Tidal phenomenon of the dockless bike-sharing system and its causes: the case of Beijing

Abstract

Dockless bike-sharing system, as a flexible and eco-friendly solution to improve urban public transportation, has rapidly expanded in many cities around the world. The higher flexibility of the dockless bike-sharing system produces more significant tidal phenomenon that leads to serious traffic problems. However, as a new travel mode, the spatiotemporal characteristics of tidal phenomenon of the dockless bike-sharing system is unknown. This study proposed a method to quantify tidal traffic patterns of shared bikes in Beijing, the capital and megacity of China, and then applied multinomial logit model to reveal main causes of these patterns. Five traffic patterns were found on weekdays, among which three patterns display extreme convergence and divergence states during morning and evening rush hours. Only three patterns exist on weekends and the tidal traffic phenomenon becomes less intensive but lasts longer. Population is the most decisive factor, which determines the density of total traffic flow. Subsequently, resident-employment ratio controls the direction of commute flows thus causing tidal traffic on weekdays, while land use diversity and factors related to leisure activities are more influential on weekends. With the knowledge of tidal phenomenon of dockless bike-sharing usage, some operational strategies were suggested, such as optimizing the stock of the shared bikes in different time and locations, which will benefit bike-sharing enterprises and the local administrators to mitigate problems caused by tidal traffic and promote the usage and efficiency of dockless bike-sharing system.

Keywords:

• Beijing
• convergence and divergence
• dockless bike-sharing system
• human mobility
• tidal traffic phenomenon
• transit-oriented development (TOD)

Acknowledgement

The authors would like to thank the Academic Information Centre of Urban Planning, China Academy of Urban Planning and Design for providing part of the data.

Additional information

Funding

This work was supported by research grants from The Hong Kong Polytechnic University under Projects 1-ZE6Q and 9B0F, and funding from the PhD studentship of The Hong Kong Polytechnic University.