IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i10p4293-d1397721.html
   My bibliography  Save this article

Public Bicycle Dispatch Method Based on Spatiotemporal Characteristics of Borrowing and Returning Demands

Author

Listed:
  • Zhizhen Liu

    (Hunan International Scientific and Technological Innovation Cooperation Base of Advanced Construction and Maintenance Technology of Highway, Changsha University of Science & Technology, Changsha 410205, China
    School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410205, China)

  • Ziyi Wu

    (School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410205, China)

  • Feng Tang

    (School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410205, China)

  • Chao Gao

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China
    School of Engineering and Design, Technical University of Munich, 80333 Munich, Germany)

  • Hong Chen

    (College of Transportation Engineering, Chang’an University, Xi’an 710064, China)

  • Wang Xiang

    (School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410205, China)

Abstract

Public bicycle systems (PBSs) serve as the ‘last mile’ of public transportation for urban residents, yet the problem of the difficulty in borrowing and returning bicycles during peak hours remains a major bottleneck restricting the intelligent and efficient operation of public bicycles. Previous studies have proposed reasonable models and efficient algorithms for optimizing public bicycle scheduling, but there is still a lack of consideration for actual road network distances between stations and the temporal characteristics of demand at rental points in the model construction process. Therefore, this paper aims to construct a public bicycle dispatch framework based on the spatiotemporal characteristics of borrowing and returning demands. Firstly, the spatiotemporal distribution characteristics of borrowing and returning demands for public bicycles are explored, the origin–destination (OD) correlation coefficients are defined, and the intensity of connections between rental point areas is analyzed. Secondly, based on the temporal characteristics of rental point demands, a random forest prediction model is constructed with weather factors, time characteristics, and rental point locations as feature variables, and station bicycle-borrowing and -returning demands as the target variable. Finally, bicycle dispatch regions are delineated based on actual path distances between stations and OD correlation coefficients, and a public bicycle regional dispatch optimization method is established. Taking the PBS in Ningbo City as an example, the balancing optimization framework proposed in this paper is validated. The results show that the regional dispatch optimization method proposed in this paper can achieve optimized dispatch of public bicycles during peak hours. Additionally, compared with the Taboo search algorithm (TSA), the genetic algorithm (GA) exhibits a 11.1% reduction in rebalancing time and a 40.4% reduction in trip cost.

