plan 5\nState\nment\nA Statement from the Honorable Minister\nOtunba Adeniyi \nAdebayo, CON\nHonorable Minister, \nFederal Ministry of Industry, \nTrade and Investment\nOur Esteemed Investors and Stakeholders,\nIt gives me great pleasure to present the revised \nNigerian Automotive Industry Development Plan \n(NAIDP), an initiative which was originally launched \nin 2014. This underscores the commitment of \nthe Federal Government and the administration \nof President Muhammadu Buhari, GCFR towards \npromoting industrialization.\nThe NAIDP 2014 focused efforts on revitalizing the \nNigerian automotive industry, with wide-ranging \ninitiatives to address some of the nuances within \nthe industry. However, national and, indeed, global \neconomic challenges, as well as issues with respect \nto implementation and monitoring significantly \nchallenged the delivery of the objectives of the 2014 \nPlan. \nThe emergence of African Continental Free Trade \nArea (AfCFTA) Agreement and the need to position \nthe country as a strong leading player within the \nregional automotive ecosystem necessitated the \nreview of the NAIDP.\nThis was further reinforced by recent technological \ndevelopments and new opportunities in the global \nautomotive industry as well as requests from \nexisting and potential investors in order to unlock \nthe potential of the industry, maximise its value and \npromise of economic growth and development for \nthe country. \nThe revised NAIDP 2023 presents the aspirational \nvision, objectives, key pillars, enablers, and \nstrategic framework to optimally grow the Nigerian \nautomotive industry through 2033. I am pleased to \ninform you that this plan addresses the challenges \nconstraining effective delivery of the objectives. \nSpecifically, I will like to express appreciation to \nPresident Muhammadu Buhari for his support and \ncommitment towards concluding this task. Also, my \nesteemed appreciation goes to the Management \nof the Africa Export-Import Bank (AFREXIM) for the \nsupport towards completing the review. \nThis plan is intended to chart a course for the \nindustry over the next ten years. More importantly, \nit seeks to set the industry on the journey of ‘a \nthousand miles’. I, therefore, enjoin all stakeholders \nand industry actors to extend their commitment and \nsupport towards rebuilding the automotive industry.\nNigerian Automotive Industry Development Plan 6\nFore\nward\nForeward\nJelani Aliyu, MFR\nDirector-General, \nNational Automotive Design and \nDevelopment Council\nThe Automotive Industry can have a catalytic effect \non the industrialization of a country as it drives \nmass production, local content, localization of \nproduction techniques and job creation. It also \nstimulates growth of other sectors such as glass, \nrubber, asphalt, wood, gasoline, insurance and road \nconstruction. These are the kinds of benefits that we \nsought from the inclusion of the Automotive Sector \nin the Nigerian Industrial Revolution Plan (NIRP). \nThe review of the NAIDP was done to address \nexisting challenges and include the right levers \nrequired for the Automotive Industry in a country \nlike Nigeria: to enable exponential growth by \nproviding the necessary enhanced fiscal and non\u0002fiscal incentives, programmes and initiatives. Since \nthe beginning of the implementation of the 2014 \nNAIDP to date, the Council has succeeded in driving \nan investment of over US $1 billion by the private \nsector into the Nigerian Automotive Industry, setting \nup factories and assembly plants in a number of \nstates, with a combined installed capacity of over \n400,000 units per annum, and the creation of \nover 50,000 direct and indirect jobs. The Council \nhas also implemented several programmes and \ninitiatives including the nationwide development \nof twenty (20) Automotive Training Centres, the \nongoing development of three (3) Automotive \nIndustrial Parks and three (3) Automotive Testing \nCentres and Laboratories to enable infrastructure \nsharing between Producers/Assemblers for testing \nand certification of vehicles and automotive \ncomponents. The Council has also enabled the \nstart of assembly of Electric Vehicles, such as the \nHyundai Kona EV, and has also developed four \n(4) Pilot Solar Powered Electric Vehicle Charging \nStations.\nThe reviewed NAIDP is aimed at aggressively \nbuilding on the successes achieved so far: to \nstrategically address challenges and exponentially \nleverage on new local and global opportunities. \nI wish to express my deepest appreciation to the \nHonourable Minister and the Honourable Minister \nof State for supporting the review process of the \nNAIDP, as well as staff of the Federal Ministry of \nIndustry, Trade and Investment (FMITI), Nigerian \nInvestment Promotion Commission (NIPC), \nAutomotive Assemblers, African Export-Import \nBank (AFREXIM), African Association of Automotive \nManufacturers (AAAM), Japan International \nCorporation Agency (JICA) and all other supporting \nstakeholders for making this a reality. \nThe development of a country’s Automotive \nIndustry is a marathon and not a sprint. The \nCouncil is pleased to present to you the revised \nNAIDP and request you support the Council in its \nimplementation.\nNigerian Automotive Industry Development Plan 7\nThe Automotive Value Chain\nTier 2 - Component Suppliers Tier 3 - \nInitial Raw\nMaterial suppliers\nSteel\nPlastic\nRubber\nSilica\nConsumer Market\nDealers + Customers\nOEM’s Zone of Visibility ( Information about Data & Process )\nTier 1 - Component Suppliers\nOEM - In house Manufacturing\nPower train Assembly Process Tyre Supplier\nSupplier\nPiston\nGears\nAlley\nwheel\nRubber Tyre\nLeather\nSeat \nframe\nNone\nProcessor\nAdhesive\nGlass Sheet\nSeat Supplier\nElectronics \nSupplier\nGlass\nSupplier • Production\n• Sub assemblies\n• Painting\n• Machining\n• Sensor installation\n• Final assembly\nOEMs\nImport\nDelivery to Sales\nBranches\nDealers & Distributors\nProduct Flow\nManufacturers & Assemblers 1 2 3 4 Distributors / Dealers After Sale Services\nIndustry Regulators\nFinancing Institutions\nInsurance Repair &\nMaintenance\nRescue\nServices\nCentral Bank\nCommercial Banks\nMicro Finance Banks\nSource: National Action Committee on AfCFTA\nNigerian Automotive Industry Development Plan 8\nOriginal Equipment Manufacturers \n(OEMs):\nOEMs design, assemble, and market the \nfinal automotive product, and, on occasion, \nmanufacture equipment for it. OEMs are also the \noriginal producers of the vehicle’s components \nand are often the direct client of a retail company \nor distributor that sells directly to consumers and \ncorporations. Many of them are well-positioned \nwithin the automotive sector and include \ncompanies such as BMW, Ford, Mercedes Benz, \nNissan, Toyota, and Volkswagen.\nTier 1 supplier: \nManufacture components and/or systems \naccording to specified criteria, these firms supply \nOEMs directly. Tier 1 suppliers typically have \nstrong relationships with OEMs and are the final \nstep before a component reaches the OEMs. The \ncomponents they supply are in a wide range and \ninclude items such as the vehicle’s drive train, \nseats, pistons, keys, GPS, steering wheels, and car \nlights. Examples of tier 1 suppliers include Bosch \nand Continental.\nTier 2 supplier:\nProduce parts throughout the automotive industry \nvalue chain. Importantly, these suppliers usually \nserve multiple industries and not exclusively the \nautomotive industry. Tier 2 supplier’s supply \ncomponents such as computer chips, nuts, bolts, \nengine fans and fan belts. Examples of Tier 2 \nsuppliers include computer chip manufacturers \nlike Intel and Nvidia. \nTier 3 supplier:\nWithin the automotive industry, the term Tier 3 \nrefers to suppliers of raw, or close-to-raw, materials \nlike metal, plastic, steel, and rubber. OEMs, Tier \n1, and Tier 2 companies all need raw materials to \nproduce their specific components, so the Tier \n3s supply all levels. Examples of Tier 3 suppliers \ninclude plastic, steel, and Petrochemicals \nindustries.\nSemi – Knocked Down Assembly\nIn this process, the manufacturer (OEM) partially \nstrips down a vehicle at the origin and reassembles \nit in another country (Nigeria). However, \nthe manufacturers (OEM) cannot sell them \nimmediately as an SKD unit. So, it needs some \nmore manufacturing or assembly once the vehicle \nreaches its destination country (Nigeria) as SKD \nunit.\nSKD is defined by a list of parts and their assembly \ncondition. The qualifying list for SKD kits and their \nassembly condition (foreign or local) permitted \nunder this Policy are listed in the list below:\nCompletely Knocked Down Assembly\nIn this process, the manufacturer (OEM) completely \nstrips down or disassembles a vehicle at the origin \nand reassembles it in another country (Nigeria). \nHowever, the manufacturers (OEM) cannot \nsell them immediately as a CKD unit. So, more \nmanufacturing or assembly is required once the \nvehicle reaches its destination country (Nigeria) as \nCKD unit.\nCompletely Knocked Down (CKD) parts: In \naddition to the table above which classifies the \nassembly condition (local or foreign) of parts for \nCKD the floor panel, body sides and roof panel \nare separately supplied and assembled locally. \nThis Body Shell and all other parts are welded and \nfitted locally. \nThe Automotive Value Chain\nNigerian Automotive Industry Development Plan 9\nBody Type Definiton\nPassenger Vehicles Vehicles constructed for passengers carrying with up to four to eight \nseats (excluding the driver).\nMotorcycles Vehicles with 2 wheels, including scooters and mopeds, as well as \npowerful electric bikes\nLight goods vehicles \n(LGVs)\nVehicles for transporting goods and must have a gross weight of 3.5 \ntonnes or less.\nHeavy goods vehi\u0002cles (HGVs)\nVehicles for transporting goods and must have a gross weight over \n3.5 tonnes. This includes vehicles that are not used for freight.\nBuses and coaches\nVehicles for passengers carrying with nine seats or more (excluding \nthe driver). This includes minibuses, which are usually similar in \nconstruction to vans.\nCommercial Vehicles\nA “commercial vehicle” is a vehicle which is used or maintained for \nthe transportation of persons for hire, compensation, or profit or \ndesigned, used, or maintained primarily for the transportation of \nproperty (for example, trucks and pickups).\nMotor Tricycle \nVehicle\na vehicle with three symmetrically arranged wheels, having other \ntechnical characteristics than a motorcycle, fitted with an engine \nhaving a maximum design speed of more than 45 km/h e.g. “Keke \nNapep”\nAntique Vehicles defined as a car that was originally manufactured at least 39 years \nago\nThe Automotive Value Chain\nIndustrial Definitions\n Source: Vehicle licensing statistics: notes and definitions - GOV.UK (www.gov.uk), Vehicle Definitions - \nCalifornia DMV\nIntroduction\n\nNigerian Automotive Industry Development Plan 11\nIntroduction\nThe automotive industry holds significant \npotential to become a pivotal catalyst for Nigeria’s \neconomic growth and development. In its full \nglory, it incorporates a wide range of industrial \nprocesses including metals, plastics, rubber, glass \nand electronics, and is frequently perceived as \nbeing emblematic of national industrialisation. \nAs a result, the sector has often received strong \ngovernment support in economies across the \nworld.\nThe importance of the industry to the Nigerian \neconomy was highlighted in the Nigerian Industry \nRevolution Plan; with the Nigerian Automotive \nIndustry Development Plan (NAIDP), launched in \n2014 signalling government’s efforts to ensure \nthe take-off of the industry as a key sector with \ncross-cutting linkages across several industries \nand services, contributing to various economic \ndevelopment imperatives. \nThe NAIDP in 2014, the automotive industry \nhas attracted over US$1 billion in foreign direct \ninvestment and comprises about 30 assembly \noperators with an installed capacity to assemble \n400,000 vehicles annually. This progress has \nhowever, been slow and performance, suboptimal \nas the Nigerian automotive industry remains \ndominated by importation of second-hand vehicles \nmainly from the EU, Japan, and the USA. In 2020, \npassenger cars constituted the largest export item \nfrom the United States to Nigeria (about US$701 \nmillion) according to the U.S. Census Bureau. \nThis situation makes it difficult for Original \nEquipment Manufacturers (OEMs) to achieve the \neconomies of scale that guarantee anticipated \nreturn on investment and profitability from their \noperations. The trend also affects the development \nof the auto component suppliers and related \nindustries and subsequently, the ability to move to \nhigher local value-added modes of manufacturing \n(CKD/CBU).\nOther prevailing challenges include low income/\nlimited affordability, poor infrastructure and high \nlogistics costs and a lack of viable and sustainable \nfinance schemes to support the sector. \nThe auto industry currently plays a \ndisproportionately small manufacturing role in \nNigeria while the economic benefits of having \na fully-fledged integrated auto manufacturing \nsector are considerable. In South Africa, for \nexample, 110,000 people are directly employed \nin the assembly of vehicles and manufacture of \ncomponents whilst 900,000 people are employed \nin the full automotive value chain from mining/\nfarming to retail, insurance, and finance. Nigeria \nhas the potential to harness this sector, however, \nthe inability to implement an effective National \nAutomotive Plan is hampering its progress. \nConsequently, the state of the current industry \nand the unique challenges highlighted have \nnecessitated the review of the Nigerian Automotive \nIndustry Development Plan (NAIDP). With the \nrecently signed African Continental Free Trade \nArea (AfCFTA) Agreement in 2019, there exists \nan opportunity to position Nigeria as a hub for \nthe manufacture of automobiles and automotive \ncomponents for the African markets. \nTherefore, in recognition of the central importance \nof the domestic automotive industry to the future \ngrowth of the Nigerian economy, the NADDC \ncommissioned the revision of the Nigerian \nAutomotive Industry Development Plan. \nThis NAIDP 2023 presents the aspirational \nvision, objectives, pillars, enablers, and \nstrategic framework to reposition the industry. \nIt is the outcome of multiple engagements \nwith stakeholders across the value chain; and \nevaluation of the issues and trends within \nthe domestic, regional and international auto \nvalue chain. The Plan, which also takes into \nconsideration of existing studies and reports \nconducted by various groups such as JICA, \nAfrexim and AAAM, is aimed at setting up the \nNigerian automotive industry for regional \nleadership. \nNigerian Automotive Industry Development Plan 12\nThe document is structured as follows:\n• Chapter 1: Introduction \n – Sets the context for the Revised Plan and \nprovides general overview of the Plan. \n• Chapter 2: Global Automotive Industry Review \n - This section provides an overview of the global \nmarket, outlook and future trends shaping the \nindustry. It also provides a snapshot of the \nAfrican automotive industry, including a view on \nthe implications of AfCFTA on the Nigerian auto \nindustry. The section closes with a summary of \nkey learnings from the review as imperatives for \nthe development of the NAIDP 2023. \n• Chapter 3: Nigerian Automotive Industry \nReview \n – This section delves into the Nigerian automotive \nsector to better understand its performance and \ncurrent situation. It also provides an overview \nof the NAIDP 2014 and its performance; and \nthe challenges within the Nigerian automotive \nindustry today. \n• Chapter 4: Establishing a vision and associated \nobjectives for the Nigerian Auto Industry\n – Here, we define the vision and strategic \ndirection of the NAIDP 2023 through to 2033. \nIt expatiates on the key elements of the vision \nstatement, to ensure clear understanding of \nthe industry’s aspirations; and outlines the key \nindustry targets to 2033.\n• Chapter 5: Master Plan- Strategic Pillars & \nEnablers \n – This section highlights the framework proposed \nto actualise the vision and its associated \nobjectives. Here, seven (7) strategic pillars and \nthree (3) enablers that will ensure coordination \namongst players and necessary facilitation of \nthe delivery of the vision and targets identified \nin Chapter 4, are presented.\nIntroduction\nGlobal\nautomotive\nindustry\nreview\n\n"
