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A data-driven dynamic repositioning model in bicycle-sharing systems

Author

Listed:
  • Zhang, Jie
  • Meng, Meng
  • Wong, Yiik Diew
  • Ieromonachou, Petros
  • Wang, David Z.W.

Abstract

The new generation of bicycle-sharing is an O2O (online-to-offline) platform service that enables the users to access the bicycle with a smartphone App. This paper proposes a dynamic repositioning model with predicted demand, where the repositioning time interval is fixed. A data-driven Neural Network (NN) approach is introduced to forecast the bicycle-sharing demand. The repositioning objective function at each time interval is defined to simultaneously minimize the operator cost and penalty cost. In addition to the normal constraints in static repositioning problem, flow conservation, inventory-balance and travel time constraints are taken into account. Due to the non-deterministic polynomial-time hard (NP-hard) nature of this model, a hybrid metaheuristic approach of Adaptive Genetic Algorithm (AGA) and Granular Tabu Search (GTS) algorithm is applied to calculate the solution. Based on predicted demand, the initial repositioning plan is made by AGA statically at the beginning of study horizon, which ensures the global optimization of the first solution. As time goes on, repositioning plan is checked and updated according to the real-usage patterns using GTS algorithm, which has the advantage of high-performance local-search within a short computing time. Numerical analysis is conducted using the real cases. The simulation results reveal that the proposed methodology can effectively model the dynamic repositioning problem in response to real-time bicycle-sharing usage. The proposed methodology can be a value-added tool in enhancing the feasibility and sustainability of bicycle-sharing program.

Suggested Citation

  • Zhang, Jie & Meng, Meng & Wong, Yiik Diew & Ieromonachou, Petros & Wang, David Z.W., 2021. "A data-driven dynamic repositioning model in bicycle-sharing systems," International Journal of Production Economics, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:proeco:v:231:y:2021:i:c:s0925527320302693
    DOI: 10.1016/j.ijpe.2020.107909
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    Cited by:

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    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. Liu, Yixiao & Tian, Zihao & Pan, Baoran & Zhang, Wenbin & Liu, Yunqi & Tian, Lixin, 2022. "A hybrid big-data-based and tolerance-based method to estimate environmental benefits of electric bike sharing," Applied Energy, Elsevier, vol. 315(C).
    4. Jaume Torres & Enrique Jiménez-Meroño & Francesc Soriguera, 2024. "Forecasting the Usage of Bike-Sharing Systems through Machine Learning Techniques to Foster Sustainable Urban Mobility," Sustainability, MDPI, vol. 16(16), pages 1-14, August.
    5. Xu Zhao & Jie Zhang & Ning Zhang & Yiik Diew Wong & Yufang Zhou & Meng Meng, 2021. "A GIS-CA Model for Planning Bikeways upon the Footpath Network," Sustainability, MDPI, vol. 13(16), pages 1-11, August.
    6. Jara-Díaz, Sergio & Latournerie, André & Tirachini, Alejandro & Quitral, Félix, 2022. "Optimal pricing and design of station-based bike-sharing systems: A microeconomic model," Economics of Transportation, Elsevier, vol. 31(C).

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