# Transport Starter Data Kit: Historical socio-transport data for Nigeria

**Authors**

Naomi Tan 1,2 , Robert Ambunda3, Nikola Medimorec3 Angel Cortez3, Agustina Krapp3, Erin Maxwell1, John Harrison1, Mark Howells 1,2

**Affiliations**

1. Centre for Sustainable Transitions: Energy, Environment and Resilience, Loughborough University
2. Centre for Environmental Policy, Imperial College London
3. SLOCAT Partnership on Sustainable, Low Carbon Transport **Abstract** Data on transport activity is an important element for the development of national transport decarbonisation strategies. By having freight and passenger transport information, the impacts on vehicle and fuel consumption changes from replacing internal combustion engine vehicles with electric vehicles can be calculated. The development of a national decarbonisation strategy requires significant efforts. However, access to data is often a barrier to starting transport system modelling in developing countries, thereby causing delays. This article provides data that can be used to support a model for Nigeria, which may act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, focusing on national reports, statistical yearbooks, and academia. **Keywords** U4RIA Transport data Transport modelling MAED Nigeria

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## Specifications Table

| Subject | Transport |
| --- | --- |
| Specific subject area | Transport Data |
| Type of data | Tables Graphs |
| How data were acquired | Literature survey (databases and reports from international organisations; journal articles) |
| Data format | Raw and analysed |
| Parameters for data collection | Data collected based on inputs required to create an energy system model for Nigeria |
| Description of data collection | Data were collected from the websites, annual reports and databases of international organisations, as well as from academic articles and existing modelling databases. |
| Data source location | Not applicable |
| Data accessibility | With the article and in a repository. Repository name: Zenodo. Direct URL to data: \[ [https://doi.org/10.5281/zenodo.6539979](https://doi.org/10.5281/zenodo.6539979) |\
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## Value of the Data\
\
- The data can be used to develop national transport demand models to inform national investment outlooks and decarbonisation strategies.\
- The data are useful for country analysts, policy makers, and the broader scientific community, as a zero-order starting point for model development.\
- This data could be used to examine a range of possible transport pathways, in addition to the examples given in this study, to provide further insights into the evolution of Nigeria’s transport system.\
- The data can be used both for conducting an analysis of transport activity and emissions, but also for capacity building activities.\
- The data can be used as a call to action in addressing transport data gaps and establishing parameters for data collection to improve the consistency of transport-climate research in these countries.\
\
# 1\. Data Description\
\
The data provided in this paper can be used to support the development of a transport model for Nigeria. The data provided were collected from publicly available sources, including statistical yearbooks, transport ministry reports, statistics from national authorities and affiliated research institutions, academia, and journal articles. Global datasets (primarily from the World Bank) were only consulted if severe data gaps existed. The dataset includes parameters on passenger and freight transport activity, disaggregated by transport mode (road, rail, aviation, etc.) and geographic scale (inter-city or inner-city), if available. The dataset also covers the size of the vehicle fleet, disaggregated by vehicle types. The data coverage and subtypes vary among the parameters. The overall ambition is to include the most recent available year(s).\
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## Item Description of Content\
\
Figure 1 A graph showing total population (million people), as well as the share of\
\
urban and rural population in Nigeria.\
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Figure 2 A graph showing total GDP (million USD in 2015), as well as the share of the\
\
different sectors contributing to GDP in Nigeria: agriculture, construction, mining, manufacturing, service, and energy.\
