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National Data on Transport Modal Shares for 131 Countries

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National Data on Transport Modal Shares for 131 Countries Article How Do People Move Around? National Data on Transport Modal Shares for 131 Countries Grigorios Fountas 1, Ya-Yen Sun 2 , Ortzi Akizu-Gardoki 3 and Francesco Pomponi 4,* 1 Transport Research Institute (TRI), School of Engineering & the Built Environment, Edinburgh Napier University, Edinburgh EH10 5DT, UK; [email protected] 2 UQ Business School, The University of Queensland, Brisbane, QLD 4072, Australia; [email protected] 3 Faculty of Engineering of Bilbao, University of the Basque Country, 48013 Bilbao, Spain; [email protected] 4 Resource Efficient Built Environment Lab (REBEL), Edinburgh Napier University, Edinburgh EH10 5DT, UK * Correspondence: [email protected]; Tel.: +44-(0)-13-1455-3590 Received: 19 May 2020; Accepted: 16 June 2020; Published: 18 June 2020 Abstract: The COVID-19 pandemic has brought global mobility into the spotlight, with well over 100 countries having instituted either a full or partial lockdown by April 2020. Reduced mobility, whilst causing social and economic impacts, can also be beneficial for the environment and future studies will surely quantify such environmental gains. However, accurate quantification is intimately linked to good quality data on transport modal shares, as passenger cars and public transport have significantly different emissions profiles. Herein, we compile a currently lacking dataset on global modal transport shares for 131 countries. Notably, these are the countries covered by the Google Community Mobility Reports (plus Russia and China for their global relevance), thus allowing for a smooth integration between our dataset and the rich information offered by the Google Community Mobility Reports, thus enabling analysis of global emissions reductions due to mobility restrictions. Beyond the current pandemic, this novel dataset will be helpful to practitioners and academics alike working in transport research. Keywords: global mobility; transport modal share; COVID-19; Google Community Mobility Reports 1. Introduction The COVID-19 pandemic has restated the importance of ‘one world, one health’ [1]. This unity has also been felt in other aspects of society, such as mobility restrictions caused by global lockdown measures, with well over 100 countries entering some form of lockdown [2]. These restrictions prompted Google to take the unprecedented measure of collecting, harmonizing, and releasing anonymized data on global mobility [3]. This information is invaluable in understanding the reduction in people’s movement due to global restrictions but does not report on what kind of transport is avoided by moving less. Given that studies are already appearing based on the Google Community Mobility Reports (GCMR) [4,5], and likely many more will follow, we intend to support such research by presenting an unprecedented dataset of modal transport shares for 131 countries (see Figure1). Notably, these are the countries covered by the GCMR (plus Russia and China for their global relevance), thus allowing for a smooth integration between our dataset and the rich information offered by Google. This novel data will not only support immediate research efforts linked to understanding the environmental implications of the COVID-19 pandemic but it also represents a stepping-stone for creating and maintaining such global datasets for future research and analyses on global transport issues. World 2020, 1, 34–43; doi:10.3390/world1010003 www.mdpi.com/journal/world World 2020, 1, FOR PEER REVIEW 2 World 2020, 1 35 represents a stepping-stone for creating and maintaining such global datasets for future research and analyses on global transport issues. Figure 1. OverviewFigure 1. Overview of countries of countries covered covered in in the the datasetdataset presented presented in this in article. this Full article. details Fullwith details with country namescountry are availablenames are available in the in Supplementary the Supplementary Material. 2. Materials and Methods 2. Materials and Methods To compile the global modal share data to distinguish primarily between public vehicle use (rail, To compilebus, coach) the globaland private modal vehicle share use (mainly data the to passenger distinguish car), a three-step primarily data betweencollection procedure public vehicle use (rail, bus, coach)was developed and private and implemented. vehicle Table use 1 (mainly shows the thesource passenger type and number car), of acountries three-step covered data in collection each step. procedure was developed and implemented. Table1 shows the source type and number of countries covered in each step. Table 1. Data sources and proportion of data per source. Number of Source Type Percentage of Countries