  },
  {
    "url": "https://rsisinternational.org/journals/ijrias/articles/optimization-of-vehicle-fleet-replacement-decisions-and-fuel-cost-dynamics-in-the-nigerian-transport-sector/?categories=transportation",
    "text": "Optimization of Vehicle Fleet Replacement Decisions and Fuel Cost Dynamics in the Nigerian Transport Sector – International Journal of Research and Innovation in Applied Science (IJRIAS)\n\n# Optimization of Vehicle Fleet Replacement Decisions and Fuel Cost Dynamics in the Nigerian Transport Sector\n\n- Titilope Caroline OYINADE\n- 1148-1180\n- May 23, 2025\n- Transportation\n\nOptimization of Vehicle Fleet Replacement Decisions and Fuel Cost Dynamics in the Nigerian Transport Sector\n\nTitilope Caroline OYINADE\n\nDepartment of Business Administration, University of Benin, Benin City\n\nReceived: 25 December 2024; Accepted: 30 December 2024; Published: 23 May 2025\n\n### INTRODUCTION\n\nBackground to the Study\n\nAny country’s economic growth and social mobility are largely dependent on the transportation industry (Inegbedion & Aghedo, 2018). In Nigeria, a country with a vast and diverse population, efficient transportation is not only essential for connecting people and goods but also for fostering economic growth and prosperity. The Nigerian transport sector has historically been supported by fuel subsidies, which have alleviated the financial burden on both consumers and businesses (Ismail, Hezekiah & Bilikisu, 2014). However, recent policy changes, including the removal of fuel subsidies, have introduced new challenges and considerations for fleet managers and stakeholders in the transport industry.\n\nThe primary goal of replacement policy is to determine an item’s economic life, as retaining an item longer than necessary may result in a decline in the profit generated by the item’s use. Making the decision to replace the equipment at the appropriate stage of its useful life will maximize the profit from the equipment (Hossam, Ahmed, & Ahmed, 2023). The transition from subsidized fuel to market-based fuel prices has brought about significant shifts in operating costs for transportation companies, necessitating a re-evaluation of their fleet management strategies (Ismail et al. 2014). The decision to replace aging vehicles involves a complex interplay of economic, operational, and environmental factors, all of which must be balanced to ensure the sustainability and efficiency of transport operations. In light of this, it is in everyone’s best interest from a practical standpoint to justify the best terms for equipment renewal. (Lapkina & Malaksiano, 2023).\n\nAs fuel costs rise due to subsidy removal, the timing of vehicle replacement becomes paramount. Replacing vehicles too early can lead to unnecessary capital expenditure, while delaying replacements can result in higher maintenance costs, reduced fuel efficiency, and environmental concerns (Amiens, Oisamoje & Inegbedion, 2015). Striking the right balance is crucial not only for the financial viability of transport businesses but also for the broader economy by ensuring the uninterrupted movement of goods and people. The aims of the organizations might frequently suffer as a result of poor vehicle replacement decisions (Edwards, 2009). Reducing failure frequency balances failure expense with maintenance advantages including downtime, dependability, and availability. This is the basic objective of equipment replacement. In the operations of commercial transportation enterprises, the investment in transport vehicles often represents the biggest deployment of resources (Rani & Sukumari, 2014).\n\nThis study will employ advanced analytics and optimization techniques to model the relationship between fleet replacement decisions and fuel cost dynamics.\n\nThe removal of fuel subsidies in the Nigerian transport sector has introduced a complex set of challenges that demand innovative solutions. This study will delve into the intricacies of determining the optimum replacement time for vehicle fleets while accounting for the constraints imposed by increased fuel costs and alternatives to fuel. By doing so, it seeks to contribute valuable insights to the ongoing discourse surrounding sustainable and efficient fleet management practices, ensuring the continued vitality of the Nigerian transport sector in a changing economic environment.\n\nStatement of Research Problem\n\nThe transport sector in Nigeria plays a pivotal role in the country’s economic development by facilitating the movement of goods and people. One critical aspect of managing this sector efficiently is determining the optimum replacement time for vehicle fleets, especially in light of constraints imposed by the removal of fuel subsidies. The removal of fuel subsidies has significant implications for the cost of fuel, which is a major operational expense for transport companies and individuals alike.\n\nThe majority of resources allocated to the operations of commercial transportation businesses are often spent on transport vehicles. Making the wrong choices while replacing vehicles can frequently go against the organizations’ objectives. A methodical and trustworthy strategy for making judgments is therefore required in order to ensure that the likelihood of making a mistake is suitably reduced. The number of organizations that use equipment varies as much as how and when it is changed. It is unlikely that many Nigerian transportation firms have a written replacement plan for their fleet of vehicles.\n\nHowever, how and when equipment is replaced can have an impact on yearly expenses that range from hundreds to millions of dollars (Edwards, 2009), as well as dependability and system efficiency, as optimal equipment replacement increases equipment reliability. Since commuters’ locations are changed by using cars, which is a crucial transition in a transportation system, automobiles are perhaps the most crucial piece of equipment for transportation businesses. Since average costs are at their lowest when equipment should be changed, the goal of equipment replacement choices is to guarantee that equipment is replaced before it begins to incur greater costs. Therefore, the need for equipment replacement is driven by the need to reduce costs (Fallahnezhard & Niaki, 2011; Fan Machemehl & Gemar, 2012), since longer replacement time intervals have a tendency to lead to higher operational and overall expenses for equipment.\n\nLarge fleets of vehicles, often buses or taxis, are maintained by transportation businesses in Nigeria. The success of these businesses depends on these automobiles, which are an expensive investment. Choosing the appropriate time to replace each of the current cars is a glaringly major obstacle to the efficient management of such a big fleet of vehicles. Although there are evidence that these kinds of replacement decisions greatly impact the fleet’s failure rate and, in turn, the dependability of such fleets, they do not appear to have any clearly demonstrated effects on profitability.\n\nThe best equipment replacement is regarded as a successful tactic for boosting a failing system’s reliability. The cost of all the equipment that most producers purchase appears to be strongly influenced by investments in machinery and equipment. Given the constrained earnings and profits, many businesses replace their equipment at random, which might result in replacement at an age that is below ideal, which could limit earnings and hence profitability. The best age to start a business might limit profits and revenues. Because most assets have a lifespan that is ideal, determining the ideal age is crucial for planning and budgeting. The techniques used in finance theory to calculate an asset’s optimum life make the assumption that either an asset’s productivity declines with time or that its operational expenses rise annually.\n\nHowever, the sector faces multifaceted challenges that impact its efficiency and sustainability. Two critical factors, overloading and fuel cost, have historically posed significant constraints on the operation of vehicle fleets within the sector (Ismail et al. 2014). While overloading has been a major constraint in many of the research conducted in the transport sector for instance (Inegbedion, 2014; Inegbedion & Ahgedo, 2018; Inegbedion & Osifo, 2014), due to safety concerns and road deterioration, the recent removal of fuel subsidies in Nigeria has introduced a new dimension, and making fuel costs a crucial consideration in fleet management decisions. Hence, this research work aims to arrive at an efficient fleet management strategy given the increment in fuel cost and possible alternatives to ensure transport company break-even.\n\nFurthermore, previous research in this area of study has used data from God is Good Motor as a case study from 2009 to 2013 (Inegbedion, 2014). This study accessed the same data set from 2009 to 2022. This is to ensure a more comprehensive analysis and to take advantage of recent data availability, validation, robustness, and generalizability of previous research findings.\n\nThe study aims to offer practical recommendations for vehicle fleet management in Nigeria’s transport sector in a changing economic and policy landscape marked by fuel subsidy removal, thereby enhancing economic efficiency and environmental sustainability.\n\nResearch Questions\n\nTo guide this study, the following research questions have been formulated:\n\n1. to what extent does total operating cost of vehicle affect replacement time of vehicle fleet?\n2. how much do cost variations affect replacement time of vehicle fleet?\n3. what are the appropriate models for assessing the impact of cost of fuel on fleet replacement?\n4. how do various fuel alternatives affect the longevity of fleet vehicles and their overall sustainability performance?\n\nResearch Objectives\n\nThe research objectives is to provide valuable insights and recommendations for optimizing the replacement of vehicle fleets in Nigeria as this would contribute to improving fleet efficiency, reducing operational costs, promoting environmental sustainability and guiding policymakers and fleet managers evidence-based decisions. Specifically, the study objectives are to:\n\n1. ascertain the extent to which total vehicle costs influence replacement time of vehicle fleet.\n2. determine the extent to which cost variations affect the replacement time of vehicle fleet.\n3. to investigate the appropriate models for assessing the impact of cost of fuel on fleet replacement.\n4. to examine the extent to which fuel alternatives affect the longevity of fleet vehicles and their overall sustainability performance.\n\nResearch Hypotheses\n\nThe following Null hypotheses were tested\n\nH01: total vehicle costs do not influence replacement time of vehicle fleets.\n\nH02: cost variations do not significantly affect replacement time of vehicle fleets.\n\nH03: costs of fuel do not influence fleet replacement.\n\nH04: fuel alternatives do not affect the longevity of fleet vehicles and their overall sustainability performance.\n\nScope of the Study\n\nBasically, the study sought to investigate the optimum replacement time of vehicle fleets with cost of fuel constraints as a result of subsidy removal in Nigeria. The study will focus on transport companies in Benin City reason being that the city has a lot of reputable transport companies in Nigeria. The study will focus on the activities of God is Good (GIG) mobility because the firm is one of the best and technological-driven private commercial transport company in Nigeria. GIG mobility located in Benin- City. Edo State.\n\nThe study is consistent with the goal of the replacement theory for deteriorating items, which seeks to replace equipment or equipment parts in a manner that minimizes economic loss. It confines itself to the cost model of equipment replacement. The operation of GIG mobility for the period of 2009 – 2023 will be adopted for the study. This is because the era has experienced incessant increase in fuel price and also the total removal of fuel subsidy.\n\nSignificance of the Study\n\nBy incorporating data on fuel prices, vehicle maintenance costs, operational efficiency, and environmental impact, the study aims to provide evidence-based insights to guide fleet managers, policymakers, and other stakeholders in making informed decisions about when to replace vehicles within the new economic landscape.\n\n1. Operational Efficiency: Optimizing the replacement of vehicles in a fleet can significantly improve operational efficiency. Identifying the optimal replacement time based on the factors such as age, millage and maintenance costs help ensure that vehicles are replaced before they become unreliable or inefficient. This, in turn, reduces the risk of breakdowns, minimizes downtime, and enhances overall fleet performance.\n2. Cost Savings: Vehicle management involves significant costs, including acquisition costs, maintenance expenses, and disposal costs. A well-planned equipment replacement strategy can lead to cost savings by minimizing maintenance and repair expenses associated with older vehicles, reducing fuel consumption through the adoption of more cost saving vehicles and avoiding costly breakdowns and downtime. Additionally, by considering the total cost of ownership, including depreciation, resale value and lifecycle costs, fleet managers can make financially informed decisions regarding vehicle replacement.\n3. Environmental Impact: The transportation sector, including vehicle fleets, contributes to environmental degradation through emissions of greenhouse gases and air pollutants. By optimizing equipment replacement, fleet managers can transition to newer, cleaner and a smaller carbon footprint. This aligns with national and international commitment to address climate change, improve air quality and promote sustainable development.\n4. Cost of fuel: Studying cost of fuel as a constraint to optimum replacement of vehicle fleet in Nigeria have a broader economic implication, higher fuel prices can impact transportation cost, inflation rates and the competitiveness of transportation companies. Studying the cost of fuel as a constraint to motor vehicle replacement helps assess the potential economic benefits by adopting fuel-efficient vehicles or alternative fuel technologies.