Abstract
The demand for data-driven models to inform sustainable transportation planning has become more important as countries address the complexities of urban mobility. However, data collection and curation are time-consuming and can be challenging due to data inaccessibility and inaccuracy. The Transport Starter Data Kit therefore aims to address these challenges, offering a one-stop-shop for transport modelling-related data. The Kit contains historical annual data (1990–2021) on passenger and freight activity, energy intensities, load factors, and vehicle stock, segregated by mode and fuel where available. Additionally, population and GDP data, which influence transport activity, are included. The value of the dataset lies not only in the range of variables it offers but also in the compilation from multiple authoritative sources, providing researchers, consultants, and policymakers interested in data-based transport modelling with a foundational base for their model development. By adopting, adapting, and applying the data, clear policies may be developed which can underpin the necessary finances for sustainable transport development.
Keywords: Transport, Transport systems, MAED, OSeMOSYS
Specifications Table
| Subject | Transportation Management |
| Specific subject area | Transport Systems Modelling |
| Data format | Raw, Analyzed |
| Type of data | Table, Graph |
| Data collection | Historical data from 1990 to 2021 were collected from the websites, annual reports, and databases of international organisations, as well as from academic articles and existing modelling databases. The data were collected for use with the Model for Analysis of Energy Demand (MAED) tool, which can project transport demand based on historical data. Nonetheless, the data available through this document is independent of the tool. |
| Data source location | Raw data sources are listed in the references section and Table 1 of this article. |
| Data accessibility | Repository name: Zenodo Data identification number: 10.5281/zenodo.8060153 Direct URL to data: https://zenodo.org/records/8060153 |
1. Value of the Data
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The socio-transport data are useful for country analysts, policymakers, and the broader scientific community, to understand historical transport trends or as a base for systems model development for specific countries to inform national transport investment outlooks and policy plans. For example, by analyzing trends in passenger transport demand using this dataset, policymakers can identify patterns that may influence future infrastructure investments or environmental policies.
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The data is compiled in one repository, which reduces the data collection time, thus, can be useful for capacity building events with limited time. To date, the dataset has been used in Climate Compatible Growth's Energy Modelling Platforms, as well as Master's theses at Imperial College London and University College London to investigate forecasted transport demand and develop capacity expansion plans for electrification of the transport sector [1].
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The data can be used with the tools Model for Analysis of Energy Demand (MAED) and the Open Source Energy Modelling System (OSeMOSYS) to project future transport demand and installed capacity needed for the required demand, respectively. This promotes the use of evidence-based decision-making for the transport sector, which is increasingly important as countries seek to develop sustainable and efficient transport systems alongside the power sector. Nonetheless, the data collected in this paper are independent to these tools.
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The data are open-source and country-specific which is not easily accessible in current literature. By compiling secondary data from multiple, diverse sources, the work provides analysts with complete and accessible datasets, helping to overcome barriers of data inaccessibility.
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The data not only provides a range of transport and macroeconomic variables but also offers data compiled from multiple authoritative sources for added reliability.
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The dataset promotes the U4RIA goals [2], which are Ubuntu, Retrievability, Reusability, Repeatability, Reconstructability, Interoperability, and Auditability.
2. Data Description
This paper presents historical socio-transport data from 1990 to 2021 by country and related data by region within selected countries in Africa, Asia, and South America. The selected list of countries can be found in Table 5. The time period and countries are selected based on relevancy and data availability, and their data can be used as input to develop a transport system model for the selected countries. The data collected were from publicly available sources, including the reports of international organizations, journal articles, and existing databases, which complies with the U4RIA goals, which stand for Ubuntu, Retrievability, Reusability, Repeatability, Reconstructability, Interoperability, and Auditability. In more detail, the U4RIA goals are designed to improve transport modelling for policy and financial support through guidelines and best practices [2]. The methods of data collection and preparation are described in Section 2 of this article. The data sources used are listed in Table 1, with each source given a reference code which will be referred to throughout the paper. The dataset includes raw data on population, gross domestic product (GDP), as well as passenger activity, freight activity, vehicle stock number, energy efficiency, and load factor by mode, where available. Social data on population and GDP are included as transport demand are dependent on both factors [3]. GDP share by sector (agriculture, construction, mining, manufacturing, services, and energy) were processed based on assumptions. This was produced to comply with the Model for Analysis of Energy Demand (MAED) tool [4], nonetheless, the data provided in this paper are independent to the tool. For transparency and easy uptake, each data is also given an observation code based on the Statistical Data and Metadata eXchange (SDMX) Code List for Observation Status, which can be found in the dataset file. For further comprehensive understanding, country-specific datasets are available externally for each country (see Table 5 within the Appendix for links to each available country-specific dataset). Most data in the Transport Starter Data Kit were collected from The World Bank (TWB) DataBank (DB) [5], United Nations (UN) Department of Economic and Social Affairs (DESA) [6,7], International Road Federation (IRF) World Road Statistics (WRS) [8], International Union of Railways (UIC) Statistics Rail Information System and Analyses (RAILISA) [9], Asian Development Bank (ADB) Asian Transport Outlook (ATO) [10], and the Statistical, Economic and Social Research and Training Centre for Islamic Countries (SESRIC) Statistical Yearbook [11]. Further data sources used are listed in Table 1.