\
Tables 1 to 3 Tables showing passenger transport activity in Nigeria for the most recent year data was available. The data are curated from national statistics agencies or other government-affiliated agencies.\
\
Table 4 An additional table showing passenger transport activity in Nigeria based on\
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UN DESA Statistics Division data (see explanation below). The data feature information for 2018.\
\
Tables 5 to 7 Tables showing freight transport activity in Nigeria for the most recent year data was available.\
\
Table 8 An additional table showing freight transport activity in Nigeria based on UN\
\
DESA Statistics Division data (see explanation below). The data feature information for 2018.\
\
Tables 9 to 10 Tables showing vehicle fleet data in Nigeria for the most recent year data were available.\
\
For the parameters on passenger and freight transport activity, an additional dataset was included in Table 4 and Table 8. The UN DESA Statistics Division modelled passenger activity and freight activity for every country in support of SDG Indicator 9.1.2 1 . Passenger activity data provide information for road, rail, and air transport. Freight data cover road, rail and inland water, and aviation. The passenger-km and tonnes-km data originate from the Open SDG Data Hub. In this dataset, only the data for International Transport Forum (ITF) (representing mostly OECD countries) and UNECE countries (mostly European countries) are based on national reporting. For non-ITF/UNECE countries, the data are estimated using the ITF model, which uses several covariates such as GDP, population, and transport network coverage. A description of the model can be found in the ITF Transport Outlook 2017.\
\
# 1.1 Population\
\
Population data including total population, population growth, and split by rural or urban was gathered from The World Bank Open Data platform2. Figure 1 displays the total population disaggregated by urban and rural in Nigeria.\
\
1 Freight: [https://www.sdg.org/datasets/undesa::indicator-9-1-2-freight-volume-by-mode-of-transport-tonne-](https://www.sdg.org/datasets/undesa::indicator-9-1-2-freight-volume-by-mode-of-transport-tonne-) kilometres/about ; Passenger: [https://www.sdg.org/datasets/undesa::indicator-9-1-2-passenger-volume-passenger-kilometres-by-mode-](https://www.sdg.org/datasets/undesa::indicator-9-1-2-passenger-volume-passenger-kilometres-by-mode-) of-transport/about [https://data.worldbank.org/](https://data.worldbank.org/)\
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**Figure 1: Total population (million people) disaggregated by urban and rural in Nigeria**\
\
# 1.2 Gross domestic product (GDP)\
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GDP data including total GDP, GDP growth, and GDP share by sector (agriculture, manufacturing, service) was collected from The World Bank Open Data platform2. Where data was not available, data processing was done. Figure 2 shows the total GDP, as well as the share by sector, in Nigeria.\
\
**Figure 2: Total GDP (million USD in 2015) disaggregated by share in Nigeria**\
\
# 1.3 Passenger transport activity\
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Information on passenger transport activity in Nigeria exists for all land transport modes. Three different datasets have been included because each dataset covers information for a different year, and they use different typologies for transport modes. In Table 1, the passenger transport activity for 2010 from a low- carbon energy study is covered. Table 2 shows road transport activity for 2016, published by the\
government of Nigeria. The third table only provides rail transport information, sourced from the World\
Bank.\
\
The low-carbon energy study believes that private cars recorded 268800 million passenger-km, taxis 77800\
million passenger-km, and motorcycles 43300 million passenger-km in 2010. Rail represents the smallest\
contribution to passenger transport activity with just 800 million passenger-km.\
\
Table 1: Estimated passenger transport activity (million passenger-km) in Nigeria\
\
| Mode | 2010 |\
| --- | --- |\
| Motorcycles | 43300 |\
| Private cars | 268800 |\
| Taxis | 77800 |\
| Light buses | 2600 |\
| Coaches | 12000 |\
| Rail | 800 |\
\
Source: Dioha, M. and Kumar, A., 2020, Sustainable energy pathways for land transport in Nigeria, Utilities Policy,\
