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Bike-sharing systems: User dissatisfaction in the presence of unusable bicycles

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  • Mor Kaspi
  • Tal Raviv
  • Michal Tzur

Abstract

In bike-sharing systems, at any given moment, a certain share of the bicycle fleet is unusable. This phenomenon may significantly affect the quality of service provided to the users. However, to date this matter has not received any attention in the literature. In this article, the users' quality of service is modeled in terms of their satisfaction from the system. We measure user dissatisfaction using a weighted sum of the expected shortages of bicycles and lockers at a single station. The shortages are evaluated as a function of the initial inventory of usable and unusable bicycles at the station. We analyze the convexity of the resulting bivariate function and propose an accurate method for fitting a convex polyhedral function to it. The fitted polyhedral function can later be used in linear optimization models for operational and strategic decision making in bike-sharing systems. Our numerical results demonstrate the significant effect of the presence of unusable bicycles on the level of user dissatisfaction. This emphasizes the need to have accurate real-time information regarding bicycle usability.

Suggested Citation

  • Mor Kaspi & Tal Raviv & Michal Tzur, 2017. "Bike-sharing systems: User dissatisfaction in the presence of unusable bicycles," IISE Transactions, Taylor & Francis Journals, vol. 49(2), pages 144-158, February.
    • Handle: RePEc:taf:uiiexx:v:49:y:2017:i:2:p:144-158
    DOI: 10.1080/0740817X.2016.1224960
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    Citations

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    Cited by:

    1. Negahban, Ashkan, 2019. "Simulation-based estimation of the real demand in bike-sharing systems in the presence of censoring," European Journal of Operational Research, Elsevier, vol. 277(1), pages 317-332.
    2. Caggiani, Leonardo & Camporeale, Rosalia & Marinelli, Mario & Ottomanelli, Michele, 2019. "User satisfaction based model for resource allocation in bike-sharing systems," Transport Policy, Elsevier, vol. 80(C), pages 117-126.
    3. Alexandros Nikitas, 2019. "How to Save Bike-Sharing: An Evidence-Based Survival Toolkit for Policy-Makers and Mobility Providers," Sustainability, MDPI, vol. 11(11), pages 1-17, June.
    4. Saif Benjaafar & Daniel Jiang & Xiang Li & Xiaobo Li, 2022. "Dynamic Inventory Repositioning in On-Demand Rental Networks," Management Science, INFORMS, vol. 68(11), pages 7861-7878, November.
    5. Zhou, Yu & Kou, Gang & Guo, Zhen-Zhu & Xiao, Hui, 2023. "Availability analysis of shared bikes using abnormal trip data," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    6. Zhang, Liye & Xiao, Zhe & Ren, Shen & Qin, Zheng & Goh, Rick Siow Mong & Song, Jie, 2022. "Measuring the vulnerability of bike-sharing system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 353-369.
    7. 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).
    8. Zhou, Yu & Chen, Yang & Liu, Shenyan & Kou, Gang, 2024. "Availability simulation and transfer prediction for bike sharing systems based on discrete event simulation," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    9. Quan-Lin Li & Rui-Na Fan, 2022. "A mean-field matrix-analytic method for bike sharing systems under Markovian environment," Annals of Operations Research, Springer, vol. 309(2), pages 517-551, February.
    10. Si, Hongyun & Su, Yangyue & Wu, Guangdong & Liu, Bingsheng & Zhao, Xianbo, 2020. "Understanding bike-sharing users’ willingness to participate in repairing damaged bicycles: Evidence from China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 203-220.

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