Table 1.
Additional data sources used in the paper, segregated by country and continent.
| Continent | Country | References |
|---|---|---|
| Africa | Algeria | Algeria Data Portal (2023). Dataset Browser. Available at: https://algeria.opendataforafrica.org/data/#menu=topic |
| Angola | OICA (2022). Vehicles in use. Available at: www.oica.net/category/vehicles-in-use/ | |
| Gwilliam, K. (2011). Africa's Transport Infrastructure, Mainstreaming Maintenance and Management, World Bank. Available at: https://ppiaf.org/documents/3136/download | ||
| Benin | Direction de la Programmation et de la Prospective (2014). Annuaire Statistique des Transports 2001–2008. Available at: https://transports.bj/wp-content/uploads/2018/03/Annuaire_Statistique_TPT_2001_2008_VF.pdf; Ministere du Plan et du Developpement (2019); Tableau de Bord Social (2015). Available at: https://instad.bj/images/docs/insae-statistiques/sociales/Tableau%20de%20Bord%20Social/Tableau%20de%20Bord%20Social%202015.pdf; Ministere des Infrastructures et des Transports (2017). Annuire Statistique 2013–2016. Available at: https://transports.bj/wp-content/uploads/2018/03/Annuaire_Statistique_TPT_2013_2016_VF.pdf; Institut National de la Statistique et de l'Analyse Economique (2019). Annuaire Statistique 2019. Available at: https://instad.bj/images/docs/insae-publications/annuelles/AS-INSAE/Annee_2019/Annuaire_Statistique_National_2019.pdf |
|
| Botswana | Statistics Botswana (2021). Transport and Infrastructure Statistics Report. Available at: https://www.statsbots.org.bw/sites/default/files/publications/2020%20Transport%20and%20Infrastrucutre%20Statistics%20Report.pdf | |
| Burkina Faso | SSATP (2019). Policies for sustainable mobility and accessibility in cities of Burkina Faso. Available at: https://www.ssatp.org/sites/ssatp/files/publication/Country-Assesment-report-Burkina%20Faso-En.pdf | |
| Cameroon | National Institute of Statistics of Cameroon (2014). Cameroon Statistical Yearbook 2014. Available at: https://cameroon.opendataforafrica.org/ggtphlc; | |
| Ministry of Transport (2019). Transport Statistics Yearbook 2019 Edition. Available at: http://mintransports.net/Annuaire-Statisrique-du-MINT_Version_Anglaise_OK.pdf | ||
| Côte d'Ivoire | Institut National de la Statistique (2012). Annuaire des Statistiques Demographiques et Sociales. Available at: http://www.ins.ci/templates/Pub/annuaire%20demo.pdf | |
| Djibouti | Djibouti Data Portal (2022), Transports. Available at: https://djibouti.opendataforafrica.org/acqjnyc/transports | |
| Egypt | Knoema (2020). Egypt - Air transport freight. Available at: https://knoema.com/atlas/Egypt/Air-transport-freight | |
| Egypt Data Portal (2023). Dataset Browser. Available at: https://egypt.opendataforafrica.org/data/#topic=Transport | ||
| Equatorial Guinea | Equatorial Guinea Data Portal (2023). Dataset Browser. Available at: https://eguinea.opendataforafrica.org/data/#topic=Transport | |
| Eswatini | Eswatini Data Portal (2023). Dataset Browser. Available at: https://swaziland.opendataforafrica.org/data/#topic=Transport | |
| Ethiopia | The Federal Democratic Republic of Ethiopia (2020). Transport Sector - Ten Years Perspective Plan (2020/21 - 2029/30). Available at: http://www.motr.gov.et/web/guest/-/ministry-of-transport-10-year-pl-2?inheritRedirect=true | |
| Desta, D. (2021). Assessment of after-sales service management in the case of Motor and Engineering Company of Ethiopia (MOENCO). Available at: http://213.55.95.56/bitstream/handle/123456789/27454/Dawit%20Desta.pdf?sequence=1&isAllowed=y | ||
| Addis Ababa Institute of Technology (2012). Final report on pilot Global Fuel Economy Initiative study in Ethiopia. Available at: https://www.globalfueleconomy.org/in-country/africa | ||