Suggested Citation

  • Zhizhen Liu & Ziyi Wu & Feng Tang & Chao Gao & Hong Chen & Wang Xiang, 2024. "Public Bicycle Dispatch Method Based on Spatiotemporal Characteristics of Borrowing and Returning Demands," Sustainability, MDPI, vol. 16(10), pages 1-28, May.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:10:p:4293-:d:1397721
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/10/4293/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/10/4293/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maggioni, Francesca & Cagnolari, Matteo & Bertazzi, Luca & Wallace, Stein W., 2019. "Stochastic optimization models for a bike-sharing problem with transshipment," European Journal of Operational Research, Elsevier, vol. 276(1), pages 272-283.
    2. Lv, Chang & Zhang, Chaoyong & Lian, Kunlei & Ren, Yaping & Meng, Leilei, 2022. "A two-echelon fuzzy clustering based heuristic for large-scale bike sharing repositioning problem," Transportation Research Part B: Methodological, Elsevier, vol. 160(C), pages 54-75.
    3. Legros, Benjamin, 2019. "Dynamic repositioning strategy in a bike-sharing system; how to prioritize and how to rebalance a bike station," European Journal of Operational Research, Elsevier, vol. 272(2), pages 740-753.
    4. Cai, Yutong & Ong, Ghim Ping & Meng, Qiang, 2022. "Dynamic bicycle relocation problem with broken bicycles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    5. Martin, Layla & Minner, Stefan, 2021. "Feature-based selection of carsharing relocation modes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    6. Sun, Daniel(Jian) & Ding, Xueqing, 2019. "Spatiotemporal evolution of ridesourcing markets under the new restriction policy: A case study in Shanghai," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 227-239.
    7. Li, Yanfeng & Liu, Yang, 2021. "The static bike rebalancing problem with optimal user incentives," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 146(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Qingxin & Ma, Shoufeng & Li, Hongming & Zhu, Ning & He, Qiao-Chu, 2024. "Optimizing bike rebalancing strategies in free-floating bike-sharing systems: An enhanced distributionally robust approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    2. Guo, Yuhan & Li, Jinning & Xiao, Linfan & Allaoui, Hamid & Choudhary, Alok & Zhang, Lufang, 2024. "Efficient inventory routing for Bike-Sharing Systems: A combinatorial reinforcement learning framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 182(C).
    3. Hao, Wu & Martin, Layla, 2022. "Prohibiting cherry-picking: Regulating vehicle sharing services who determine fleet and service structure," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    4. Yongji Jia & Wang Zeng & Yanting Xing & Dong Yang & Jia Li, 2020. "The Bike-Sharing Rebalancing Problem Considering Multi-Energy Mixed Fleets and Traffic Restrictions," Sustainability, MDPI, vol. 13(1), pages 1-15, December.
    5. Neumann-Saavedra, Bruno Albert & Mattfeld, Dirk Christian & Hewitt, Mike, 2021. "Assessing the operational impact of tactical planning models for bike-sharing redistribution," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 216-235.
    6. Gleditsch, Marte D. & Hagen, Kristine & Andersson, Henrik & Bakker, Steffen J. & Fagerholt, Kjetil, 2024. "A column generation heuristic for the dynamic bicycle rebalancing problem," European Journal of Operational Research, Elsevier, vol. 317(3), pages 762-775.
    7. Gu, Wei & Yu, Xiaoru & Zhang, Shichen & Yan, Xiangbin & Wang, Chen, 2023. "To outsource or not: Bike-share rebalancing strategies under the service quality deviation of a third party," European Journal of Operational Research, Elsevier, vol. 310(2), pages 847-859.
    8. Wang, Yi-Jia & Kuo, Yong-Hong & Huang, George Q. & Gu, Weihua & Hu, Yaohua, 2022. "Dynamic demand-driven bike station clustering," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    9. Cheng, Yao & Wang, Junwei & Wang, Yan, 2021. "A user-based bike rebalancing strategy for free-floating bike sharing systems: A bidding model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    10. Gu, Wei & Li, Meng & Wang, Chen & Shang, Jennifer & Wei, Lirong, 2021. "Strategic sourcing selection for bike-sharing rebalancing: An evolutionary game approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    11. Li, Xiang & Wang, Xianzhe & Feng, Ziyan, 2024. "Dynamic repositioning in bike-sharing systems with uncertain demand: An improved rolling horizon framework," Omega, Elsevier, vol. 126(C).
    12. Gan, Jinxiang & Zhang, Guochuan & Zhang, Yuhao, 2024. "Bike rebalancing: How to find a balanced matching in the k center problem?," European Journal of Operational Research, Elsevier, vol. 316(3), pages 845-855.
    13. Fu, Chenyi & Zhu, Ning & Ma, Shoufeng & Liu, Ronghui, 2022. "A two-stage robust approach to integrated station location and rebalancing vehicle service design in bike-sharing systems," European Journal of Operational Research, Elsevier, vol. 298(3), pages 915-938.
    14. Fu, Chenyi & Ma, Shoufeng & Zhu, Ning & He, Qiao-Chu & Yang, Hai, 2022. "Bike-sharing inventory management for market expansion," Transportation Research Part B: Methodological, Elsevier, vol. 162(C), pages 28-54.
    15. Zhou, Yaoming & Lin, Zeyu & Guan, Rui & Sheu, Jiuh-Biing, 2023. "Dynamic battery swapping and rebalancing strategies for e-bike sharing systems," Transportation Research Part B: Methodological, Elsevier, vol. 177(C).
    16. Xue Bai & Ning Ma & Kwai-Sang Chin, 2022. "Hybrid Heuristic for the Multi-Depot Static Bike Rebalancing and Collection Problem," Mathematics, MDPI, vol. 10(23), pages 1-28, December.
    17. Chen, Fangxi & Yin, Zhiwei & Ye, Yingwei & Sun, Daniel(Jian), 2020. "Taxi hailing choice behavior and economic benefit analysis of emission reduction based on multi-mode travel big data," Transport Policy, Elsevier, vol. 97(C), pages 73-84.
    18. Jiayue Xun & Min Zhang & Gaofeng Xu & Xinyue Guo, 2024. "Diversity and Influencing Factors of Public Service Facilities in Urban (Suburban) Railway Life Circle—Evidence from Beijing Subway Line S1, China," Land, MDPI, vol. 13(8), pages 1-20, August.
    19. Cai, Yutong & Ong, Ghim Ping & Meng, Qiang, 2022. "Dynamic bicycle relocation problem with broken bicycles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    20. Hu, Yujie & Zhang, Yongping & Lamb, David & Zhang, Mingming & Jia, Peng, 2019. "Examining and optimizing the BCycle bike-sharing system – A pilot study in Colorado, US," Applied Energy, Elsevier, vol. 247(C), pages 1-12.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:10:p:4293-:d:1397721. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.