\n5. The study enables fleet managers, policy makers and stakeholders to make informed decisions considering the financial, environmental and economic aspects. It facilitates cost optimization, sustainable transportation planning and the development of strategies that align with energy and environmental goals.\n\nAbove all Research on optimum equipment replacement for vehicle fleets in Nigeria fills a knowledge gaps in the field of fleet management within the country. It provides local insights, data and recommendations specific to the Nigerian context, which guide fleet managers, policy makers and stakeholders in making informed decisions and developing effective strategies\n\n### LITERATURE REVIEW\n\nIntroduction\n\nThe chapter examines conceptual review, which includes concepts of equipment replacement time, models of equipment replacement time, determinants of equipment replacement time, vehicle operating cost, relationship between operating cost and equipment replacement time, empirical review of alternative to fuel, as well as replacement optimization framework.\n\nConceptual Review\n\nConcept of Equipment Replacement\n\nPhysical life, financial life, and economic life are three different mathematical definitions of equipment life. When evaluating equipment life, all three components must be stated and quantified in order to move toward replacement analysis and eventually decide on equipment replacement. The concepts of downtime, obsolescence, maintenance and repairs, investment, inflation, and depreciation are also put into consideration. This chapter discusses all of these topics in depth and provides examples of how to apply the economic calculations, all of which are essential to replacement analysis. The equipment manager will be able to conduct replacement analysis correctly and make informed equipment replacement decisions by combining these concepts and procedures.\n\nThe goal of equipment replacement policy is to replace equipment at the best possible moment. Choosing whether to replace old cars in the fleet is a significant and tough task of managing vehicle fleets. Such choices have a definite established effect and also have an influence on the fleet’s capacity to supply the necessary equipment (Kriett, 2009). This issue has long been recognized by fleet managers and experts, who have come up with a number of solutions. Ankoff (1961) asserts that the issue of equipment replacement entails two steps: (1) deciding on the ideal point in time or cumulative usage to replace, and (2) selecting the finest equipment that is available at the moment. The two issues—economic life and equipment selection issues—are precisely what equipment replacement policy addresses. The tendency of the equipment to decay, fail, or malfunction makes replacement necessary. A necessary service supplied by one or more assets over a limited time frame is the focus of the replacement problem. In order to maximize a certain measure of economy, the choice is often made about the replacement schedules for individual assets (Karabakal, Lohmann, & Bean, 1994). The present value of the asset is frequently this economic metric that has to be maximized.\n\nAccording to Gillepie and Hyde (2005), keeping and using a piece of equipment as long as its estimated marginal cost (MC) is less than or equal to the anticipated average total cost (ATC) of a new piece during its lifetime is the best course of action. Expressed mathematically, this strategy is MC old ≤ ( ATC new) is the average lifetime cost per unit of service expected from a new machine. Any alternate plan for replacing equipment would be more expensive. As a result, the lifetime average cost of a machine reaches its lowest point at the point in its service life when its marginal cost of operation equals the lifetime average cost of a new machine (Gillespie & Hyde, 2005). It is therefore recommended that the owner dispose off the equipment and buy a new one, which can serve as a decent or superior substitute for the old and which can be utilized up to the point of minimal average total cost.\n\nThe period at which the unit costs are at the lowest is known as the optimal replacement time (t) as defined by Sebo, Busa, Demec, and Svetlic (2013). Unit expenses will start to rise beyond that point. A replacement schedule outlines how long each asset in the sequence is to be kept in service, which determines when to replace the organization’s equipment (Akpan & Ufot, 2013). It also specifies whether to keep an existing asset (the defender) or to replace it immediately with one of the new assets (current challengers) or a sequence of future challengers to be installed after the current decision. The decision of whether to preserve a piece of equipment or replace it with a more modern technology is another name for equipment replacement challenges (Nair & Hopp, 1992). This resolution must give proper consideration to the nature of the replacement technology already in use as well as the potential for future technological improvement.\n\nThe process of replacing used equipment entails determining the right time to do so based on the study of a criterion or combination of decision criteria (Christer & Goodbody, 1980).\n\nWhen beginning a design and implementation project, equipment replacement in the process industry must take the commissioning, operating, and end-of-life phases of physical assets into account (Schuman & Brent, 2006). Guerts (1983) focuses on age-replacement, which asserts that a piece of equipment needs to be replaced with a new one if it fails or survives a specific age (preventive replacement or corrective replacement, depending on which occurs first). The term “Age” or “Time” should be understood as the variable that determines the equipment’s risk of failing. Examples of this variable include calendar time, operating hours, the quantity of goods produced, the volume of activities carried out, and the distance traveled (in the case of vehicles).\n\nThe likelihood of failure must also increase as a function of the equipment’s age for age replacement to be a rational norm, and corrective replacements must be more expensive than preventive replacements. The two replacement costs must be understood in order to apply it. Beichelt (2006) suggested a total maintenance cost limit replacement policy and shows how the reliability component may be incorporated into the total maintenance cost limit replacement model. Nevertheless, with the development of increasingly advanced and diverse condition monitoring technology, condition-based replacement is progressively emerging as a workable substitute for age replacement. The moment of failure is predicted from measurements on some “prognostic characteristics,” which are the object of the monitoring process, according to Geurts (1983). Conditioned-based replacement requires equipment to be inspected periodically or monitored continuously and replaced just before it fails. The same regulation will once again apply to the new piece of equipment. The best equipment replacement strategy, in general, is to maintain using a piece of equipment as long as its projected marginal operating cost is less than or equal to the expected average total cost of a new piece during its lifespan, as stated by Gillespie and Hyde (2005).\n\nReasons to replace equipment\n\n1. Age and usage: After being bought and put to use, a piece of equipment gradually starts to deteriorate and develop mechanical issues. It eventually approaches the end of its usable life and has to be replaced. The process of choosing when to replace equipment is thus a key component of effective equipment fleet management. In essence, this choice is deciding whether it is not more economically feasible to fix a malfunctioning piece of equipment (Gouglas, 2005). Equipment has an expiration date, and as it ages, the likelihood of breakdowns, problems, and inefficiencies increases. It could be time to replace your equipment with newer, more dependable ones if it is obsolete or exhibiting significant wear and tear.\n2. Cost Reduction: The typical guideline for reducing the yearly total cost of owning and running a piece of equipment is to make a modification when that cost starts to rise. The cost of interest also starts to decline at this stage when repair expenses start to rise faster than depreciation. Total cost increases, however, frequently happen very gradually, as a result, while the rule of growing total cost might provide an overall idea of when to replace a specific unit, it cannot provide an exact response. Note that the estimated repair costs indicate a progressive upward trend over time. In practice, however, repair expenses might vary significantly from one year to the next, from simple upkeep to a total overhaul Edwards (2009).\n3. Reliability: Most operators additionally factor in timeliness costs while making replacement selections in addition to the normal machinery expenses. Any occurrence that is timely, seasonable, or well-timed is referred to as being timely. Thus, the opportunity cost of not occurring at a proper moment is implied by the cost of timeliness. A machine that is trustworthy to work well is one that is reliable. Therefore, the term “equipment reliability” refers to the possibility or probability that the device will perform as expected. A machine is considered eighty percent (80%) dependable if it operates well or is anticipated to operate well eighty percent of the time. To put it another way, there is a 20% chance of becoming dissatisfied after using that equipment.\n4. Need to increase capacity: It is essential for management to replace outdated equipment with models that have a larger capacity to compete with competitors in the industry or even superior ones, without endangering timeliness, when productivity needs to be increased greatly as a result of expansion. Similar to this, when production drops, it could be required to cut operational expenses by getting rid of some equipment.\n\nVehicle Operating Costs\n\nGupta, Sharma and Ahuja (2007) opined that vehicle operating costs are a crucial factor in decision-making for individual, business, and policymakers. The costs associated with running a firm, or with running a machine, part, piece of equipment, or facility, are known as operating costs. These costs encompass a wide range of expenses associated with owning and using a vehicle. Pfueger (2005) states that running costs often comprise expenses that are spent as a direct result of using the unit. The variation in machine utilization affects these expenses. They are:\n\nFuel Costs: Fuel costs are a significant portion of operating expenses. The price of fuel and the vehicle’s fuel efficiency determine how much you spend on fueling. The type of fuel (gasoline, Compressed Natural Gas (CNG), electricity) also impacts costs.\n\nMaintenance and Repairs: Regular maintenance includes services like oil changes, filters replacement, tire rotations, and brake checks. Unplanned repairs for mechanical issues, wear and tear, and accidents are additional expenses.\n\nDepreciation: Depreciation is the decline in a vehicle’s value over time. It’s a major cost for vehicle owners, particularly those who plan to sell or trade in their vehicles in the future.\n\nInsurance Premiums: Insurance costs vary based on factors like the type of coverage, the vehicle’s make and model, the driver’s history, and the location. Premiums include liability, collision, and comprehensive coverage.\n\nRegistrations and Taxes: Annual vehicle registration fees and taxes contribute to operating costs. These fees are typically based on the vehicle’s value, weight, or other factors determined by local regulations Pfueger (2005).\n\nFinancing Costs: For those who finance a vehicle purchase, monthly loan payments or lease payments add to operating expenses.\n\nTires and Tire Maintenance: Tire replacement and maintenance expenses include purchasing new tires and periodic balancing, alignment and rotation.\n\nLicense and Permits: Some commercial vehicles require special licenses or permits for operation, which come with associated costs.\n\nToll fees and Parking: Tolls and parking fees can be ongoing expenses, particularly for urban drivers who navigate through toll roads and city parking areas.\n\nCleaning and Detailing: Regular cleaning and detailing help maintain the vehicle’s appearance and value. Costs vary based on the cleaning frequency and the type of service Pfueger (2005).\n\nVehicle upgrades and modifications: Optional upgrades, modifications and accessories contribute to the overall operating cost. These may include features like Global Positioning Systems (GPS) entertainment systems, and aftermarket parts.\n\nEnvironmental and regulatory fees: Some regions impose environmental fees or emissions testing requirements, which add to vehicle operating costs.\n\nCalculating and managing vehicle operating costs is essential for budgeting purposes, evaluating the economic feasibility of vehicle ownership, and making informed decisions when purchasing or using a vehicle. Different vehicles, usage patterns, and geographic locations can lead to varying operating cost profiles. As technology evolves, particularly in the area of alternative fuels and electric vehicles, operating costs may shift as well Ahuja (2007).\n\nMeasurement of Vehicle Operating Costs\n\nMeasuring vehicle operating costs involves quantifying the various expenses associated with owning and using a vehicle. Vehicle expenses may be divided into fixed and variable costs, sometimes referred to as operational, marginal, or incremental costs, which rise with mileage, in accordance to the Vitoria Transport Policy Institute (2022). There are other expenses that are usually labeled as fixed expenditures, such as taxes on vehicles, insurance, and registration. Depreciation does not grow with miles on a car, although insurance does.\n\nHere’s how the costs can be measured according to the institute:\n\nFuel cost: Measuring fuel consumption has to do with keeping track record of fuel consumption per miles driven and fuel filled. Calculate miles per gallon (MPG) or liters per kilometer(L/100km) to measure fuel efficiency. Besides, record the amount spent on fuel during a specific period.\n\nMaintenance and Repairs: Maintain a maintenance log by recording all maintenance activities, including oil changes, filter replacements, and repairs. Also, track maintenance expenses are to sum up all cost related to scheduled maintenance and unexpected repairs.\n\nDepreciation: Determine initial and current value by calculating the difference between the initial purchase price and the current market value of the vehicle, Furthermore, calculation of depreciation involves dividing the difference in value by the number of years the vehicle has been owned.\n\nInsurance Premiums: Record insurance payments by documenting insurance payments made over a specific period.\n\nFinancing Costs: Calculate interest payments by determining the total interest paid over the loan or lease agreement.\n\nTire Costs: Note tire replacements by recording the number of times you replace the vehicle’s tire and associated costs.\n\nRegistration and Taxes: Record the amount paid for vehicle registration and associated costs.\n\nTotal Cost of Ownership (TCO): sum all expenses by adding all the costs mentioned above to calculate the total cost of ownership over a specific period (e.g., monthly, annually) Lisa (1995).\n\nCost per Mile (Kilometer): Divide total cost, this is done by dividing the total operating cost by the number of miles (or kilometers) driven during the same period to calculate the cost per unit of distance.