| TricksFast (2020). Ethiopia registered vehicle reaches 1.2 million. Available at: https://tricksfast.com/ethiopia/ethiopia-registered-vehicle-reaches-1-2-million/ | ||
| Gabon | Gabon Data Portal (2023). Dataset Browser. Available at: https://gabon.opendataforafrica.org/data/#menu=topic | |
| Gambia | Gambia Data Portal (2023). Dataset Browser. Available at: https://gambia.opendataforafrica.org/data/#menu=topic | |
| Ghana | Lee, N. (2016). Decision support methodology for national energy planning in developing countries: an implementation focused approach. Available at: https://www.semanticscholar.org/paper/Decision-support-methodology-for-national-energy-in-Lee/7383d3289f197b180cf7b1cdd7222570f358f4eb | |
| Ghana Statistical Service (2015). Ghana's Statistical Year Book 2010–2013. Available at: https://statsghana.gov.gh/gsspublications.php?category=MTAyOTI2NTM5NC42ODM1/webstats/8no4s4pq66 | ||
| Table B-1 - Calculated values for a study, references mentioned are Anin, E.K., Annan, J., Otchere, A.F. (2013). Evaluating the role of mass transit and its effect on fuel efficiency in the Kumasi metropolis, Ghana. Int. J. Bus. Soc. Res. 3, 107–116; UITP, UATP (2010). Report on statistical indicators of public transport performance in Africa. Union Internationale des Transports Publics (UITP & Union Africaine des Transports Publics (UATP), Brussels. Available at: https://www.researchgate.net/publication/358107686_Evaluating_the_Role_of_Mass_Transit_and_its_Effect_on_Fuel_Efficiency_in_the_Kumasi_Metropolis_Ghana |
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| Kenya | JICA (2006). The Study on Master Plan for Urban Transport in the Nairobi Metropolitan Area. Available at: https://openjicareport.jica.go.jp/pdf/11823093_03.pdf | |
| KNBS (2021). Statistical Abstract 2021. Available at: https://www.knbs.or.ke/download/statistical-abstract-2021/ | ||
| GIZ TraCS (2018). Greenhouse gas emissions from the transport sector: Mitigation options for Kenya. Available at: https://www.changing-transport.org/wp-content/uploads/2018_GIZ_INFRAS_Transport_Mitigation_Options_Kenya.pdf | ||
| Malawi | Malawi (2023). Dataset Browser. Available at: https://malawi.opendataforafrica.org/data/#menu=topic | |
| Mali | Mali Data Portal (2023). Dataset Browser. Available at: https://mali.opendataforafrica.org/data/#menu=topic | |
| Mauritania | Mauritania Data Portal (2023). Dataset Browser. Available at: https://mauritania.opendataforafrica.org/data/#menu=topic | |
| Morocco | Morocco Data Portal (2023). Dataset Browser. Available at: https://morocco.opendataforafrica.org/data/#menu=topic | |
| Mozambique | Instituto Nacional de Estadística (2020). Estadísticos dos transportes e comunicações. Available at: http://www.ine.gov.mz/estatisticas/estatisticas-sectoriais/transporte-e-comunicacao | |
| Nigeria | 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. Available at: https://www.sciencedirect.com/science/article/abs/pii/S0957178720300291?via%3Dihub | |
| Federal Republic of Nigeria (2020). Third National Communication of the Federal Republic of Nigeria. Available at: https://unfccc.int/sites/default/files/resource/NIGERIA_NC3_18Apr2020_FINAL.pdf | ||
| Senegal | Agence Nationale de la Statistique et de la Demographie (2020). Situation Economique et Sociale du Sénégal Ed. 2017/2018. Available at: https://www.ansd.sn/sites/default/files/2023-03/13-SES-2017-2018_Transport.pdf | |
| South Africa | Havenga, J.H., Simpson, Z.P., King, D. de Bod, A. and Braun, M. (2016). Logistics Barometer South Africa 2016. Stellenbosch University. Available at: http://www.sun.ac.za/english/faculty/economy/logistics/Pages/logisticsbarometer.aspx | |
