Allocation strategies in a dockless bike sharing system: a community structure-based approach
This study develops a methodology to determine the optimal allocation position to deploy the bikes in a competitive dockless bike sharing market. The community structure approach in complex network theory is utilized to offer the bike allocation strategies to the market leader in two specific market regimes, with a potential competitor, and without a potential competitor. Two different heuristics are proposed to handle the two scenarios respectively due to different design objectives, wherein the first one aims to attract maximum users and the other one aims to use minimum resources to cover maximum service area. Two hypothetic networks are adopted to illustrate the difference in design under these two regimes. Two numerical studies – a simplified Sioux Falls network and a real network in Singapore – are used to demonstrate the algorithm performance and show the applicability of the model for the scenario that no potential competitor exists.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/15568318
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Supplemental Notes:
- © 2021 Taylor & Francis Group, LLC. Abstract reprinted with permission of Taylor & Francis.
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Authors:
- Zhang, Jian
- Meng, M
- Wang, David Z W
- Du, B
- Publication Date: 2022-2
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 95-104
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Serial:
- International Journal of Sustainable Transportation
- Volume: 16
- Issue Number: 2
- Publisher: Taylor & Francis
- ISSN: 1556-8318
- EISSN: 1556-8334
- Serial URL: http://www.tandfonline.com/loi/ujst20
Subject/Index Terms
- TRT Terms: Bicycles; Communities; Competition; Complex systems; Market structure; Networks; Vehicle sharing
- Geographic Terms: Singapore
- Subject Areas: Administration and Management; Operations and Traffic Management; Pedestrians and Bicyclists; Planning and Forecasting; Vehicles and Equipment;
Filing Info
- Accession Number: 01835851
- Record Type: Publication
- Files: TRIS
- Created Date: Feb 11 2022 5:13PM