\n\nComparative Analysis: Compare Vehicles, if you own multiple vehicles, compare the operating costs by the number of miles (or kilometers) driven during the same period to calculate the cost per unit of distance.\n\nRelationship between Operating Cost and Equipment Replacement Time\n\nThere are many iterations of choices that must be made in relation to equipment ownership. The owner of the main piece of equipment must select how much and how frequently routine preventative maintenance should be performed. Preventive maintenance consists of routine, periodic operations designed to reduce repair costs or increase the lifespan of equipment. Lubricant replacements on a regular basis are an excellent illustration of preventative maintenance. Corrective maintenance or repair choices are the next step of decision-making following preventative maintenance. When a machine or one of its parts malfunctions in the course of business, it is crucial to fix it in order to recover operational status and, in the case of commercial transport vehicles, win back the trust of clients. Corrective maintenance decisions, rebuild decisions, or repair decisions include significant mechanical upgrades that help the equipment last longer. The equipment manager must consider an equipment replacement option when a machine nears the end of its useful life, and the majority of these decisions are complex (Mitchel, 1998).\n\nEconomic considerations have a role in equipment selections linked to upkeep and repair. They fall under the umbrella of increasing the investment’s profitability. Two more categories of decisions, in addition to those involving maintenance and repair, are presented to policymakers when it comes to heavy machinery. The first category of choice is of an operational character and deals with how to maximize the output of the machinery. The second is mechanical and deals with methods to guarantee the dependability of the apparatus.\n\nAn Earthmoving Machine goes through three stages in its life cycle: purchase, use, and sale. The equipment manager should aim to buy as frequently as possible owing to the significant capital expenditure involved since the decision to buy only occurs once over the life of each machine. After the machine is purchased, “operate” choices are commonly made; the objective is to run the machinery as inexpensively as feasible to achieve the desired productivity. In the course of a machine’s life, the “sell” choice may be considered more than once, but it is only ever made once. According to Mitchell (1998), the equipment should be sold for the highest price feasible. The three distinct economic choices might not be too challenging to understand and analyze when looked at separately. But the three of them have a complicated dynamic. Each one can have a significant influence on the others. Despite the high cost of purchasing new equipment, running costs are relatively low at the beginning of a machine’s life. The choice to “sell” should be taken into consideration when operational costs rise.\n\nThe costs associated with owning a machine are those that would otherwise have to be paid in order to use it. Inputs other than purchase and sell include expenses like taxes or insurance. The easiest way to describe ownership expenses is on a calendar basis; they are incurred whether or not the machine is in use. The average owing cost each period decreases with increased equipment retention. On the other hand, the average cost of ownership per period might be rather high if the equipment is only held for a little time due to the fact that new machines depreciate quite fast in the beginning.\n\nAccording to Mitchel (1998), using a piece of equipment results in a continuous flow of running expenses. These are expenses that arise regularly as a result of operating a machine. Operating expenses may be essentially nonexistent if the machine is not in use. The machine’s running expenses may increase significantly if it is utilized frequently. Other expenses, such as tires, repairs, and rebuilds, come up more frequently and can be rather expensive. When a machine is young, the average running costs are low, but as it becomes older, the average expenses tend to rise.\n\nConcept of Alternative to fuel\n\nFollowing the termination of fuel subsidies by the Federal Government, there are signs that more Nigerians are requesting the usage of compressed natural gas. When President Bola Tinubu said that the government would no longer provide gasoline subsidies, Nigerian service stations increased the price of petrol at the pump. Petrol, diesel, and propane/LPG can all be substituted with compressed natural gas as a fossil fuel. It does release greenhouse gases upon burning. In the case of a spill, it is significantly safer than other fuels and a more ecologically friendly substitute for gasoline and diesel. (Independent Newspaper in Nigeria, June, 2023.)\n\nStakeholders believe that for every million converted automobiles, compressed natural gas can bring in over N200 billion for the federal government. These CNG stations cater to Auto Gas requirement of vehicles, providing a cleaner, safer, economical, proven, and indigenous fuel. NIPCO Gas presently fuels 7000 vehicles with AutoCNG. Nipco Gas has 4 AutoCNG conversion workshops in Ogun state and Abuja FCT to convert PMS vehicles on AutoCNG. More and more fleets operate converting their fleet on AutoCNG due to safety, availability, and economic reasons.\n\nAutogas, a blend of liquefied petroleum gas and compressed natural gas used as a transportation fuel, is the second most popular and well-liked alternative vehicle fuel in use today, behind ethanol. The use of auto-gas has dramatically increased during the previous few years on a global scale. 26.7 million tons were used in 2016, an increase of 3.7 Mt (16% from 2000) and 283 000 tons (1.1% from 2015). Almost 26.8 million auto-gas cars are already on the road worldwide (Morgan, 2017). The need for more eco-friendly, better, and cleaner fuels has been driven by the problems posed by global warming. The most popular types of petroleum fuels are PMS and AGO.\n\nPossible Alternatives to Fuel\n\nThere are several alternatives to traditional fossil fuels that can be used to power vehicle fleets. These alternatives aim to reduce greenhouse emissions, dependence on oil, and overall environmental impact. Numerous studies have been conducted recently on the use of alternative fuel vehicles in the light vehicle category Singh, Singh, and Vaibhav (2020). A group of factors that are essential for the effective adoption of alternative fuel cars have been discovered by these investigations. Here are some notable alternatives:\n\nElectricity: Electric vehicles (EVs) are becoming increasingly popular due to their zero tailpipe emissions and improving battery technology. They can be charged from the electric grid and offer varying ranges, form short-range city cars to long-range options for highways.\n\nHydrogen Fuel Cells: Hydrogen fuel cell vehicles use hydrogen gas to generate electricity, with the only byproduct being water vapor. These vehicles have the advantage of quick refueling times compared to EVs, but hydrogen production and distribution can be challenging Zhou, Kong, Zhao, Huang, Wang and Campy (2019).\n\nBiofuels: Biofuels are derived from organic matter like crops, algae, or waste materials. They can be blended with or replace conventional gasoline and diesel fuels. Ethanol (derived from corn, sugarcane, etc), and biodiesel (from vegetable oils or animal fats) are common examples.\n\nNatural Gas: Compressed gas (CNG) and liquefied natural gas (LNG) can be used as alternative to gasoline and diesel. Natural gas is cleaner burning than traditional fuels and can be sourced domestically.\n\nPropane Liquefied Petrol Gas (LPG): Propane is a byproduct of natural gas processing and oil refining. Vehicles can be converted to run on propane, which burns more cleanly than gasoline or diesel.\n\nSynthetic Fuels: Also known as e-fuels or electro fuels, these are produced by using renewable energy sources to generate hydrogen, which is then combined with carbon dioxide captured from the air. The resulting synthetic fuels can be used in conventional internal combustion engines Lai, Liu, Sun, Zhang and Xu (2015).\n\nSolar-Powered Vehicles: Solar energy can be harnessed to power electric vehicles. While fully solar-powered cars might be limited by available space for solar panels, solar-assist systems can extend the range of electric vehicles.\n\nKinetic Energy Recovery Systems (KERS): These systems capture and store energy braking or deceleration, then use it to assist the vehicle during acceleration. KERS can be employed in combination with other power sources.\n\nHybrid Vehicles: Hybrid Vehicles combine an internal combustion engine with an electric motor and battery. They can run gasoline or a combination of gasoline and electricity.\n\nPlug-in Hybrid Vehicles (PHEVs): PHEVs are hybrids with larger batteries that can be charged from an external power source. They can run on electric power for shorter distances and rely on gasoline or alternative fuels for longer trips.\n\nRange, availability to recharging infrastructure, affordability, and vehicle performance are among the factors that have been recognized as essential for the effective adoption of alternative fuel technologies (Lai et al, 2015; Sierzchula, Bakker, Maat, & Van, 2014; Singh et al., 2020; Statharas, Moysoglou, Siskos, Zazias, & Capros, 2019; Zhou et al, 2019). Range is seen as a crucial aspect, particularly for Electric Vehicles (EV) and Hybrid Electric Vehicles (HEV), since buyers are worried about the car’s capacity to go long distances before needing to recharge. Access to recharging infrastructure is crucial, since customers must have faith that they can find charging stations when they need to refuel their cars. The price of alternative fuel cars is viewed as one of the biggest obstacles to their acceptance. Different fuel substitutes must be taken into consideration for transportation businesses to break even, weighing the benefits and drawbacks associated with each solution. Additionally, it may boost the cars’ economy and enhance transportation sustainability (Alp, Tan, & Udenio, 2022; Backhaus, 2022).\n\nFinancial Incentives Policy for Gas Fuels Adoption\n\nThere may be financial incentives for the fuels themselves or the vehicles that can use them. The primary tool used by the nations evaluated for this research to encourage vehicle gas can be a reduction in excise duty, a total exemption from sales tax, or both. On occasion, a tax credit for fuel may be given to commercial vehicles. By adopting these actions, the cost of running an Alternative Fuel Vehicle (AFV) is directly cheaper than that of a gasoline or diesel vehicle, which reduces the payback period for converting or acquiring an AFV. Differences in excise duty are particularly evident since they affect pricing at the pump, making people aware of the possible financial benefits from utilizing alternative fuels Morgan (2017).\n\nMorgan (2017), suggest the following key reasons why a transportation company should consider using CNG:\n\nCost savings: CNG generally costs less than traditional fuels like gasoline or diesel. By switching to CNG, transportation companies can significantly reduce their fuel expenses, which can have a positive impact on their overall operating costs. This cost advantage can make the company more competitive by offering more competitive pricing to customers.\n\nEnvironmental Sustainability: CNG is considered a cleaner-burning fuel compared to gasoline or diesel. It produces fewer greenhouse emissions, reduces air pollution, and improves local air quality. As sustainability becomes increasingly important to consumers and regulators, transportation companies that prioritize environmentally friendly practices can gain a competitive edge by attracting environmentally conscious customers and complying with stricter emission regulations.\n\nGovernment Incentives: Many governments and regulatory bodies provide incentives and tax breaks for companies that adopt cleaner fuel technologies such as CNG. By taking advantage of these incentives, transportation companies can reduce their operational costs further and gain a competitive advantage over companies that have not embraced cleaner fuel options.\n\nAccess to restricted areas: In certain urban areas, there may be restrictions on vehicles that run on conventional fuels due to pollution concerns. By using CNG-powered vehicles. Transportation companies can access these restricted zones, providing them with more business opportunities and expanding their reach.\n\nLong-term fuel price stability: CNG prices are generally more stable compared to gasoline or diesel prices, which are subject fluctuations in global oil markets. This stability allows transportation companies to have better predictability in their fuel costs, making it easier to plan and budget for the future.\n\nTechnological Advancements: The technology for CNG-powered vehicles has advanced significantly, providing greater efficiency and performance. Modern CNG engines can offer similar power and torque to their conventional counterparts, ensuring that transportation companies do not compromise on performance while enjoying the benefits of CNG.\n\nBy embracing CNG as a fuel source, a transportation company can demonstrate its commitment to cost efficiency, environmental sustainability, and staying ahead of market trends. These factors can contribute to improved competitiveness in the industry, by attracting customers, reducing operating cost, and complying with regulatory requirements World Liquefied Petroleum and Gas Association (WLPGA, 2014).\n\nBenefits of Gas Fuels on the Vehicle and Environment\n\nWhen LPG is the only fuel utilized, there are some durable benefits over gasoline, according to experience. Engine life is said to be generally 50% longer as a result of reduced cylinder bore wear at cold starting since LPG does not wash oil from the cylinder walls and the lubricating oil has a longer effective life owing to nearly absolute lack of dilution. While deposits in the combustion chamber and on the spark plugs are reduced, the lifespan of the spark plugs is not always increased. The durability of the exhaust system is improved while running only on LPG fuel. Additionally, because to its basic chemical make-up, LPG burns more thoroughly, resulting in fewer environmental emissions of CO and HC (WLPGA, 2014).\n\nBenefits of Gas Fuels to Nigeria as Country with Natural Gas Endowment\n\nNigeria may use its own natural gas supplies instead of foreign oil to reduce import prices and strengthen the nation’s balance of payments. This is because using local natural gas as a form of transportation might help Nigeria’s current account balance. The total FOREX committed to imports in the country from 2013 to 2017 was $119.409 billion, according to data from Central Business Nigeria (CBN), and the total FOREX committed to imports in the oil sector was $36.371 billion, or about 13.5% of all imports made by the nation.\n\nConditions for CNG fleet Conversion\n\nConverting vehicles to run on compressed natural gas (CNG) involves modifying the vehicle’s fuel system to accommodate CNG as a fuel source. CNG is a cleaner-burning alternative to gasoline or diesel, and it can offer cost savings and reduced emissions (WLPGA, 2015). However, there are certain conditions and considerations that need to be met for a successful CNG conversion:\n\nVehicle Compatibility: Not all vehicles can be easily converted to run on CNG. Typically, vehicles with gasoline engines are more suitable for CNG conversion compared to diesel engines. Modern fuel-injected vehicles are usually better candidates for conversion than older carbureted vehicles.