Volume 64, 101034, ISSN 0957-1787, [https://doi.org/10.1016/j.jup.2020.101034](https://doi.org/10.1016/j.jup.2020.101034).,\
[https://www.sciencedirect.com/science/article/abs/pii/S0957178720300291?via%3Dihub](https://www.sciencedirect.com/science/article/abs/pii/S0957178720300291?via%3Dihub)\
\
Table 2 provides data for 2016 and it disaggregates road transport modes by fuel type (diesel and gasoline).\
This dataset includes light duty trucks because it covers light passenger commercial buses. The total\
passenger transport activity is indicated as 552800 million passenger-km for 2016. Comparing it to the road\
transport data in Table 1, it shows an increase of 36% between 2010 and 2016. Such a steep increase has to\
be seen with caution and it has to be understood that the two datasets underlie different methodologies\
and are not directly comparable.\
\
Further, the information on fuel type shows that 98% of private car activity and 75% of light duty truck\
activity rely on gasoline. The fuel usage of heavy duty buses is equally divided between diesel and gasoline\
usage. The data is part of the Third National Communication of the Federal Republic of Nigeria, and it is the\
basis for future projections on transport development and related greenhouse gas emissions.\
\
Table 2: Passenger transport activity (million passenger-km) by fuel type in Nigeria\
\
| Mode |\
| --- |\
| Cars |\
| Cars |\
| Motorcycles |\
| Light duty trucks(incl. light passenger commercial buses) |\
| Light duty trucks(incl. light passenger commercial buses) |\
| Heavy duty buses |\
| Heavy duty buses |\
\
|  | Sub-type | 2016 |\
| --- | --- | --- |\
|  | Gasoline | 168482.384 |\
|  | Diesel | 3438.416 |\
|  | Gasoline | 28192.8 |\
|  | Diesel | 227200.8 |\
|  | Gasoline | 75733.6 |\
|  | Diesel | 24876 |\
|  | Gasoline | 24876 |\
\
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\
Both datasets only provide a single year. To outline at least the development of rail passenger transport\
activity, the data by the World Bank is included in Table 3. It shows the development from 1990 to 2005.\
The quality of data is concerning in this case because there is very large variation throughout the years.\
\
Table 3: Recorded rail passenger transport activity (million passenger-km) in Nigeria\
\
| Mode | 1990 | 1991 | 1992 | 1993 | 1994 | 1995 | 1996 |\
| --- | --- | --- | --- | --- | --- | --- | --- |\
| Rail | 1269 | 689 | 105 |  | 471 |  |  |\
\
| 1996 | 1997 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 |\
| --- | --- | --- | --- | --- | --- | --- | --- |\
|  | 479 |  |  |  |  | 593 |  |\
\
Source: World Bank, 2022, Railways, passengers carried (million passenger-km) - Nigeria,\
[https://data.worldbank.org/indicator/IS.RRS.PASG.KM?locations=NG](https://data.worldbank.org/indicator/IS.RRS.PASG.KM?locations=NG)\
\
[https://data.worldbank.org/indicator/IS.RRS.PASG.KM?locations=NG](https://data.worldbank.org/indicator/IS.RRS.PASG.KM?locations=NG)\
\
Overall, it shows that there are major data gaps for passenger transport activity data in Nigeria. There are\
no frequent updates, and the data collection does not seem to be institutionalised with clear responsibility\
given to a national authority.\
\
Examining the UN DESA modelled data, it is estimated that road passenger activity in Nigeria is 217953\
million passenger-km in 2018 (Table 4). Rail only represents 190 million passenger-km and aviation 4087\
million passenger-km in 2018. Comparing it to the datasets above, the values for road transport are half of\
the other datasets. Rail data by UN DESA is similar to the World Bank but significantly lower than the\
dataset in Table 1.\
\
Table 4: Modelled passenger transport activity (million passenger-km) in Nigeria\
\
| Mode | 2018 |\
| --- | --- |\
| Aviation | 4087.96 |\
| Rail | 190.68 |\
| Road | 217953.94 |\
\
1.4 Freight transport activity\
\
Information on freight activity for Nigeria is available for trucks and rail. Again, the same three datasets have\
been included because they share different insights into the situation in the country. Trucks recorded 67300\
million tonnes-km in 2010. Rail records 700 million tonnes-km in the same year. The data is sourced from a\
sustainable energy pathway study for land transport in Nigeria.\
\
| Mode |\
| --- |\
| Trucks |\
| Rail |\
\
| 2010 |\
| --- |\
| 67300 |\
