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Dynamic prediction-based relocation policies in one-way station-based carsharing systems with complete journey reservations

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  • Repoux, Martin
  • Kaspi, Mor
  • Boyacı, Burak
  • Geroliminis, Nikolas

Abstract

In this paper, we study the operations of a one-way station-based carsharing system implementing a complete journey reservation policy. We consider the percentage of served demand as a primary performance measure and analyze the effect of several dynamic staff-based relocation policies. Specifically, we introduce a new proactive relocation policy based on Markov chain dynamics that utilizes reservation information to better predict the future states of the stations. This policy is compared to a state-of-the art staff-based relocation policy and a centralistic relocation model assuming full knowledge of the demand. Numerical results from a real-world implementation and a simulation analysis demonstrate the positive impact of dynamic relocations and highlight the improvement in performance obtained with the proposed proactive relocation policy.

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  • Repoux, Martin & Kaspi, Mor & Boyacı, Burak & Geroliminis, Nikolas, 2019. "Dynamic prediction-based relocation policies in one-way station-based carsharing systems with complete journey reservations," Transportation Research Part B: Methodological, Elsevier, vol. 130(C), pages 82-104.
    • Handle: RePEc:eee:transb:v:130:y:2019:i:c:p:82-104
    DOI: 10.1016/j.trb.2019.10.004
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    Cited by:

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    3. Golalikhani, Masoud & Oliveira, Beatriz Brito & Carravilla, Maria Antónia & Oliveira, José Fernando & Antunes, António Pais, 2021. "Carsharing: A review of academic literature and business practices toward an integrated decision-support framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    4. Huang, Kai & An, Kun & Rich, Jeppe & Ma, Wanjing, 2020. "Vehicle relocation in one-way station-based electric carsharing systems: A comparative study of operator-based and user-based methods," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    5. Stokkink, Patrick & Geroliminis, Nikolas, 2021. "Predictive user-based relocation through incentives in one-way car-sharing systems," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 230-249.
    6. 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).
    7. Luetian Sun & Rui Song, 2022. "Improving Efficiency in Congested Traffic Networks: Pareto-Improving Reservations through Agent-Based Timetabling," Sustainability, MDPI, vol. 14(4), pages 1-24, February.
    8. Zhang, Si & Sun, Huijun & Liu, Yang & Lv, Ying & Wu, Jianjun & Feng, Xiaoyan, 2024. "Carsharing equitable relocation problem: A two-stage stochastic programming approach with learning-embedded endogenous uncertainty in demand," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).
    9. Liu, Yang & Xie, Jiaohong & Chen, Nan, 2022. "Stochastic one-way carsharing systems with dynamic relocation incentives through preference learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    10. Obrenović, Nikola & Ataç, Selin & Bierlaire, Michel, 2024. "Light electric vehicle sharing systems: Functional design of a comprehensive decision making solution," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).

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