\n\nCertification and Regulation: Ensure that CNG conversions are complaint with local and national regulations. Some regions have specific requirements and standards for CNG conversions to ensure safety and environmental standards are met.\n\nQualified Conversion Kits: Use certified and approved CNG conversion kits. These kits include components like CNG storage cylinders, regulators, injectors, and control systems Efforts are to ensure that conversion kit is designed for your specific vehicle make and model.\n\nProfessional Installation: CNG conversions should only be performed by trained and certified professionals. A professional mechanic or conversion shop with experience in CNG conversions should carry out the installation to ensure safety and proper functioning (WLPGA, 2015).\n\nFuel System Modification: The vehicle’s fuel system needs to be modified to accommodate CNG. This includes adding CNG storage cylinders (typically mounted in the trunk or bed of the vehicle), a high-pressure regulator, and injectors that can deliver CNG to the engine.\n\nEmission Control: CNG conversions should maintain or improve emissions performance compared to the original gasoline system. Properly calibrated and functioning emissions control systems are essential to meet environmental standards.\n\nPerformance and Power Considerations: CNG has a lower energy content compared to gasoline, which can result in a decrease in power and range. Vehicle performance might be slightly affected, so its important to manage expectations.\n\nMaintenance: CNG systems require regular maintenance to ensure safety and optimal performance. This includes periodic inspections of the CNG components, such as cylinders, regulators, and injectors.\n\nSafety Precautions: CNG is stored under high pressure, so safety precautions are paramount. Proper installation, regular maintenance, and adherence to safety guidelines are critical to prevent accidents.\n\nFuel Availability: Consider the availability of CNG refueling stations in your area. CNG refueling infrastructure might be limited, so its important to ensure you have access to fuel before converting your vehicle.\n\nCost Analysis: Evaluate the costs of CNG conversion, including the price of the conversion kit, installation, potential decrease in performance, and fuel savings over time. Determine whether the investment aligns with your needs and budget.\n\nBefore proceeding with a CNG conversion, its recommended to consult with experts in the field, gather information from reliable sources, and thoroughly assess the feasibility and benefits of the conversion for your specific vehicle and circumstances.\n\nHistory of CNG fleet conversion and adoption in Nigeria Transport Sector\n\nCompressed natural gas (CNG) is being employed in automotive transportation in countries like Italy, France, New Zealand, Canada, the United States of America, Russia, etc., according to Oghenejohoh and Akpabio (2023). The 1950s saw the beginning of early CNG transportation tests in Russia. With the implementation of intricate programs to guarantee the establishment of a network for the delivery of CNG to vehicles, interest was renewed in the 1980s. As part of its efforts to exploit natural gas (NG) resources, the Nigerian government proposed using compressed natural gas (CNG) as a vehicle fuel in 1997. However, development has been slow Olufemi (2015).\n\nOver liquid fuels compressed natural gas (CNG) offers operational benefits. It combines well with air in an engine even when the temperature is low and does not require heat to vapourize. CNG also lessens issues with engine wear and exhaust pollutants.\n\nThe Nigerian Gas Company (NGC), a subsidiary of the Nigerian National Petroleum Corporation (NNPC), launched the Compressed Natural Gas (CNG) pilot scheme in 1989 to promote CNG as an automotive fuel as part of the company’s efforts to promote natural gas utilization in the nation and increase the revenue base of the company Oghenejohoh and Akpabio (2023).\n\nPresently, in the Nigerian transport sector, commercial transport company recently have started to adopt CNG as an alternative to fuel. God is Good mobility has been discovered the most technologically sophisticated road transport company in Nigeria. Although GIGM may not have the most cutting-edge technological systems, its utilization of technology across its activities much outpaces those of its rivals. GIG Mobility, the transport management system uses GIGM to handle many elements of its operations. However, frequent passengers know that GIGM keeps to schedule at least 80% of the time. The fleet can be tracked by GIG Mobility, which enables them to look into unexpected delays and take appropriate action. This reduces trip times and boosts the fleet’s overall efficiency Oyeniyi (2016).\n\nHowever, adoption of CNG in Nigeria has been discovered with the following drawbacks: A high rate of engine knocking in vehicles, lack of luggage room, expensive conversion, explosion propensity, a dearth of CNG stations Oyeniyi (2016).\n\nHistory of GIG mobility in Nigeria\n\nGod is good motor now known as GIG mobility is a transportation company in Nigeria. It’s a popular intercity cum country transport service that provides bus services to various destinations across Nigeria and beyond. It was founded by Edwin Ajaere in 1998. The company started its operations with a focus on providing safe and reliable bus transportation services in Nigeria.\n\nOver the years, GIG Mobility has grown to become of the leading intercity transportation companies in Nigeria. The company’s commitment to safety, comfort, and professionalism has contributed to its popularity among travelers. GIG mobility has expanded its network to cover numerous routes connecting major cities and towns across Nigeria Rafiu (2022).\n\nOne of the distinguishing features of GIG mobility is its adoption of technology to enhance customer experience. The company has implemented online booking platforms and mobile apps, allowing customers to book tickets, choose their seats, and track buses in real-time. This has made the booking process more convenient for travelers. GIG mobility boasts a modern fleet of buses that range from standard to luxury options, catering for different customer preferences. They offer various services such as express routes, shuttle services, and charter services. This diverse range of offerings has contributed to the company’s popularity.\n\nRafiu (2022) stated that GIG mobility has gained a strong reputation for its focus on safety, comfort and professionalism. The company’s name, which incorporates a positive message has also likely contributed to its brand recognition and popularity. It was found that by Rafiu (2022), that GIG mobility is the best private transport company in Nigeria and also highly technological driven in relation to other transport companies in Nigeria.\n\nTheoretical Review\n\nThere are several theories and models that have been developed to address the question of when to replace equipment. These theories provide different perspectives and methodologies for determining the optimal timing for equipment replacement. Below are some of the prominent theories of equipment replacement:\n\nEconomic Life Cycle Theory\n\nThis theory suggests that equipment should be replaced when the sum of its operating and maintenance costs becomes greater than the costs of replacement. It aims to find the point where the total cost, including acquisition, operation, maintenance, and disposal, is minimized over the equipment’s life cycle. Hartman and Tan (2014) opines that the total of all possible expenditures that may be incurred with the equipment over the course of its lifespan (including the cost of purchase and the total cost of ownership) is used to calculate the life cycle cost of a piece of equipment. Because of the notion of declining money value in the “time value of money,” it is well known that the cost of a spending today is higher than the cost of an expenditure next year. In order to take into consideration the time worth of money in this study, a discount rate was applied. We must move these expenditures to a point in time that serves as a baseline in order to compare costs incurred at various dates. In order to assess the present value of the expenses for the case study, the discount rate factor was taken into account. Through the lens of their repair limit theory, Drinkwater and Hastings (1967) offer an alternative perspective on the economic replacement decision dilemma. The life-cycle costing (LCC) study takes into account every expense related to the use of an item. In light of this, equation (2.1) is suggested in this article to determine the life cycle cost of earthmoving equipment Pedram, Mehdi, and Saheed (2016). The goal of this model is to calculate the cost of construction equipment during its lifetime. As a result, the life cycle cost of earthmoving equipment is represented as follows:\n\nLCCe =VAC+TC+IOC+IC+FC+MCC +RC+GOC+TC+DTC+SCC (2.1)\n\nWhere: LCCe is the earth moving equipment life cycle cost.\n\nVAC is the equipment acquisition cost.\n\nTC is the tire cost.\n\nIOC is the cost of intermediate overhauls.\n\nLC is the lubricant cost.\n\nFC is the fuel cost.\n\nMCC is the cost of maintenance and checkup.\n\nRC is the repair cost.\n\nGOC is the cost of general overhauls.\n\nTC is the tax DTC is the downtime cost.\n\nSCC is the cost of sleep capital.\n\nWhen determining the economic life of capital equipment, there are two main conflicts to consider according to Pedram et al (2016): (1) The aged asset’s rising operational and maintenance expenses.\n\n(2) The decreasing ownership cost of maintaining the equipment in use as the initial capital cost is deducted over a longer period of time. The economic life model calculates the dollar cost as a result of an endless series of replacements or modifications during the first N cycles. It is helpful to convert the entire discounted expenses related to the economic life to an Equivalent Annual Cost (EAC), which is represented in the equation (2.1) in order to make the comprehension of this total discounted cost easier (Campbell and Jardine 2001).\n\n(2.2)\n\nwhere A=Acquisition cost\n\nCi=O & M costs of equipment in its ith year of life, assuming payable at the start of year\n\ni=1, 2, . . ., n\n\nr=Discount factor\n\nSn=Resale value of equipment of age n years\n\nn=Replacement age\n\nC(n)=Total discounted cost for a chain of replacements every n years\n\nAge-based Replacement Model\n\nIn this approach, equipment is replaced based on its age, often using a predetermined replacement age. This theory assumes that equipment becomes less reliable and more costly to maintain as its ages. Replacement decisions are made to avoid increased maintenance costs and potential failures. Comparing multi-component systems to single-unit systems, the research of age replacement plans is more difficult. This is because, at the moment of replacement, a number of still-functioning components might exist. To calculate the replacement costs, it is important to know the expected value of the random variable known as the number of non-failed components. In other words, the age replacement issue for a system with several components requires additional derivations and analysis. When the components’ lives are continually distributed, age replacement strategies for multi-component systems have lately been studied. For parallel and series systems with dependent components, Safaei (2020) investigated age replacement policies for equipment and found it reliable.\n\nCondition-based Maintenance (CBM) Theory\n\nThis revolves around monitoring the condition of equipment using various sensors and techniques. Replacement decisions are based on the equipment’s actual condition rather than a predetermined schedule. When the monitored condition indicates a significant decline in performance or potential failure, replacement is initiated. The key to CBM is carrying out preventive maintenance tasks before faults happen while continuing to utilize the product. But creating monitoring and maintenance solutions based on real-time sensor data is quite difficult. Failures that are detectable by one or more indications are dealt with by CBM. Cadick, Gabrielle, Traugott and John (2009). In a CBM environment, maintenance efforts (people, processes, and tools) are applied based on the actual condition of the equipment rather than its age, so equipment in good condition does not require as frequent maintenance as equipment that has reached the predicted age of deterioration. Utilizing test tools or statistical modeling of data to foretell equipment status is the foundation of CBM Cadick et al (2009).\n\nTechnology Obsolescence Theory\n\nThis theory focuses on replacing equipment when newer technologies offer significant improvements in efficiency, performance, or cost-effectiveness. The decision is based on the idea that holding onto outdated equipment may lead to competitive disadvantage or increased operational costs.\n\nOptimization Models\n\nOptimization techniques, such as linear programming, dynamic programming, and simulation, can be employed to find the optimal replacement strategy considering multiple variables, constraints, and objectives. Even though the problem is categorized as a complicated system, using the optimization model is a straightforward and simple process for solving many problems. Additionally, dynamic programming’s division of the system into successive stages makes the issue much simpler, clearer, understandable, and controllable. This technique was solved using Microsoft Excel, a well utilized piece of classic PC software. This makes it possible for many non-technical people interested in environmental policies to handle such issues with ease (Alaa, 2009).\n\nAchieving the global optimum for each stage of the model is ensured by using linear programming to find the best solution at each level. When the alternatives of the streamlined method are established, a sensitivity analysis is immediately created. Each option provides the best values for the many factors linked to the lowest prices.\n\nRepair Limit Theory\n\nThe 1960s saw the debut of repair limit theory. The concepts of “defender” and “challenger” are used to describe repair limit theory. According to Terborgh (1949), the defender is the equipment that the corporation is now using, and the challenger is new equipment that might perform the same function as the defender. A capital good reaches the end of its maximal physical life when repair becomes physically impossible, according to Ahmed (1973). Furthermore, “the maximum economic life of any equipment may be defined as ending when its repair cost exceeds its replacement cost”; as a result, the equipment replacement problem aims to achieve the equipment’s optimal economic life. Mitchell (1998) described the repair limit as a cap on the quantity of repairs that may be made to the repair limit is a cap on the quantity.