| 700 |\
\
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\
Source: Dioha, M. and Kumar, A., 2020, Sustainable energy pathways for land transport in Nigeria, Utilities Policy,\
Volume 64, 101034, ISSN 0957-1787, [https://doi.org/10.1016/j.jup.2020.101034](https://doi.org/10.1016/j.jup.2020.101034).,\
[https://www.sciencedirect.com/science/article/abs/pii/S0957178720300291?via%3Dihub](https://www.sciencedirect.com/science/article/abs/pii/S0957178720300291?via%3Dihub)\
\
Freight transport relies completely on diesel, where trucks are estimated to record 69247 million tonnes-km\
in 2016. It is a minor increase from the 2010 values, but the compatibility is limited as described in the\
subsection above.\
\
Table 6: Freight transport activity (million tonnes-km) by fuel type in Nigeria\
\
| Mode | Sub- |\
| --- | --- |\
| Heavy duty trucks | Diesel |\
| Rail | Diesel |\
\
| type | 2016 |\
| --- | --- |\
|  | 69246.9735 |\
|  | 104.0265 |\
\
Rail freight activity by the World Bank is outdated as 2005 is the most recent year with data. It indicates 77\
million tonnes-km, which is significantly less than Table 5 and slightly less than Table 6. Thus, the data has to\
be taken with caution.\
\
Table 7: Freight transport activity (million tonnes-km) in Nigeria\
\
| Mode | 1990 | 1991 | 1992 | 1993 | 1994 | 1995 | 1996 |\
| --- | --- | --- | --- | --- | --- | --- | --- |\
| Rail | 178.2 | 213.3 | 183.6 | 95.4 | 95.4 | - | - |\
\
| 1996 | 1997 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 |\
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\
|  | 480 | - | 54 | - | - | 44 | 39 | 57.214 | 76 |\
\
Source: World Bank, 2022, Railways, good transported (million ton-km) - Nigeria,\
[https://data.worldbank.org/indicator/IS.RRS.GOOD.MT.K6?locations=NG](https://data.worldbank.org/indicator/IS.RRS.GOOD.MT.K6?locations=NG)\
\
1.5 Vehicle fleets\
\
According to the UN DESA modelled data, freight activity for 2018 in Nigeria is 73112 million tonnes-km for\
road, 12156 million tonnes-km for rail, 5144 million tonnes-km for waterways, and 19 million tonnes-km for\
aviation. Freight transport is mostly achieved through road transport.\
\
| Mode | 2018 |\
| --- | --- |\
| Aviation | 19.42 |\
| Inland waterways | 5144.63 |\
| Rail | 12155.96 |\
| Road | 73112.17 |\
\
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\
as of 2018. Data is available for 2016, 2017, and 2018. The statistics show an average annual growth of 2%.\
58% of the fleet are commercial vehicles, followed by 41% private vehicles.\
\
Table 9: Official vehicle fleet numbers in Nigeria\
\
| Mode | 2016 | 2017 | 2018 |\
| --- | --- | --- | --- |\
| Private | 4628060 | 4682309 | 4819251 |\
| Commercial | 6741871 | 6756372 | 6785956 |\
| Government | 131178 | 138761 | 149470 |\
| Diplomatic | 2301 | 5889 | 6194 |\
| Total fleet | 11503410 | 11583331 | 11760871 |\
\
Source:\
\
● Data for 2016: National Bureau of Statistics, Nigeria, 2017, Road Transport Data (Q2 2017),\
[https://www.nigerianstat.gov.ng/pdfuploads/Road\_Transport\_Data\_%E2%80%93\_Q2\_2017\_.pdf](https://www.nigerianstat.gov.ng/pdfuploads/Road_Transport_Data_%E2%80%93_Q2_2017_.pdf);\
● Data for 2017 and 2018: National Bureau of Statistics, Nigeria, 2018, Road Transport Data (Q2 2018),\
\
● Data for 2017 and 2018: National Bureau of Statistics, Nigeria, 2018, Road Transport Data (Q2 2018),\
[https://nigerianstat.gov.ng/elibrary/read/813](https://nigerianstat.gov.ng/elibrary/read/813)\
\
An additional dataset from a Nigerian case study exploring the future transition to electric vehicles features\
detailed information on the light-duty vehicle fleet by fuel type. The data is created by the author based on\
3\
an another study from 2013. It assumes that 98% of private cars as well as 98% of light commercial vehicles\
are powered by gasoline. This data singles out motorcycles, representing 41% of all light-duty vehicles in\
Nigeria.\
\
Table 10: Assumed vehicle fleet numbers by fuel type in Nigeria\
\
| Mode | Sub-type | 2015 |\
| --- | --- | --- |\
| Total fleet | - | 12700562 |\
| Motorcycles | Gasoline | 5240981 |\
| Motorcycles | Electric | 0 |\
| Private Car | Gasoline | 6382022 |\
| Private Car | Diesel | 130245 |\
| Private Car | Electric | 0 |\
| Light commercial vehicle | Gasoline | 919551 |\
| Light commercial vehicle | Diesel | 18766 |\
| Light commercial vehicle | Electric | 0 |\
| Light bus | Gasoline | 8817 |\
