Motorized transport currently accounts for more than 15% of world greenhouse gas emissions1. As most humans live in urban areas and two-thirds of world population will live in cities by 20502, mitigating car traffic in cities has become crucial for limiting climate change effects3,4,5,6. Daily commuting is the main driver for passenger car use – about 75% of American commuters drive everyday (U.S. Department of Transportation, Bureau of Transportation Statistics, National Transportation Statistics. Table 1–41 at (2016)) – while alternative transport modes such as public transportation networks are unevenly developed among countries and cities (List of Metro Systems, Wikimedia Foundation, 2020).

Over the last decades, various attempts to assess the environmental impact of car use in cities have emerged from multiple fields, ranging from econometric studies to physics or urban studies7,8,9,10,11. A seminal result of transport theory, by Newman and Kenworthy10, correlated transport-related emissions with a determinant spatial criterion: urban density. Alternatively, Duranton and Turner11 claimed that public transport services were unsuccessful in reducing traffic, as transit riders lured off the roads are replaced by new drivers on the released roads. Such results, however, crucially lack both theoretical and empirical foundations12,13,14,15 and new research16 shows that the two main critical factors that control car traffic in cities are urban sprawl and access to mass rapid transit (MRT).

More generally, understanding mobility in urban areas is fundamental, not only for transport planning, but also for understanding many processes in cities, such as congestion problems, or epidemic spread17,18 for example. But what is a good measure of access to transit? Studies have mainly focused on the number of lines or stops19,20,21, length of the network or graph analysis22,23,24. Few works16,25,26, however, have considered investigating catchment areas of MRT stations, i.e. looking at the share of population living close to MRT stations, for instance within walking distance. Such conditions have however proved to be essential in explaining commuting behaviours and mobility patterns16.

The most detailed definition of such catchment metrics is the People Near Transit (PNT), and originates from a 2016 publication from the Institute for Transportation and Development Policy (IDTP)25. It produces a rigorous dataset of the share of population living close to transit (less than 1 km) for 25 cities in the world (12 in OECD countries). However, definitions of urban areas and rapid transit systems in that dataset are multiple and need to be refined while the number of cities must be expanded.

Hence, in order to expand our global knowledge of urban mobility, we need a common, unified and universal definition of access to public transit as well as sound measures of such a quantity. In this paper, we clarify its definition and propose what is to our knowledge the largest global dataset of PNT.

Our analysis uses functional urban areas (FUA) in OECD countries, a consistent definition of cities across several countries27. We restrict our measures to mass rapid transit, usually referring to high-capacity heavy rail public transport, to which we added light rails and trams. In our sense, mass rapid transit thus encompasses:

  • Tram, streetcar or light rail services.

  • Subway, Metro or any underground service.

  • Suburban rail services.

Buses are not comprised in that definition. In contrast with25, we do not exclude any form of commuting trains based on station spacing or schedule criteria. As we detail it in the Method section, we identify services and corresponding stops with the General Transit Feed Specification (GTFS), a common format for public transportation schedules and associated geographic information (GTFS Static Overview., 2020).

Crossing open-access information from public transport agencies in OECD urban areas with population-grid estimates of world population28, we publish here a list of 85 OECD cities (see Fig. 1) for which we were able to compute the People Near Transit (PNT) levels defined as the share of urban population living at geometric distances of 500 m, 1,000 m and 1,500 m from any MRT station in the agglomeration:

$${rm{PNT(}}d{rm{)}}=frac{{rm{population}},{rm{s}}{rm{.}},{rm{t}}{rm{.}},{rm{euclidean}},{rm{minimum}},{rm{distance}},{rm{ < }},d}{{rm{total}},{rm{population}}}$$


where d = 500, 1000, 1500.

Fig. 1

The 85 OECD cities for which we found data are mostly found in Europe and in North America (MapTiler Basic and MapTiler Topo., 2020).

We display on Tables 1 and 2 the 5 cities with easiest access to MRT (largest PNT) and the 5 cities with scarcest access to MRT (smallest PNT).

Table 1 Population Near Transit values: Share of population living within catchment area from a MRT station at thresholds 500 m, 1000 m and 1500 m. Top 5 cities with easiest (1000 m) access to MRT.
Table 2 Population Near Transit values: Share of population living within catchment area from a MRT station at thresholds 500 m, 1000 m and 1500 m. 5 cities with poorest (1000 m) access to MRT.

We also provide for each city the population grid-maps with corresponding MRT access level, i.e. grid-maps of MRT catchment areas at different distances with population in each grid. As an example, Fig. 2 shows the 1000 m catchment area of MRT stations in Paris.

Fig. 2

1000 m catchment areas of MRT stations (in orange) in Paris functional urban area (boundaries are in black) (MapTiler Basic and MapTiler Topo., 2020).

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