\n\nReplacement Models\n\nIn most of the models found in the literature and proposed to companies, the criteria for reform/replacement are mainly of an economic nature; very few use environmental or technological criteria. Obviously, construction equipment (subject of this research project) cannot be isolated from this generality even if the environment and technological progress must be better taken into account in the choices (decisions) of reform and/or renewal Ricardo (2017). It is useful to remember that in any construction company, the department in charge of equipment is responsible for the “health” of each piece of equipment throughout its useful life cycle, from acceptance through to its aging period and downgrading. He is also responsible for controlling the expenses relating to the possession of each piece of equipment. This is how a certain number of questions will successively arise and may influence the reform policy:\n\nWhat is the expected durability of the equipment? When does this hardware provide maximum operating gain? When maintenance actions should be stopped by stopping “therapeutic relentlessness” on this equipment? When should it be reformed (downgraded)? What is the resale value of the equipment? Alternatively, conversely, what is the cost of scrapping or dismantling? Should it be replaced identically or with new generation equipment? Should it be renovated?\n\nThese questions are essential for the equipment manager when it comes to addressing the technical and economic dimension of the reform models to be proposed. We have already seen that, of all the criteria discussed, the optimal lifespan is the most used in the literature. Which is completely understandable since the vocation of a company is above all to obtain profit. This is (of): The minimization of the life cycle cost (LCC) taking into account the evolution of the silver rate for classicØ construction equipment (> 10 years). In this case, Ricardo (2017) opines that, it is a question of summoning the equations respectively:\n\nᶿt = y/t where y = cumulative cost of equipment of age t\n\n{Cr + Cr f(t)}/Vra(t) ˂ ᶿ decision in favour of compensation\n\n{Cr0 + Crf (t)}/Vra (t) = ᶿ\n\ndecision favourable to the reform and Cro (t) representing the equipment repair limit at time t\n\nTechnological evolution weighs less in decommissioning/replacement decisions. It is a criterion which, if taken into account, is because it influences the performance of the equipment. In this case, it is difficult not to link the replacement to the economic aspect of the decision-making. This reflection is also valid for the environmental criterion of the reform decision. Indeed, the company is pushed to the decision of replacement for “environmental” reasons when its profit is threatened by possible surcharges, as is the case in certain current projects. Minimization of the average annual maintenance cost (Cma) by using equations Galvis (2014) and Blank (2005). The annual value method developed with the equivalent uniform annual cost (CAUE) minimization.\n\nThe equation Ricardo (2017) known as the Blank and Tarquin formula makes it possible to obtain the economic service life (ESL) which, minimized, leads to the optimal replacement time. From the replacement model of Ricardo where the month is used as the period in the studies unlike the others which use the year. The advantage of the work of Ricardo (2017) is its easy adaptation to equipment with relatively short lifetimes such as small equipment. Equation Fraser (2000) is invoked to illustrate this method. The repair cost limit is also an interesting criterion used by Drinkwater (1967). to propose a fairly practical method. The latter succeeded in proving the advantages of their method with respect to the criterion of the limit of life. Indeed, the equipment is downgraded because of the repair costs, which increase considerably with age relations Lussier (1961), Tufts (1982) and Drinkwater (1967).\n\nEquipment replacement methods\n\nThe replacement method of Ricardo (2017). Contrary to many works encountered in the literature Fraser (2000) and Jardine (2017) where studies of replacement and reform of equipment use the year as a period, Ricardo (2017) and his collaborators find that the month is the most appropriate Lussier (1961) and Tufts (1982). Indeed, using the year as a period could be too broad for some, such as the category “small construction equipment” for example. The mathematical model was built on the work of Tufts (1982) who proposed an approach based on a solid theory of investment. This model supports decision making on replacement age, but can also be used to make a decision where the options are either to replace, repair, or choose from different machine options. Working capital and risk premiums are included in the model proposed by Ricardo (2017); this model will help determine the optimal replacement period, as the following equation describes:\n\nFraser (2000)\n\nWith\n\nn = Optimal replacement period (months); 1≤ n ≤ N\n\nMCRFn = Monthly Capital Recovery Factor\n\nH0 = Initial investment during period zero\n\nK = Number of loan installments\n\nPPi = Principal repayment in period i\n\nP(i) = Monthly discount factor\n\nMVn = Expected market value of the asset in period n\n\nLBk = Loan balance at the end of period k\n\nNOPATi = Net operating income after tax for period i\n\nIn this model, the company buys the equipment using a mix of debt and equity. Debt will be assumed to have an annual percentage interest rate, while equity will be hedged using an expected minimum acceptable rate of return. Thus, the model is designed to determine a period during which the replacement maximizes the benefits generated by the equipment.\n\nThe Annual Value Method\n\nThe annual value method With this method developed by Blank (2005), the economic lifetime of an item of equipment is the number of years n, during which the equivalent annual uniform cost Blank (2005). Engineering is a minimum, taking into account the most recent cost estimates over all possible years of asset lives. One methodology to approach this analysis is to acquire data from as many pieces of equipment (of the same type) as possible and obtain the averages for these expenses. To do this, a linear regression model can be used to determine for each component the costs expected over the number of years of life of the equipment. This way of approaching the problem considers the age of the hardware as the independent variable. Another linear regression can be used to determine the expected number of failures per period of year n. Again, the age of the equipment is taken as the independent variable. For the specific case of opportunity cost due to catastrophic failure, it can be calculated based on the probability of catastrophic failure. If we consider that the age of equipment follows a normal distribution with a given mean and standard deviation, the cumulative probability of catastrophic failure will increase with age. To find the minimum useful life cost, one increases the useful life value, called k, from 1 to the maximum expected value for the asset N, i.e., k = 1,2, 3, …N For each value of k,\n\nThe CAUEk value is calculated using the following formula from\n\n(Blank, 2005)\n\nWhere VSk\n\nCAOj : annual operating cost during year j (j = 1, 2, …,k)\n\nThe following must be transformed into annual values for each number of years, the equipment is studied, for an interest rate i:\n\nThe cost of acquiring the equipment Operation and maintenance costs (major maintenance cost, opportunity cost, opportunity cost for catastrophic failure)\n\nThe salvage value at year n\n\nThe salvage value at the end\n\nThese expenditures in annual value will then be added together to obtain a value of the CAO annual operating cost for each year.\n\nThe annual capital recovery value must then be added to the CAO to obtain the Economic Service Life (ESL). The optimal replacement time would be when the ESL is at a minimum.\n\nNB: Any cost that does not change with the age of the equipment (such as the cost of labor in certain special cases) should not be included in the calculation.\n\nGrant’s Model (1950)\n\nThe Terborgh cost reduction model was streamlined by Grant (1950). He talks statistically about the issues of when to replace outdated equipment due to inadequacy, unnecessary maintenance, obsolescence, and deteriorating efficiency. He provides methodologies for studying and addressing the economic replacement problem when management is confronted with any one of the following three constraints:\n\n(i) When a piece of equipment that is more efficient is introduced before the old one is replaced.\n\n(ii) When the cost of goods remains constant across their useful lives\n\n(iii) When the equipment’s yearly running expenses aren’t declining.\n\nDean’s Model (1951)\n\nDean (1951) put up a replacement strategy for equipment that is ideal. For the first time, one author specifically called for the corporation to make a capital investment in equipment replacement in order to compete with other investment options. In order to do this, he made the case that alternative capital investment options, whether they include the replacement of equipment or any other type of expenditure, should be evaluated using the return on investment. As a function of equipment age, the total cost of current equipment is calculated as the sum of applicable operating expenses and capital waste costs. When the total cost is equal to the average yearly cost of the new equipment plus the annual return on the capital investment for the new equipment minus one, replacement takes place.\n\nEmpirical Review\n\nHossam et al. (2023) looked at the relative weight of the many variables affecting the resale value of construction machinery. On average project expenses and the profit made from the usage of construction equipment were shown to be significantly impacted by equipment replacement laws. Equipment managers must properly predict the equipment’s resale value in order to make the best decisions on equipment replacement. The aim of this study is to pinpoint the factors that influence construction equipment’s resale value. These factors were ranked in order of how much of an impact they had on the equipment’s resale value using statistical analysis and the analytical hierarchy process (AHP).\n\nA research on the creation of equipment replacement periods that took wear and tear and obsolescence into consideration was conducted by Lapkina and Malaksiano (2023). The study’s goal is to create techniques for scheduling the best times to replace worn-out equipment with newer, more sophisticated equipment while taking into consideration the degree of variability in the potential values for equipment performance indicators. The following goals were met in order to complete the goal: to conduct a quantitative estimation of the level of uncertainty of performance indicators depending on the choice of the service life of old and new equipment, to justify the selection of equipment performance assessment criteria when switching to a new type of equipment, to determine a multi-criteria estimate of average values and degree.\n\nOptimum replacement time for a deteriorating system was studied by Rani and Sukumari (2014). In the study, the preventive replacement technique for figuring out the best time to replace a component of an automobile system that degrades with time is presented and explored, while the component is nearing the end of its useful life. The optimal replacement time may be calculated since the failure time follows the Weibull distribution. This period reduces overall downtime and increases the percentage of time that each component of the system is operational. The study did not look into the factors that influence equipment degradation and replacement.\n\nWei, Randy, and Mason (2012) investigated “Equipment replacement optimization: dynamic programming used approach.” The primary goals of this study were to give a deterministic dynamic programming (DPP) based optimization model formulation and to suggest the Bellman and Wagner methods to the equipment optimization (ERO) problem. With or without taking yearly budget concerns into account, the established solution approach may be utilized to determine the best keep/replacement choices for both new and used automobiles. To explain and walk through the Bellman DDP solution procedure, a straightforward numerical example was provided. This example showed how the DDP may be used to manually solve the cost reduction ERO problem via backward recursion. Using the most recent Texas Department of Transportation data, the created DDP-based ERO program was evaluated and verified.\n\nAccording to Srinivasan, Francis, and Purushothaman (2014), gasoline will run out within a few years and will cost more money every day. The exhaust is a further issue with current fuel usage. The exhaust contains NO, CO, CO, SO, lead, and other particles that have a negative impact on human health and cause air pollution on a two-fold scale. In order to save money and protect the environment, it is crucial to use alternative fuels.\n\nNatural gas, according to Ubani and Ikpaisong (2018), is a safe, clean-burning fuel that may minimize your gas station expenditures while simultaneously promoting environmental protection and reducing Nigeria’s dependency on foreign oil. It is a naturally occurring mixture of gaseous hydrocarbon, non-gaseous non-hydrocarbons, and gaseous non-hydrocarbons that is present in subsurface reservoir rocks and may be found either on its own (non-associated gas) or in association with crude oil (associated gas). Natural gas is currently regarded as one of the best energy sources for the world and the future since it is more environmentally friendly than other kinds of fossil fuels. Nigeria enjoys her top position as the most populous nation in Africa and the country with the seventh-highest natural gas reserves in the world.\n\nIn Nigeria, the following gas uses are now possible:\n\n1. Gas to electricity and gas to reinjection systems\n2. Petrochemicals using gas as a feedstock\n3. Liquefied Natural Gas, or LNG\n4. Liquid petroleum gas, or LPG\n5. Compressed natural gas, or CNG.\n\nThe usage of Compressed Natural Gas (CNG) as vehicular fuel in Nigeria, according to Ubani and Ikpaisong (2018), offers a variety of advantages, with the economic benefit receiving special attention. CNG is made by compressing natural gas to a tenth of the volume that it takes up at regular atmospheric pressure. In order to calculate the cost savings from using CNG instead of PMS, a thorough economic study was conducted using the example of a driver who travels 100 km per day on average during the roughly 30 days that make up a month. The results showed that switching a car from PMS to CNG may save N1,143 per day and N34,290 per month. The expense of doing so is recouped before the end of the sixth month.\n\nFor instance, the International Energy Agency (IEA) proposed in May 2012 that global demand for natural gas could rise more than 50% by 2035, from 2010 levels. The Intergovernmental Panel on Climate Change (IPCC) states that in order to prevent drastic effects of global warming, global greenhouse gas (GHG) emissions must be cut by 50 to 80 percent by 2050. We need to develop every economically feasible energy source in order to fulfill the rising energy demand and reduce GHG emissions. We all know that no one energy source is sufficient to address the world’s rising energy demands.