| Light bus | Diesel | 180 |\
| Light bus | Electric | 0 |\
\
Source: Dioha, M. O. et al., 2022, Exploring the role of electric vehicles in Africa's energy transition: A Nigerian case\
study, iScience, Volume 25, Issue 3, 103926, [https://www.cell.com/iscience/fulltext/S2589-0042(22)00196-1](https://www.cell.com/iscience/fulltext/S2589-0042(22)00196-1)\
\
3\
Cervigni, R., Rogers, J.A., and Dvorak, I., 2013, Assessing Low-Carbon Development in Nigeria: An Analysis\
of Four Sectors (The World Bank), [https://doi.org/10.1596/978-0-8213-9973-6](https://doi.org/10.1596/978-0-8213-9973-6).\
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# 2\. Methodology\
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The focus is on national data for passenger activity (passenger-km), freight activity (tonnes-km) and modes of transport (number of vehicles). The priority is to collect data released by national governments, government-affiliated organisations, or country-specific studies. The research identifies the most recent available data and any data available from 1990 onwards. The priority was for any data after 2010, because transport is a very dynamic growth sector and anything before 2010 adds limited value to understanding the current real-world situation.\
\
Desk research is the main data collection approach for the Transport Starter Data Kits. The desk research examined annual yearbooks, transport statistics, country reporting, and any national statistical portals. Websites of the national government, transport ministries, statistical institutes and other related authorities were examined. Only when severe data gaps exist, global datasets are consulted. In some cases, World Bank data4 on rail passenger and rail freight is included.\
\
Each Transport Data for Starter Data Kit set contains an additional dataset, which is sourced from the United Nations Department of Economic and Social Affairs (UN DESA) Statistics Division. It is included as a secondary priority because this dataset is the result of a modelling exercise and covers every country. The UN DESA modelled passenger activity and freight activity has the purpose to support the Sustainable Development Goal Indicator 9.1.2 5 . The passenger activity provides information for road, rail, and air transport. Freight data covers the road, rail and inland water, and aviation. The passenger-km and tonnes- km data originate from the Open Sustainable Development Goals (SDG) Data Hub. In the UN DESA dataset, only the data for countries participating in the International Transport Forum (ITF) (representing mostly member countries of the Organisation for Economic Co-operation and Development (OECD)) and the United Nations Economic Commission for Europe (UNECE) (mostly European countries) are based on national reporting. For non-ITF/UNECE countries, data are estimated using the ITF model, which uses several covariates such as gross domestic product, population, and transport network coverage. A description of the model can be found in the ITF Transport Outlook 2017 6 . The UN DESA dataset is included in the Transport Data for Starter Data Kits as additional tables to fill in the incomplete picture that most countries present. The UN DESA modelled data is less accurate and it shall only be regarded as offering the wider picture of transport activity in the country.\
\
The collected data have been shared with a group of relevant SLOCAT partners to validate and explore any additional sources. The SLOCAT partners were selected based on their actions to lead projects in the region and their involvement in data-focused knowledge products or projects. The consultation involved ten anonymous organisations.\
\
4 Rail passenger data: World Bank, 2022, Railways, passengers carried (million passenger-km), [https://data.worldbank.org/indicator/IS.RRS.PASG.KM](https://data.worldbank.org/indicator/IS.RRS.PASG.KM); rail freight data: World Bank, 2022, Railways, goods transported (million ton-km), [https://data.worldbank.org/indicator/IS.RRS.GOOD.MT.K6](https://data.worldbank.org/indicator/IS.RRS.GOOD.MT.K6) 5 UN DESA, 2021, Indicator 9.1.2: Freight volume by mode of transport (tonne kilometres): [https://www.sdg.org/datasets/undesa::indicator-9-1-2-freight-volume-by-mode-of-transport-tonne-kilometres/about](https://www.sdg.org/datasets/undesa::indicator-9-1-2-freight-volume-by-mode-of-transport-tonne-kilometres/about) ; UN DESA, 2021, Indicator 9.1.2: Passenger volume (passenger kilometres) by mode of transport: [https://www.sdg.org/datasets/undesa::indicator-9-1-2-passenger-volume-passenger-kilometres-by-mode-of-](https://www.sdg.org/datasets/undesa::indicator-9-1-2-passenger-volume-passenger-kilometres-by-mode-of-) transport/about ITF, 2017, ITF Transport Outlook 2017, [https://www.itf-oecd.org/transport-outlook-2017](https://www.itf-oecd.org/transport-outlook-2017)\
