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Exploring travel patterns and static rebalancing strategies for dockless bike-sharing systems from multi-source data: a framework and case study.
- Source :
- Transportation Letters; May2023, Vol. 15 Issue 4, p336-349, 14p
- Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- ADMINISTRATIVE & political divisions
HEURISTIC algorithms
CYCLING
PUBLIC transit
Subjects
Details
- Language :
- English
- ISSN :
- 19427867
- Volume :
- 15
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- Transportation Letters
- Publication Type :
- Academic Journal
- Accession number :
- 163407756
- Full Text :
- https://doi.org/10.1080/19427867.2022.2051798