How urban density, network topology and socio-economy influence public transport ridership: Empirical evidence from 48 European metropolitan areas

Understanding the determinants of public transport ridership is important in order to plan attractive public transport systems efficiently. This study analyses at a meta-level per capita public transport ridership across 48 European cities based on a rich database collected as part of this study. The dataset includes detailed mode-specific information about the public transport networks, hence extending previous research by analysing each public transport mode separately while simultaneously taking into account the main determinants of ridership identified by a thorough literature review of 36 previous studies, e.g. urban demographics and land uses.

Factor analysis was deployed revealing four main composite determinants, namely i) metro coverage, network connectivity, and urban density, ii) suburban rail coverage, iii) economic inequality, and iv) light rail coverage. Subsequent multiple regression analysis confirmed the a priori hypothesis of per capita ridership being positively associated with the extent of network coverage in terms of metro, suburban rail and light rail transit.

The importance of network connectivity was included with results suggesting that the number of transfer stations was more important than the cyclomatic number of the public transport network. Cities with higher economic inequality in terms of higher unemployment, lower per capita GDP and higher GINI-coefficient showed lower public transport ridership.

Finally, the analyses highlighted the importance of proper definitions of urban areas in order to perform consistent analyses of data across cities. This revealed the impact on transit ridership of urban density defined as population and especially job intensity per km2. As the study is based on aggregate, cross-sectional data from a relatively small sample of European cities, it is not without limitations in terms of mainly revealing correlational structures rather than causations as well as not including all variables related to public transport ridership.

Future studies should further investigate these interrelationships before drawing conclusions on the causational relationships.