The analysis of catchment areas of metro stations using trajectory data generated by dockless shared bikes
place - asia, place - urban, mode - bike, mode - subway/metro, planning - methods
Dockless bike sharing, Bike trajectories, Map matching, Bike catchment area, Shanghai metro
The dockless bike-sharing system is getting popular and widely used for connecting with public transportation. This study addresses questions on how the catchment areas of metro stations are influenced by the dockless bike-sharing system and what their characteristics are. We develop methods to process bike trajectories and generate the bike catchment areas of metro stations. The proposed methods are applied to generate the bike catchment areas of the metro stations in Shanghai as a case study. We then conduct analyses to answer our research questions in three aspects. First, we analyze the spatial distribution patterns of the bike catchment areas and determine that the sizes of bike catchment areas increase from the city center to the suburban area. Second, using two indicators, namely coverage ratio and overlap degree, we examine the impact of dockless bike sharing on the catchment areas as compared with 800 m pedestrian catchment areas. As a resut, the catchment coverage ratio of the central city is increased by 104% and the maximum overlap degree increases from five to nine stations. Third, we apply regression models to explore the factors associated with the sizes of the bike catchment areas. The results show that the sizes of the bike catchment areas are positively associated with good metro service, frequent morning trips, diverse users, and large distances to the city center and terminal stations, but negatively associated with the density of metro stations. Based on the analytical results, we outline the application potentials and implications for relevant planning.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Lin, D., Zhang, Y., Zhu, R., & Meng, L. (2019). The analysis of catchment areas of metro stations using trajectory data generated by dockless shared bikes. Sustainable Cities and Society, Vol. 49, article 101598.