| Venter, C. and Mohammed, S.O. (2013). Estimating car ownership and transport energy consumption: A disaggregate study in Nelson Mandela Bay. Journal of the South African Institution of Civil Engineering. 55. 2–10. Available at: https://www.researchgate.net/publication/282543048_Estimating_car_ownership_and_transport_energy_consumption_A_disaggregate_study_in_Nelson_Mandela_Bay | ||
| Tunisia | Statistiques Tunisie (2023). Tunisia Database. Available at: http://dataportal.ins.tn/en | |
| Uganda | Uganda Data Portal (2023). Dataset Browser. Available at: https://uganda.opendataforafrica.org/data/#topic=Transport | |
| United Republic of Tanzania | National Bureau of Statistics (2019). Tanzania Socio-Economic Database. Available at: http://www.tsed.go.tz/ | |
| African Development Bank (2013). Tanzania Transport Sector Review. Available at: https://www.afdb.org/fileadmin/uploads/afdb/Documents/Project-and-Operations/Tanzania_-_Transport_Sector_Review.pdf | ||
| Zambia | Zambia Ministry of Transport and Communication (2017). MTC Sector Performance. Available at: https://zambiamtc.opendataforafrica.org/kenvome/mtc-sector-performance; Ministry of Transport and Communications (2019). 2019 Annual Report. Available at: https://www.motl.gov.zm/?page_id=1260#1635868305817-8b615f21-d43c | |
| Zimbabwe | National Statistics Agency (2018). Zimbabwe Statistics 2010. Available at: https://zimbabwe.opendataforafrica.org/awadaad/zimbabwe-statistics-2010 | |
| Taiwan Province of China | United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022. Online Edition. Available at: https://population.un.org/wpp/ | |
| International Monetary Fund (2023). World Economic Outlook. Available at: https://www.imf.org/en/Data | ||
| Taiwan National Statistics (2023). Breakdown of gross domestic product (GDP) of Taiwan from 2012 to 2022, by economic sector. Available at: https://www.statista.com/statistics/321366/taiwan-gdp-breakdown-by-sector/ | ||
| Viet Nam | General Statistics Office of Vietnam (2020). Available at: https://www.gso.gov.vn/en/homepage/ | |
| South America | Brazil | Goes, G. V., Gonçalves, D. N. S., de Almeida D'Agosto, M., La Rovere, E. L., & de Mello Bandeira, R. A. (2020). MRV framework and prospective scenarios to monitor and ratchet up Brazilian transport mitigation targets, Climatic Change, doi:10.1007/s10584-020-02767-6. Available at: https://link.springer.com/article/10.1007/s10584-020-02767-6 |
2.1. Population
Annual population count (million people), population share by urban and rural (%), and population growth from 1990 to 2021 (%) for the selected countries are presented in the dataset. Data was directly collected from the World Bank DataBank [5]. Using the dataset, various trends can be analyzed. For example, the average population for each continent can be analyzed (Fig. 1).
Fig. 1.
Average population (million people) of Africa, Asia, and South America. Note that only 10 countries in Asia and 2 countries in South America, following the countries selected in the dataset, were used to calculate the average. Thus, these averages are not representative of the entire continent.
2.2. Gross domestic product (GDP)
Annual GDP (million USD (2015)), GDP by sectorial share (agriculture, construction, mining, manufacturing, services, and energy) (%), and GDP growth (%) from 1990 to 2021 for the selected countries are presented in this paper. Annual GDP and GDP growth are collected directly from the World Bank DataBank [5]. Where data was not available, data processing was done. Nonetheless, trends for each continent can be analyzed (Fig. 2). GDP share by sector was also further processed, with both the methodologies outlined in Section 2 (Fig. 3).
Fig. 2.
