Exploring travel patterns and static rebalancing strategies for dockless bike-sharing systems from multi-source data: a framework and case study

ABSTRACT

This paper proposes a research framework for investigating the travel patterns of dockless bike-sharing and accomplishing the large-scale bike rebalancing at the city level. A case study involving Shanghai combines GPS-based bike-sharing usage data and road network data. First, the spatiotemporal mobility patterns are analyzed visually; then community detection is used to divide the study area into management sub-areas according to the mobility characteristics of bike-sharing users; in addition, a clustering algorithm is used to identify virtual stations. On this basis, a heuristic algorithm is used to generate a rebalancing scheme that enables multiple visits to a given station. The results show that Shanghai can be divided into 28 bike-sharing management sub-areas. Static rebalancing based on the identified management sub-areas reduces the number and driving distance of rebalancing vehicles in use, which is a better outcome than that with a method based on administrative divisions.

KEYWORDS:

• Dockless bike-sharing system
• travel pattern
• community detection
• static bike-sharing rebalancing
• public transport
• sustainable transport

Acknowledgments

The authors are grateful to anyone who provided comments on the earlier version of this study and gratefully acknowledge the contribution of anonymous reviewers to this study’s improvement.

Disclosure statement

No potential conflict of interest was reported by the author(s).