Quantitative analysis of the relationships between dockless bike sharing and public transport: A trip-level perspective

Abstract

The widespread expansion of dockless bike sharing (DBS) services has had non-negligible effects on public transport systems by establishing intricate connections with public transport modes, such as metro and bus. An accurate understanding of the intricate relationships between DBS and public transport is crucial for promoting synergistic operations and maximizing the benefits. This study presents a novel quantitative analysis methodology from a trip-level perspective based on multi-source data to explore the relationships between dockless bike sharing and public transport without reliance on geospatial thresholds. By utilizing a comprehensive dataset encompassing bike sharing service operations, public transit facilities, and optimal route choices, we introduce the concepts of degree of site proximity (DSP) and degree of trip substitution (DTS), which are designed to assess the opportunities for cooperation and competition in DBS trips. Clustering techniques categorize recorded trips into specific types: competition, cooperation, cooperation-competition, and independence. A survey of dockless bike sharing users in Shanghai, China was conducted to obtain data on usage of DBS and the alternative choices without DBS. A comparison between the survey results and the estimates from the proposed methodology validates its effectiveness. In-depth analyses of factors such as weekends, cycling distances, and station densities reveal patterns of variation in the relationship between DBS and public transport systems. These findings provide valuable insights for urban planners and policymakers to enhance the integration of DBS and public transport systems, thereby improving the overall efficiency and sustainability of urban transportation networks.