Average GDP (billion USD (2015)) of Africa, Asia, and South America. Note that only 10 countries in Asia and 2 countries in South America, following the countries selected in the dataset, were used to calculate the average. Thus, these averages are not representative of the entire continent.
Fig. 3.
Simplified diagram of the different sectors that contribute to overall GDP. An asterisk (*) denotes that the data for the sector is provided by The World Bank DataBank [5].
2.3. Passenger activity
Passenger activity (million passenger-km) for road, rail, air, and inland waterways are noted in the dataset for all selected countries. Passenger activity by mode is also displayed, where available. These data were mainly collected from TWB DB [5], UN DESA [6], IRF WRS [8], the UIC RAILISA [9], and the ADB ATO [10]. Country-specific databases and articles from which data were collected from are listed in Table 1. These data were collected directly from the sources.
2.4. Freight activity
Freight activity (million ton-km) for road, rail, air, and inland waterways from the year 2015 onwards are noted in the dataset for all selected countries. These data were mainly collected from TWB DB [5], UN DESA [7], IRF WRS [8], UIC RAILISA [9], ADB ATO [10], and the SESRIC Statistical Yearbook [11]. Country-specific databases and articles from which data were collected are listed in Table 1. These data were collected directly from the sources. T
2.5. Vehicle stock number
Vehicle stock number (units) for various road transport modes (motorcycle, car, bus, light-duty vehicle, heavy-duty vehicle, minibus, agricultural and forestry tractors, special purpose vehicles, and so on) are listed in the dataset for all selected countries.. These data were mainly collected from the IRF WRS [8] and the ADB ATO [10]. Country-specific databases and articles from which data were collected are listed in Table 1. Most of the data were collected directly from the sources, with a small number calculated with the methodology described in Section 2.
2.6. Energy intensity
Energy efficiency data in the unit of MJ/passenger for different vehicle and fuel types, where available, in Africa, Asia, and South America were collected directly from Kane [12], International Energy Agency (IEA) [13], and Goes [14], respectively (Table 2). Due to lack of available data, it was assumed that energy efficiency within each continent were the same. Energy efficiency data for megajoules/vehicle-kilometre was also obtained from Venter and Mohammed [15], however this was location-specific to South Africa.
Table 2.
Energy efficiency (megajoules/passenger) of different vehicle and fuel types, where available, in Africa, Asia, and South America.
| Region | Year | Vehicle mode | Energy efficiency | Unit |
|---|---|---|---|---|
| Africa | 2013 | Car (electric) | 0.55 | MJ/passenger |
| Car (hybrid) | 1.56 | MJ/passenger | ||
| Car (petrol) | 2.22 | MJ/passenger | ||
| Minibus (petrol) | 0.66 | MJ/passenger | ||
| Asia | 2018 | Motorcycle | 0.5 | MJ/passenger |
| Car | 1.8 | MJ/passenger | ||
| Minibus | 0.7 | MJ/passenger | ||
| Bus | 0.7 | MJ/passenger | ||
| Light-duty vehicle | 2.7 | MJ/passenger | ||
| Rail | 0.2 | MJ/passenger | ||
| Aviation | 1.8 | MJ/passenger | ||
| South America | 2005, 2010, 2015, 2017 | Car | 1.1 | MJ/passenger |
| Country | Year | Vehicle mode | Energy efficiency | Unit |
| South Africa | 2004 | Motorcycle | 102.8 | MJ/vehicle-km |
| Car | 396.4 | MJ/vehicle-km | ||
| Minibus | 513.8 | MJ/vehicle-km | ||
| Bus | 1833.5 | MJ/vehicle-km | ||
| Light-duty vehicle | 451.4 | MJ/vehicle-km | ||
| Rail (passenger) | 10.3 | MJ/vehicle-km |
2.7. Load factor
Load factors (passenger/vehicle) for different vehicle and fuel types, where available, for Africa and South America are noted in Table 3. Due to lack of available data, the load factor from Kane [12] is assumed to be representative of Africa. Similarly, the load factor from Oviedo [16] is assumed to be representative of South America. Data for Asia was unavailable. Nonetheless, country-specific data for ten African countries: Côte d'Ivoire, Cameroon, Ethiopia, Ghana, Kenya, Namibia, Nigeria, Senegal, United Republic of Tanzania, and South Africa, are available directly from Merven [17].
Table 3.