\
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Desk research is an approach that limits the research to material available on the internet, accessible through search engines and linked to government and statistical institutes’ websites. However, this does not pose a major limitation to obtaining data. Nearly every country has functional websites for statistics and transport authorities. In a few cases, websites are not well maintained, resulting in missing or broken hyperlinks to reports. By using services that provide access to archived websites, some of these broken pages can be retrieved. The collected information has been shared with partners and no additional information has been received.\
\
While over 1,500 languages are spoken across Sub-Saharan Africa, government datasets are generally published in a smaller subset of languages including English, French, Portuguese and others. Nonetheless, language is not a barrier to navigating through the material and identifying the relevant parameters. The involved team members can navigate through reports in such languages. If needed, automatic translation tools were used.\
\
The World Bank’s data platform provided GDP share by sector for agriculture, manufacturing, and services. However, GDP share by construction, mining, and energy was also needed to align the data structure with the MAED tool. To address the lack of data available for these sectors, the authors assumed that construction, mining, manufacturing, and energy all fall within the industry sector. Thus, to obtain data for the three remaining sectors, the remaining percentage after considering agriculture, manufacturing, and services from The World Bank’s data platform, was divided by three. It is therefore assumed that the GDP share of the construction, mining, and energy sectors are the same.\
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# 3\. Ethics Statement\
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## Not applicable.\
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# 4\. CRediT Author Statement\
\
## Naomi Tan: Investigation, Conceptualisation, Methodology; Data Collection; Visualization, Writing and\
\
Editing; **Robert Ambunda: Data Collection; Investigation; Writing and Editing; Nikola Medimorec:** Conceptualisation; Methodology; Data Collection; Investigation; Writing, Review & Editing; Supervision; **Angel Cortez:** Data Collection; Agustina Krapp: Data Collection; Erin Maxwell: Data Collection; John **Harrison: Supervision; Mark Howells: Supervision**\
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# Acknowledgements\
\
We would like to acknowledge the SLOCAT Partnership on Sustainable, Low Carbon Transport who helped make this and future iterations possible. We would also like to acknowledge the International Road Federation (IRF) and the International Union of Railways (UIC) for providing us with these data. The data are extracted from IRF World Road Statistics (WRS) and their use is subject to copyright and specific Terms and Conditions available on the WRS website. More WRS data are available for free on its Data Warehouse [www.worldroadstatistics.org](http://www.worldroadstatistics.org/). Likewise, data was extracted from the UIC Statistics Rail Information System and Analyses (Railisa) and more can be found on its online tool [https://uic-stats.uic.org/](https://uic-stats.uic.org/)\
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# Funding\
\
As well as support in kind provided by the employers of the authors of this note, we also acknowledge core funding from the Climate Compatible Growth Program (#CCG) of the UK's Foreign Development and Commonwealth Office (FCDO). The views expressed in this paper do not necessarily reflect the UK government’s official policies.\
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# Declaration of Competing Interests\
\
The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article.