Table 1. Data sources andCountries proportion of data per source. ITF & Eurostat (Step 1) 37 28% CountrySource specific Type source (Step 2) Number52 of Countries Percentage40% of Countries Regression-based estimates (Step 3) 42 32% ITF & EurostatTotal (Step 1)131 37100% 28% Country specific source (Step 2) 52 40% Regression-basedStep 1 estimates (Step 3) 42 32% The firstTotal step was to use international, reputable 131 databases that systematically compile 100% and report transport modal split information. Parameters on these databases are regularly updated, cleaned, and reported following certain standards. Retrieving data directly from these centrally- Step 1 managed sources helps safeguard the data in terms of their quality and consistency. In this step, data were identified for 37 countries using the following sources: The first(a) step The was ITF to(International use international, Transport Forum) reputable Transp databasesort Statistics thatdatabase systematically [6], which provides compile the and report transport modalmodal split share information. information Parametersfor various countries on these across databases the globe that are are regularly members updated,of the cleaned, Organization for Economic Co-operation and Development (OECD). The reported cases are and reported followingfrom various certain years; 47% standards. of data were Retrievingfrom 2018, 23% data from 2017, directly 13% from from 2016, these and 17% centrally-managed from sources helps safeguard2015 or earlier. the data in terms of their quality and consistency. In this step, data were identified for(b) 37 The countries Eurostat,using the Statistical the following Office of the sources: European Union [7], which provides a comprehensive database with a broad range of transport statistics (including modal shares) for all of the European Union countries as well as for candidate countries and those belonging to the (a) The ITF (InternationalEuropean Free Trade Transport Association. Forum) 63% of data Transport were from 2017, Statistics 7% from database2016, 7% from [ 62015,], which and provides the modal25% share from information 2014 or earlier. for various countries across the globe that are members of the Organization for Economic Co-operation and Development (OECD). The reported cases are from various years; 47% of data were from 2018, 23% from 2017, 13% from 2016, and 17% from 2015 or earlier. (b) The Eurostat, the Statistical Office of the European Union [7], which provides a comprehensive database with a broad range of transport statistics (including modal shares) for all of the European Union countries as well as for candidate countries and those belonging to the European Free Trade Association. 63% of data were from 2017, 7% from 2016, 7% from 2015, and 25% from 2014 or earlier. Step 2 For countries that are not covered in these international databases, the second step was to source data individually; data for 53 countries were compiled in this step. The relevant documents were identified through a systematic online search using keywords such as “passenger modal share”, “passenger modal split”, “passenger mode share”, or similar. Upon the identification of World 2020, 1 36 appropriate sources, we manually reviewed them to extract the relevant shares for the specified transport modes (for further details on the transport modes covered, see Section3). For several countries, official transportation data only report city-level traffic, mainly covering passenger transport in the metropolitan areas. In these cases, transport shares were weighted against their city population to proxy the nationwide modal share pattern. These individual sources included: (c) Reports from acknowledged international organizations or associations with particular focus on passenger mobility, such as the World Bank [8], the International Association of Public Transport [9], the Japan International Cooperation Agency [10], the Global BRT Data [11], and the United Nations Economic Commission for Europe [12]. 11% of data were from 2019–2018, 42% from 2017–2014, and 47% from 2013 or earlier. (d) National governance or policy documents, such as mobility reports, national transport strategies, and assessments or urban mobility plans that are publicly available. 33% of data were from 2015–2012, 50% of data were from 2010–2009, and the rest were from 2004. It should be noted that sources (c) and (d) were mostly used to identify modal shares in low-income or developing countries, where comprehensive transport databases are not typically held by Public Authorities [13]. Special consideration was given to countries with unique mobility characteristics in terms of preferred transport modes. These characteristics may refer to the extensive use of private motorized modes other than a passenger car (e.g., two or three wheelers, auto rickshaw),
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