Load factors (passengers per vehicle) for different vehicle and fuel types, where available, in Africa and South America. Country-specific load factors for ten African countries are also noted in the table.
| Region | Year | Vehicle mode | Load factor |
|---|---|---|---|
| Africa | 2013 | Car (electric) | 1.4 |
| Car (hybrid) | 1.4 | ||
| Car (petrol) | 1.4 | ||
| Minibus (petrol) | 7.8 | ||
| South America | 2018 | Car | 1.2 |
| Bus | 36 | ||
| Country | Year | Vehicle mode | Load factor |
| Côte d'Ivoire | 2010 | Car | 2 |
| Minibus | 18 | ||
| Bus (diesel) | 60 | ||
| Cameroon | 2010 | Car | 2.3 |
| Minibus | 17 | ||
| Bus (diesel) | 45 | ||
| Ethiopia | 2010 | Car | 3.7 |
| Minibus | 11 | ||
| Bus (diesel) | 80 | ||
| Ghana | 2010 | Car | 2 |
| Minibus | 18 | ||
| Kenya | 2010 | Car | 1.7 |
| Minibus | 18 | ||
| Bus (diesel) | 70 | ||
| Namibia | 2010 | Car | 1.3 |
| Nigeria | 2010 | Car | 1.8 |
| Minibus | 18 | ||
| Bus (diesel) | 43 | ||
| Senegal | 2010 | Car | 2 |
| Minibus | 35 | ||
| Bus (diesel) | 66 | ||
| United Republic of Tanzania | 2010 | Car | 1.9 |
| Minibus | 29 | ||
| Bus (diesel) | 45 | ||
| South Africa | 2010 | Car | 1.4 |
| Minibus | 8.5 | ||
| Bus (diesel) | 37.1 |
3. Experimental Design, Materials and Methods
Data were primarily collected from the databases and websites of international organizations, including TWB DB [5], UN DESA [6,7], IRF WRS [8], UIC RAILISA [9], ADB ATO [10], SESRIC Statistical Yearbook [11], and the IEA [13]. Additionally, data were sourced from existing studies, country reports, and national websites from researchers, organizations, and government as noted in Table 1. The subsections below note how the data were collected and processed in more detail.
3.1. Population
Data for population including total population, urban population, and population growth are from TWB DB [5]. Only one country, Taiwan Province of China, had missing urban population share data for various years, to which the authors addressed by extrapolating from the available historical years. This was done by calculating the difference in urban population share percentage between years with available data. The difference was then divided by the number of years with missing values. This method allowed for a fast and consistent approach to addressing missing values. However, it is acknowledged that this may not fully capture the abrupt changes in urbanization patterns. Nonetheless, it provides a complete dataset for population analyses.
3.2. GDP
Data for GDP including total GDP, GDP share by sector, and GDP growth are from TWB DB [5]. Similar to population, several countries had missing data for certain years. In this case, the approach that was done with population was repeated, where data was extrapolated from the available historical years. Where data were missing at the start of the investigation period only, the average increase or decrease rate was calculated based on the next 15 years, and applied to the missing values. Likewise, where data were missing at the end of the investigation period only, the average increase or decrease rate was calculated based on the former 15 years, and applied to the missing values. In the instance where there were not an adequate number of years to extrapolate, or where the extrapolation led to unrealistic numbers, an overall average was calculated based on the available data. Table 4 notes the countries where data was extrapolated or averaged.
Table 4.
List of countries and their data processing methodology. An asterisk (*) denotes that he country had both extrapolation and averaging as the methodology.
| None | Extrapolation | Average | ||
|---|---|---|---|---|
| Egypt | United Republic of Tanzania | Algeria | Liberia* | Djibouti |
| Eswatini | Togo | Angola | Libya | Equatorial Guinea |
| Ethiopia | Tunisia | Benin | Malawi | Liberia* |
| Gabon | Uganda | Botswana | Mali | South Sudan* |
| Ghana | Zambia | Burkina Faso | Mauritania | Myanmar* |
| Morocco | Indonesia | Burundi | Mozambique | |
| Namibia | Lao People's Democratic Republic | Cameroon | Senegal | |
| Niger | Malaysia | Central African Republic | Sierra Leone | |
| Nigeria | Republic of Korea | Chad | Somalia | |
| Rwanda | Brazil | Congo | South Sudan* | |
| South Africa | Colombia | Côte d'Ivoire | Sudan | |
| Democratic Republic of the Congo | Zimbabwe | |||
| Eritrea | Cambodia | |||
| Gambia | Myanmar* | |||
| Guinea | Philippines | |||
| Guinea-Bissau | Taiwan Province of China | |||
| Kenya | Thailand | |||
| Lesotho | Viet Nam | |||
Further data processing was done with all countries to obtain GDP share by sector. TWB DB 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 TWB DB [5], was divided by three. It is therefore assumed that the GDP share of the construction, mining, and energy sectors are the same. Fig. 3 shows an illustrative diagram of the methodology.