\n\nIn the words of Ogunlowo (2016), a more varied energy mix is also necessary to provide energy security and combat climate change. We must create all feasible and ecologically responsible energy sources in order to meet this social need that is only going to increase over time. There is natural gas accessible to provide the world with a practical substitute because it is the cleanest fossil fuel. Its availability, dependability, adaptability, and quantity will all be important. The study comes to the conclusion that a number of obstacles have made it difficult for CNG to be used as a fuel for road transportation in Nigeria. This is supported by the case study’s insights, the semi-structured interviews’ participants’ consistent responses, and the results of the Delphi survey. These factors are: Insufficient focus, unconducive energy market structure, limited access to funding, weak transportation market structure, weak institutions for vehicle standards enforcement and low level of public awareness.\n\nIn his 2005 study, Redner looked at freight transportation firms’ vehicle replacement planning. The strategy for establishing the best replacement policy for the vehicles used by a freight transportation business is presented in this article. The equipment replacement cost minimization model is used in the investigation.\n\nThe study took into account the utilization intensity (annual mileage) of vehicles in consecutive years of their operational lives, the technical durability of vehicles (such as maximum mileage), as well as various methods of financing the fleet investments (buying with cash, renting, or leasing), in order to apply the minimal average cost replacement policy to the vehicles whose utilization intensity decreases over time. The issue was stated in terms of one criterion, which was linear, deterministic, and\n\nStatistical quality control (SQC) and partly observable Markov decision processes (POMDP) were combined by Ivy and Nembhard (2005) for use in determining maintenance decisions for failing systems. They defined the observed distribution for the POMDP modeling in their study by using SQC to a sample of a real-world system. In their study, simulation approach was used to combine SQC and POMDP to generate and evaluate maintenance policies in relation to process features, system operating costs, and maintenance expenses.\n\nIn their study on “Development of vehicle replacement programme for a road transport company,” Offiong, Akpan, and Ufot (2013) covered a workable replacement strategy for a transport company. To identify an appropriate replacement interval for different groups of vehicles, an annual maintenance schedule and depreciation costs were developed, and a replacement model that takes the value of money into account was implemented. For the purpose of establishing fair prices that allow for a profit margin and a successful vehicle replacement program, a fare model was developed. According to the data, the planning horizon is indefinite and the replacement model implies that the firm under study replaces cars with similar vehicles. With an identical vehicle and an indefinite planning horizon, a vehicle may be replaced at any age.\n\nIn their 2014 study, Ajibade, Odusina, Rafiu, Ayanrinde, Adeleke, and Babarinde looked at the application of the replacement model to decide when to replace aging industrial equipment. They tried to pinpoint the precise moment that instrument replacement is most cost-effective. The information utilized in the article is all about the price of repairing a 250 KVA Mikano generating plant, which was created by the works and services department of the Polytechnic of Ibadan’s Adeseun Ogundoyin campus in Eruwa. The replacement model accounts for objects whose maintenance costs rise with time while ignoring variations in the value of money over the research period. The decision variable, or average equipment cost, was manually calculated. According to the analysis’s findings, by the fifth year.\n\nThe impact of degradation on resale value was examined by Ekeocha, Odukwe, and Aguwamba in 2011. Front-end loaders, CAT 140H motor graders, capsule fillers, injection mover machines, Mercedes Benz express buses, and Hiace minibuses all had maintenance expenses and salvage values that were recorded. Using the Monte Carlo simulation with the uniform probability distribution, values of degradation were produced as random numbers. The procedure of dynamic programming enumeration was used as a solution strategy. Field data from several industries were used to validate the model’s outputs once it had been calibrated. Finally, the model’s output was contrasted with output from other models. Basically, the findings indicate that, for construction machinery, the ideal replacement days are between 4 and 6 years, and 16 and 20 years, when corrective maintenance is performed regularly and on schedule.\n\nThe ideal lifestyle limit for a fleet of freight vehicles is derived by Redmer (2005) and exhibits declining use as equipment ages and stable utilization levels within age classes. His methodology is founded on Eil’s LCCA strategy.\n\nBuddhakulsomsiri and Parthandee (2006) published a second research that emphasizes the value of lowering equipment use over equipment age. Their approach is based on Hartman (1999). Utilization in Hartman’s model is defined as a decision variable, however it is not in the research of Buddhakulsomsiri and Parthandee (2006). Utilization is a model parameter since it is expected that utilization for each age class is constant. The usage level assumptions stated by Redmer (2005) and Buddhakulsomsiri and Parthandee (2006) are the same. Furthermore, decreased consumption may result from a dependent use pattern, according to Buddhakulsomsiri and Parthandee (2006). “Since there are numerous vehicles that can provide the same service or fulfill the same purpose, it is the newer ones that are more effective.” Given the rapid development, many studies have attempted to understand reasons why alternative to fuel energy should be considered. Alternative to fuel adoption include CNG, BEV etc. Fleet managers should consider these options for social, economic and environmental impact the decisions hold Zhenhus, Andre, Christiana, & Wei, (2022).\n\nFigure 1: Adoption of BEV as an alternative to fuel.\n\nZhenhus, et. al. (2022).\n\nResearch Gap\n\nWhile there has been significant research conducted on vehicle fleet management and equipment replacement strategies, there are still several research gaps that exist when it comes to identifying the optimum equipment replacement of vehicle fleet. For instance Inegbedion & Osifo (2014) conducted a research on overloading and replacement of vehicle fleets of commercial transport companies in Nigeria. Another research on this area of study include Aghedo & Inegbedion (2015), who emphasized overloading as a major constraints for vehicle replacement time. However, overloading is not sufficient to be a major constraints any longer as a result of fuel subsidy removal, and with fuel now accounting for a major percentage of vehicle operating expenses in Nigeria.\n\nThis study, seek to find an optimum replacement for vehicle fleet taking into consideration cost of fuel and possible alternatives transport companies can adopt other than vehicle using fuel for energy. The transport sector in Nigeria plays a pivotal role in the country’s economic development by facilitating the movement of goods and people. However, the sector faces multifaceted challenges that impact its efficiency and sustainability. Two critical factors, overloading and fuel cost, have historically posed significant constraints on the operation of vehicle fleets within the sector. While overloading has been a formal constraint in many of the research conducted in the transport sector, due to safety concerns and road deterioration Inegbedion & Osifo (2014), the recent removal of fuel subsidies in Nigeria has introduced a new dimension, making fuel costs a crucial consideration in fleet management decisions. Hence, this research work aims to arrive at an efficient fleet management strategy given the increment in fuel cost and possible alternatives to ensure transport company break-even.\n\nAlso, the study extends the scope of other research beyond the traditional routes, Benin-Lagos, Benin-Port Harcourt, and Benin-Abuja. This research has the potential to contribute to a more comprehensive understanding of trade and transportation dynamics in West Africa. By examining routes that have received less attention, Ghana, specifically focuses on the less explored routes connecting these two nations.\n\nFurthermore, previous research in this area of study has used data from God is good motor as a case study from 2009 to 2013. This study accessed the same data set from 2009 to 2022. This is to ensure a more comprehensive analysis and to take advantage of data availability, validation, robustness, and generalizability of previous research findings.\n\n### METHODOLOGICAL REVIEW\n\nThe type of model was based on the criteria established in advance, the best known of which are based on the lifespan of the equipment and the repair costs Kriett (2009). Most of these models consider economic or financial criteria Dietz (2001). They are effective for any business that is aiming for a particular criterion. They are very precise and give equipment managers specific leads on specific problems related to renewal. Although they lead to very relevant results in terms of contributions to lowering costs or increasing profits, they do not take into account other variables involved, such as the environment and technology. The models of Sarache, Castrillon, Gonzales and Viveros (2009) had better take into account the realities of the company.\n\nModelling equipment replacement is necessitude by the deteriorating nature of most equipment. This deteriorating nature precipitates downtime and failure rates of equipment with age. This explains why some of the equipment replacement models are consistent with modeling of equipment’s time to failure. Rani and Sukumari (2014) used Weibull distribution to show the failure rate of a deteriorating system which is the basic characteristics of most equipment. The Weibull distribution gives the distribution of lifetimes of objects. Such a distribution helps to explain or give some hint on the reliability of equipment with time.\n\nMost of the previous studies on equipment replacement either modeled equipment replacement as a stochastic process or as a Markovian deterioration or using Bayesian Inference. Others utilized forecast horizons or network simulation models; while majority of the studies used dynamic programming techniques. The preference for dynamic programming is underscored by its ability to generate greater alternatives at the point of decision making than any other method (Arvore, 2005).\n\nBellman (1955) first considers that there are two potential courses of action at each time t before constructing a dynamic programming model for replacing equipment. Either a machine is preserved for a longer amount of time or it is replaced with a machine that was purchased. In the case that the machine is retained, f(t) = fk (t), where: He defines f(t) as the total returns from a machine of age t, using an optimum replacement policy.\n\n\\[ f_k(t) = r(t) – u(t) + a f(t+1) \\tag{2.1} \\]\n\nWhere: \\( r(t) \\) = Output-return function \\( u(t) \\) = Maintenance cost function \\( a \\) = Discount factor\n\nIn the second case, where \\( f(t) = f_k(t) \\), we have:\n\n\\[ f_p(t) = s(t) – P + r(0) – u(0) + a f(1) \\tag{2.2} \\]\n\nWhere: \\( s(t) \\) = Salvage value function \\( P \\) = Acquisition cost\n\nHence, the expression for \\( f(t) \\) is:\n\n\\[ F(t) = \\max \\left[ r(t) – u(t) + a f(t+1), \\; s(t) – P + r(0) – u(0) + a f(1) \\right], \\quad t \\geq 0 \\tag{2.3} \\]\n\nThe value \\( t_0 \\) that maximizes \\( f(t) \\) is found through substitution involving \\( r(t) \\), \\( u(t) \\), \\( P \\), and \\( a \\).Bellman also presented extensions of dynamic programming technique to the problems of replacement of average machines and technological improvement. For the over-aged machine whose age (T) is greater than t0, is modified to obtain maximization for T ≤ t.\n\nEquation (3) is for maximization problem. A variant of this equation is the cost minimization equivalent given by:\n\n\\[ f_n(t) = \\min \\left\\{ C_n(x) + f_{n-1}(t – x_n) \\; \\middle| \\; x \\in \\{1, 2, 3, \\dots \\}, \\; t = 0, 1, 2, \\dots, T, \\; t – x_n \\geq 0 \\right\\} \\tag{2.4} \\]\n\nWhere: \\( C(x) \\) = Cost of equipment from year 1 to year \\( x \\) (Sniedovich, 2002)\n\nThe net cost of owning equipment over \\( x \\) years is:\n\n\\[ C_n(x) = C_p + \\sum_{i=1}^{x} M_i + \\sum_{i=0}^{x} R_i – S_i \\tag{2.5} \\]\n\nBecause determining the unknown function V—the value function—is required to solve the Bellman equation, it is categorized as a functional equation. Remember that the value function, as a function of the state x, describes the greatest feasible value of the aim. The function a(x) that characterizes the ideal course of action as a function of the state is known as the policy function, and it may be discovered by computing the value function. The value function and policy function, respectively, are the functions that explain the optimal value as a function of the state and the ideal action as a function of the state. The notion of optimality, according to which subsequent decisions always form an optimal strategy for the states emerging from the original decision, regardless of the beginning state and the initial decision, is the core strength of Bellman’s model. As a result, a complicated problem may be divided into simpler problems and solved one at a time. As a result, we gain the value function—also known as the policy function—that specifies the greatest potential value of the aim as a function of the state. Because it provides a considerably wider variety of options than the conventional approaches at the time of decision-making, the Bellman’s model has an advantage over other models (Arvore, 2005). To examine the short run and the long run relationship between gasoline subsidized price and transport sector, this research employed the co-integration and Error Correction Methodology (ECM). The Co-integration approach provides information about the long run relationship between the variables while the Error Correction Method (ECM) provides information about the short-run relationship between the variables. The error correction term provides information on the speed of adjustment from the short run disequilibrium to the long run equilibrium in the event of any deviations from the long run equilibrium (Arvore, 2005).\n\nValue Iteration Method\n\nIn simple form, the value iteration Function in Dynamic Programming Model is given by the Bellman equation:\n\n\\[ F_n(Q) = \\max \\left[ f_n x_n + f_{n-1}(Q – X_n) \\right] \\tag{2.6} \\]\n\nThis equation (6) is for a maximization problem. For minimization problem, the value iteration function is given by:\n\n\\[ F(t) = \\min \\left[ C(x) + f(t – x) \\right], \\quad x \\in \\{1, 2, 3, \\dots, k\\}, \\quad t = 1, 2, 3, \\dots, T, \\quad k \\leq T \\tag{2.7} \\]\n\nEquation (6) and (7) are variants of equation (3) and (4).\n\n### METHODOLOGY\n\nIntroduction\n\nBasically, the study sought to investigate the optimum replacement time of vehicle fleets with cost of fuel constraints of the best private commercial transport company in Nigeria. GIG mobility located in Benin- City. Edo State.