3.3. Transport activity and vehicle stock number
The disaggregation of vehicle modes for passenger activity, freight activity, and vehicle stock number follows the European Union vehicle classification, which is based on the United Nations Economic Commission for Europe (UNECE) standards [18]. It should be noted that none of the passenger and freight activity were processed. Data processing was only done to a few of the countries’ vehicle stock number. Whereby annual vehicle registration number was found instead of annual vehicle stock number, annual (new) vehicle registration number was added to give a cumulative total each year, representing vehicle stock number. This methodology was done for Kenya, Mozambique, Uganda, Zambia, and Colombia. Further, where total road freight data was only found, this was split into two for some countries to cover light-duty vehicles and heavy-duty vehicles, assuming that the two will be the same. This was done for the Democratic Republic of the Congo and Egypt.
3.4. Energy intensity
The disaggregation of vehicle modes for passenger activity, freight activity, and vehicle stock number follows the European Union vehicle classification, which is based on the United Nations Economic Commission for Europe (UNECE) standards [18]. Due to lack of available country-specific data, energy intensity levels as calculated for South Africa [12] and Brazil [14] are assumed to be representative for the rest of Africa and South America, respectively. Similarly, global average values as calculated by the IEA [13] are assumed to represent the Asia region.
3.5. Load factor
The disaggregation of vehicle modes for passenger activity, freight activity, and vehicle stock number follows the European Union vehicle classification, which is based on the United Nations Economic Commission for Europe (UNECE) standards [18]. Due to lack of available country-specific data, load factor data as calculated for South Africa [12] and Colombia [16] are assumed to be representative for the rest of Africa and South America, respectively.
Limitations
As shown in Table 2, Table 3, and the dataset, available open-access transport data is challenging to find. Thus, there are several years with empty cells, due to countries or international organizations not monitoring or publishing the related country statistics. Further, some data may be deprecated, such as several data from UN DESA [6,7]. This paper publishes these data regardless due to lack of readily available data. There are also a few discrepancies with certain values from different sources. For example, there is a sudden large increase from 2018 to 2019 for Cameroon's passenger activity. Numerous assumptions were also made to the population, GDP, and transport data, as stated in Section 2, to address data gaps. Thus, these data may lack a high level of reliability. Nonetheless, the data presented can be used as a foundational base for transport demand modelling using MAED, carbon modelling using OSeMOSYS, or others, for the 60 different countries in Africa, Asia, and South America listed in Table 5.
Ethics Statement
The authors have read and followed the ethical requirements for publication in Data in Brief and confirm that the current work does not involve human subjects, animal experiments, or any data collected from social media platforms.
CRediT Author Statement
Naomi Tan: Conceptualization, Methodology, Data curation, Writing – Original draft preparation, Validation, Visualization. Robert Ambunda: Data curation, Validation. Nikola Medimorec: Data curation, Validation. Angel Cortez: Data curation, Validation. Agustina Krapp: Data curation, Validation. Erin Maxwell: Data curation, Validation. John Harrison: Supervision, Writing – Reviewing and Editing. Mark Howells: Supervision.
Acknowledgements
The authors would like to acknowledge the SLOCAT Partnership on Sustainable, Low Carbon Transport (SLOCAT Partnership) who helped make this and future iterations possible. The authors would also like to acknowledge the International Road Federation (IRF) and the International Union of Railways (UIC) for providing data towards this paper. The data are extracted from the 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 Dara Warehouse 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 uic-stats.uic.org. Additionally, the authors acknowledge core funding from UK Aid from the UK Government via the Climate Compatible Growth programme. However, the views expressed herein do not necessarily reflect the UK Government's official policies.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix
Table 5 lists the links for the country-specific Transport Starter Data Kit, which includes the dataset and data note.
Table 5.
List of links to the country-specific Transport Starter Data Kit dataset and data note.
Data Availability
References
- 1.N. Fajriningrum, L. Hofbauer, N. Tan, and S. Pye, ‘Modelling transport decarbonisation pathways in Vietnam: synergies and trade-offs in supporting the energy transition’, Preprint.
- 2.Howells M., et al. Energy system analytics and good governance - U4RIA goals of energy modelling for policy support. Res. Sq. 2021 doi: 10.21203/rs.3.rs-311311/v1. Pre-print. [DOI] [Google Scholar]
- 3.Mohsin M., Abbas Q., Zhang J., Ikram M., Iqbal N. Integrated effect of energy consumption, economic development, and population growth on CO2 based environmental degradation: a case of transport sector. Environ. Sci. Pollut. Res. 2019;26(32):32824–32835. doi: 10.1007/s11356-019-06372-8. [DOI] [PubMed] [Google Scholar]
- 4.International Atomic Energy Agency, ‘Model for Analysis of Energy Demand (MAED-2): computer manual series no.18’, 2006.
- 5.The World Bank, ‘DataBank: world development indicators’. Accessed: Jun. 20, 2023. [Online]. Available: databank.worldbank.org
- 6.UN DESA Statistics Division, ‘Indicator 9.1.2: Passenger volume (passenger kilometres) by mode of transport’. Accessed: Jun. 20, 2023. [Online]. Available: https://hub.arcgis.com/datasets/undesa::indicator-9-1-2-passenger-volume-passenger-kilometres-by-mode-of-transport-5/about.
- 7.UN DESA Statistics Division, ‘Indicator 9.1.2: Freight volume by mode of transport (tonne kilometres)’. Accessed: Jun. 20, 2023. [Online]. Available: https://unstats-undesa.opendata.arcgis.com/datasets/4a5d7189e27148c48f045729ef9e40c8_0/about.
- 8.International Road Federation, ‘World road statistics data warehouse’. Accessed: Jun. 20, 2023. [Online]. Available: https://datawarehouse.worldroadstatistics.org/.
- 9.International Union of Railways, ‘Rail information system and analyses’. Accessed: Jun. 20, 2023. [Online]. Available: https://uic-stats.uic.org/.
- 10.Asian Development Bank, ‘Asian transport outlook national database’. Accessed: Jun. 20, 2023. [Online]. Available: https://asiantransportoutlook.com/data/.
- 11.E. and S. R. and T. C. for I. C. (SESRIC) Statistical . 2021. Statistical Yearbook on OIC Member Countries. Ankara. [Google Scholar]
- 12.L. Kane, ‘What do we mean by low carbon transport: understanding how people move in Cape Town’, Cape Town, 2016.
- 13.International Energy Agency, ‘Energy intensity of passenger transport modes, 2018’. Accessed: Jun. 20, 2023. [Online]. Available: https://www.iea.org/data-and-statistics/charts/energy-intensity-of-passenger-transport-modes-2018.
- 14.Goes G.V., Gonçalves D.N.S., de Almeida D'Agosto M., La Rovere E.L., de Mello Bandeira R.A. MRV framework and prospective scenarios to monitor and ratchet up Brazilian transport mitigation targets. Clim. Change. 2020;162(4):2197–2217. doi: 10.1007/s10584-020-02767-6. [DOI] [Google Scholar]
- 15.Venter C., Mohammed S. Estimating car ownership and transport energy consumption: a disaggregate study in Nelson Mandela Bay. J. South Afr. Inst. Civil Eng. 2013;55(1):2–10. [Google Scholar]
- 16.Oviedo D., Granada I., Perez-Jaramillo D. Ridesourcing and travel demand: potential effects of transportation network companies in Bogotá. Sustainability. 2020;12(5):1732. doi: 10.3390/su12051732. [DOI] [Google Scholar]
- 17.B. Merven, A. Stone, A. Hughes, and B. Cohen, ‘Quantifying the energy needs of the transport sector for South Africa: a bottom-up model’, Cape Town, 2012.
- 18.European Commission, ‘EU classification of vehicle types’. Accessed: Jun. 20, 2023. [Online]. Available: https://alternative-fuels-observatory.ec.europa.eu/general-information/vehicle-types.
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.