\n\nThe study is consistent with the goal of the replacement theory for deteriorating items, which seeks to replace equipment or equipment parts in a manner that minimizes economic loss. It confines itself to the cost model of equipment replacement. The operation of the best private transport company in Nigeria GIG mobility for the period of 2009 – 2022 was chosen for the study. The focus was on some vehicles plying three main routes ( Benin-Lagos, Benin- Abuja, and Benin – Port –Harcourt\n\nResearch Design\n\nThe research design is mixed, being a combination of a field survey of interstate transport companies in Benin City and a longitudinal study of vehicle fleets of a randomly selected transport company in Benin-City, Nigeria. The vehicles consisted of Toyota Hummer buses with a carrying capacity of fourteen passengers each, plying three major routes in Nigeria: Benin-Lagos, Benin-Port Harcourt and Benin-Abuja.\n\nPopulation of the Study\n\nThe population of the study include all inter – state transport companies in Nigeria. The vehicles consisted of Toyota Hummer buses with a carrying capacity of fourteen passengers each, plying three major routes in Nigeria namely Benin- Lagos, Benin- Port-Harcourt, and Benin to Abuja.\n\nSample and Sampling Technique of the Study\n\nTo capture the nitty-gritty of the objective of the study, Multi-stage sampling technique was adopted. Firstly, Benin City was randomly chosen out of the thirty six (36) state capitals and the Federal Territory, next the sampling frame of the interstate transport companies in Benin City was obtained from a field survey (see table 3.1). Thereafter, simple random sampling (lottery method) was used to select God is Good Motors Company (GIG mobility). GIG is renowned for providing quick and safe services. The organization is recognized to operate a transport business, which considerably complements its logistics activities. They also have extremely effective transportation facilities (Rafiu, 2022). The GIG mobility which was formerly referred to as God is good motors ply Benin-Lagos, Benin-Port Harcourt, Benin-Abuja, Benin –Bayelsa, Benin – Ekpoma, Benin – Umuahia. However, three routes were studied. Simple random sampling (lottery method) was used to select these three routes – Benin – Lagos, Benin-Port Harcourt, Benin-Abuja.\n\nTable 3.1 List of Major Interstate Transport Companies in Benin City\n\n| S/N | TRANSPORT COMPANY | S/N | TRANSPORT COMPANY |\n| --- | --- | --- | --- |\n| 1 | Ameosa Line | 2. | Assoicated Bus Company Limited |\n| 3. | Chase Travels & Tour | 4. | Greener Line Transport |\n| 5. | Big Joe Ventures Ltd | 6. | Julglad Travels & Tours |\n| 7. | Muyi Line Transport | 8. | Sefgetin |\n| 9. | Zumalex Nig. Enterprises | 10. | Eco Bus |\n| 11. | Edegbe Line | 12. | EFEX Executive |\n| 13. | Efosa Express | 14. | Eke Line |\n| 15. | Kings Motor | 16. | Faith Motors |\n| 17. | God’s Time Motors | 18. | God’s speed Motors |\n| 19. | Iyare Motors | 20. | Magorowa Motors |\n| 21. | Ohonba Line | 22. | Osayame Line |\n| 23. | Ulo Motors | 24. | Bob Izua Motors |\n| 25. | Faith Motors | 26. | Continental Corporate Logistics |\n| 27. | Iyayi Brothers Nigeria Limited | 28. | Amma Transport Sers. Ltd |\n| 29. | Edo Municipal Transport Service | 30. | J.B.S Transport & Supply Company |\n| 31. | Takwas Inter Transport Company | 32. | De Modern Motors |\n| 33. | Discoop Motors | 34. | Edobor Line |\n\nSource: field survey, 2023\n\nSources of data\n\nThe data used in the study were primary and secondary sources. Primary data were obtained on the major interstate transport companies in Nigeria, GIG mobility from a field survey while the secondary data were obtained on the life cycle cost( purchase, running and operating cost) of buses from the private Transport Company under investigation.\n\nBesides, This study investigates the connection between the Nigerian economy’s transportation sector and gasoline price subsidies. The Nigeria National Petroleum Corporation (NNPC) statistics bulletin and Ismail et al. (2014) works provide information on gasoline subsidy prices. Additionally, information regarding the growth of the transportation industry was gleaned from Okezie et al. (2014).\n\nModel Specification\n\nThe replacement models developed in several empirical studies (Ahmed, 1973; Arvore, 2005 ; Edward, 2008) hypothesize that vehicle replacement time is a function of total cost in the system.\n\nThe mathematical formulation of the vehicle replacement problem adapted from (Inegbedion, 2014) is given as :\n\nMinimize\\[ \\min \\sum_{j=1}^{L} C(X_j) \\tag{3.1} \\]\n\nSubject to\\[ \\sum_{j=1}^{L} X_j = T \\tag{3.2} \\]\n\nWhere:\n\nC(Xj) =ne cost of owning equipment over T years; and\n\nXj = components of time periods.\n\nAccording to the study’s goal, the research’s starting point was the value iteration function, which was followed by a sensitivity analysis of cost fluctuations brought on by environmental elements in the transportation system. The replacement models created in various empirical investigations (Ajibade, Odusina, Rafiu, Ayanrinde, Adeleke & Babarinde, 2014; Arvore, 2005; Edward, 2009; as well as Offiong, Akpan, & Ufot, 2013).\n\nThe model specifically hypothesizes that the total system vehicle costs are a function of vehicle replacement time. The Bellman equation (Bellman, 1955) describes the recursive value connection of the value iteration function of the dynamic programming model for a maximizing issue.\n\n\\[ F_n(t) = \\max \\left( f_n x_n + f_{n-1}(t – x_n) \\right) \\tag{3.3} \\]\n\nThe cost minimization equivalent of the equation\n\n\\[ F_n(t) = \\min \\left\\{ C(x_n) + f(t – x_i) \\;\\middle|\\; x \\in \\{1, 2, 3, \\dots\\}, \\; t = 1, 2, 3, \\dots, T; \\; t – x_i \\geq 0 \\right\\} \\tag{3.4} \\]\n\nWhere:\n\n1. Fn(t)= minimum net cost over period of t years, given that we start with a new machine (t = 1, 2, 3…, T);\n2. C(Xn) = cost of keeping a vehicle for x years;\n3. F(t-xn) = optimal cost of dealing with the remaining (t –x) years. That is, the cost of keeping a machine for (t – x) years after keeping for x years at a cost of C(x);\n4. \\( F(0) = 0 \\), and \\( f(1) = C(1) \\)\n\nNet cost t(1) of keeping a vehicle at first period is equal to cost of keeping a vehicle C(1) Fn the first period f(1) = C(1)\n\nTherefore, the net cost of owning equipment over T years is given as follows:\n\n\\[ C(x_i) = C_p + \\sum_{i=1}^{n} M_i + \\sum_{i=0}^{n} R_i – S \\tag{3.5} \\]\n\nEquation (5) = \\[ C(x_i) = \\C_{i=1}^{n} M_i + \\sum_{i=0}^{n} R_i + C_d \\] since \\( C_p – S = C_d \\) (3.6)\n\n-\\[ C(x_i) = \\sum_{i=1}^{n} M_i + \\sum_{i=0}^{n} R_i – C_d \\] as the new equation. (3.7)\n\nWhere:\n\nC(x) = total cost (operating cost + depreciation cost);\n\nCp= cost of purchase of new equipment;\n\nMi = maintenance cost for a period of time;\n\nRi= running cost for a period of time\n\nCd = Depreciation Cost;\n\nS = Salvage Cost;\n\nDynamic programming model used to formulate the problem is with Typescript and JavaScript as the modeling language implementation platforms.\n\nTo examine the relationship between subsidized gasoline price and transport sector, this study adopted the multifactor neoclassical production function framework, the model is expressed as:\n\nTr = f ( SPP, UPP, PQ) (3.9)\n\nWhere Tr = Transport sector’s output\n\nSP = subsidized petroleum price\n\nUPP = unsubsidized petroleum price\n\nPQ = PMS sales per litre\n\nSpecifying equation (3.9) in an exponential form, we have:\n\nTr = λoSPPβ1UPP β2PQβ3 eεt (3.10)\n\nLinearizing equation (3.10),\n\nlnTr = lnλo + β1lnSPP + β2lnSPP + β3lnPQ + et\n\nApriori expectation: β1, β2, β3\n\nIn the road transportation sector, traffic flow and the resulting congestion are important sector output. It is significant because it has repercussions for the local and global economies upstream. Many of these effects, including easier access, increased goods movement, increased trade, and increased investment, are positive when traffic flow increases smoothly. There is a threshold, though, beyond which increased traffic flows start to exacerbate congestion. This has a different set of effects, including increased wear and tear on the vehicle that necessitates equipment replacement sooner (Mogarita, 2004), increased fuel consumption, increased travel time, and generated pollution. λo, is intercept, β1 to β3 are the slope of the coefficients of the independent variables to be determined where εt is the error term at time t. Equation (3.10) is the long run regression equation to obtain the long run relationship between the variables. In order to estimate the short-run relationship among variables in equation 3.10, the corresponding error correction equation is estimated as follows:\n\n(3.11)\n\nThe ECMt-1 is the error correction term of the short run equation (3.12)\n\nThe ECM in equation 3.10 is the error correction mechanism which indicates the speed of adjustment to equilibrium whenever disequilibrium occurs in the transportation system in Nigeria (Ochei & Mamudu, 2020).\n\nOperationalization of Variables\n\nThe study investigates the vehicle replacement time of GIG mobility in Nigeria. The variables of the study are operationalized below:\n\nOperational Measurement of Replacement Time\n\nThe replacement time is placed on ratio scale. This is consistent with the measurement procedure used by Chang (2005) and Terborgh (1958).\n\nOperational Measurement of Total Cost\n\nThe study utilized actual data on purchase price of vehicles, depreciation, maintenance cost and running costs of randomly selected vehicle which is Toyota Hummer Bus. This is necessary because of the excessive data involved in trying to establish the detailed maintenance and running cost record of all vehicles used by the Company. This approach will be applied uniformly to the three vehicle groups since all the vehicles in the same group are of the same make and ply the same route and can be said to operate under the same condition.\n\nOperational Measurement of Cost of fuel as a constraint\n\nData on fuel-subsidized price is from the Nigeria National Petroleum Corporation (NNPC) statistical bulletin. Also, data used for transport sector development are obtained from GIG mobility. To examine the short run and the long run relationship between fuel subsidized price and transport sector, this research will employ the co-integration and Error Correction Methodology (ECM). The Co-integration approach provides information about the long run relationship between the variables while the Error Correction Method (ECM) provides information about the short-run relationship between the variables. The error correction term provides information on the speed of adjustment from the short run disequilibrium to the long run equilibrium in the event of any deviations from the long run equilibrium.\n\nAssumptions\n\n1. Only new vehicles are used for replacement.\n2. The depreciation method for all the vehicles is reducing, 25% for Benin – Lagos route and 40% for Benin-Abuja and Benin – Port-Harcourt route.\n3. Vehicle deterioration increases maintenance cost.\n4. Vehicle plying the same route are homogenous and thus have the same characteristics (life cycle costs and depreciation)\n5. Vehicle operates 300 days per year.\n6. Salvage Value is calculated once in asset life cycle.\n7. Cost of fuel is differentiated in terms of subsidized price and unsubsidized price.\n\nEstimation Technique\n\nDynamic programming was utilized to estimate the ideal time to replace cars based on life cycle costs and the study’s objectives. In the model, replacement time is the dependent variable, while the running, maintenance, and depreciation costs of the vehicle are the independent variables. Since all cost components are quantified in terms of money, they all come under the ratio scale. The equipment replacement optimization (ERO) issue is formulated using dynamic programming, and the data structure is created before being implemented using Java script and Pascal programming. The operating cost and maintenance cost for equipment based on model year were calculated, and the purchase price was utilized to calculate the depreciation cost. the total cost of operation and depreciation combined. The entire cost was then calculated by adding the cumulative operating cost and the cumulative depreciation. The Pascal Program read and processed the complete cost information that was gathered for the research. Thus, the annual total cost of automobiles served as the primary input. To store and solve the data, many dynamically allocated arrays were created. The Bellman’s technique was gradually solved, and the recursive function was successfully invoked. The predicted total vehicle cost, which is a function of the purchase price, yearly operating cost, maintenance cost, and salvage value, is what the DPP solution software uses to optimize the ERO decisions over a specified time horizon. In other words, the DDP technique assumes that these costs/values are fixed or predefined over the study time.\n\nJustification of the Estimation Technique\n\nDynamic programming was the study’s model of choice. The equipment replacement time was specifically determined as a function of the life-cycle expenses (buy cost, maintenance and repair cost, and operation cost) using the discrete dynamic programming model (Value iteration). The dynamic programming approach was chosen because, compared to other conventional ways of equipment replacement, it provides a significantly wider variety of possibilities at the time of decision-making (Arvore, 2005). Because the study’s pertinent inputs (buy cost, depreciation, maintenance cost, and running cost) are assessed at discrete points in time, the discrete dynamic programming model was used.\n\nBeing A Thesis Proposal Presented To The Department Of Business Administration, In Partial Fulfilment Of The Requirements For Award Of Doctor Of Philosophy (Ph. D) In Business Administration Of The University Of Benin, Benin City, Edo State.\n\n### REFERENCES\n\n1. Alaa, N. E. (2009). Simplified approach for optimization by dynamic programming. International Journal on Environmental Hydrology, 6, 1-14.\n2. Adkins, R., & Paxson, D. (2017). Replacement decisions with multiple stochastic values and depreciation. European Journal of Operational Research, 257 (1), 174–184.\n3. Adkins, R., & Paxson, D. (2013). Deterministic models for premature and postponed replacement. Omega, 41 (6), 1008 – 1019.\n4. Ahmed, B. S. (1973). Optimal equipment replacement policy. 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(2019). Understanding urban delivery drivers’ intention to adopt electric trucks in China. Transportation Research Part D: Transport and Environment, 74, 65 – 81.\n\n### APPENDIX 1\n\nList of the major Interstate Transport Company in Nigeria.\n\n| S/N | TRANSPORT COMPANY | S/N | TRANSPORT COMPANY |\n| --- | --- | --- | --- |\n| 1. | ABC MOTORS | 19. | EKILI HAULAGE |\n| 2. | ALIBE & SONS TRASNPORT COMPANY | 20. | FAITH MOTORS |\n| 3. | AMMIRE HAULAGE | 21. | GIG MOBILITY |\n| 4. | AMOS TRAVELS | 22. | GUO TRANSPORT |\n| 5. | ASTAC NIGERIA LIMITED | 23. | LIBRA MOTORS |\n| 6. | AUTOSTAR TRAVELS | 24. | MARVEL SERVICES |\n| 7. | BENUE LINKS NIG LTD | 25. | MEDITERRANEAN SHIPPING COMPANY NIGERIA LIMITED |\n| 8. | BIG JOE MOTORS | 26 | METRO FERRY |\n| 9. | BLUE CHEETAH SERVICES | 27. | MUSHILAB NIGERIA LIMITED |\n| 10. | BONNY WAY MOTORS NIG. LTD. | 28. | MUYI LINE |\n| 11. | CHISCO TRANSPORT LIMITED | 29. | NOBLEPAT GROUP |\n| 12. | CROSS COUNTRY | 30. | OSTAR-KEN INTERNATIONAL COMPANY |\n| 13. | CRYSTAL LINE | 31. | PEACE AND JOY TRANSPORT |\n| 14. | DE MODERN MOTORS | 32. | SAFE MOTOR WAY |\n| 15. | EAGLE LINE | 33. | SAIMA NIGERIA |\n| 16. | ECO LINE | 34. | TORON NIGERIA LIMITED |\n| 17. | EDEGBE MOTORS | 35. | TOTAL LOGISTICS EXECUTIVE RYDE LTD |\n| 18. | EFEX EXECUTIVE | 36. | TRIBEL GLOBAL MOTORS |\n\nSource: field survey, 2023\n\n## GET OUR MONTHLY NEWSLETTER\n\nEmail\n\nSubmit"
  }
]