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Modeling an enhanced ridesharing system with meet points and time windows

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  • Xin Li
  • Sangen Hu
  • Wenbo Fan
  • Kai Deng

Abstract

With the rising of e-hailing services in urban areas, ride sharing is becoming a common mode of transportation. This paper presents a mathematical model to design an enhanced ridesharing system with meet points and users’ preferable time windows. The introduction of meet points allows ridesharing operators to trade off the benefits of saving en-route delays and the cost of additional walking for some passengers to be collectively picked up or dropped off. This extension to the traditional door-to-door ridesharing problem brings more operation flexibility in urban areas (where potential requests may be densely distributed in neighborhood), and thus could achieve better system performance in terms of reducing the total travel time and increasing the served passengers. We design and implement a Tabu-based meta-heuristic algorithm to solve the proposed mixed integer linear program (MILP). To evaluate the validation and effectiveness of the proposed model and solution algorithm, several scenarios are designed and also resolved to optimality by CPLEX. Results demonstrate that (i) detailed route plan associated with passenger assignment to meet points can be obtained with en-route delay savings; (ii) as compared to CPLEX, the meta-heuristic algorithm bears the advantage of higher computation efficiency and produces good quality solutions with 8%~15% difference from the global optima; and (iii) introducing meet points to ridesharing system saves the total travel time by 2.7%-3.8% for small-scale ridesharing systems. More benefits are expected for ridesharing systems with large size of fleet. This study provides a new tool to efficiently operate the ridesharing system, particularly when the ride sharing vehicles are in short supply during peak hours. Traffic congestion mitigation will also be expected.

Suggested Citation

  • Xin Li & Sangen Hu & Wenbo Fan & Kai Deng, 2018. "Modeling an enhanced ridesharing system with meet points and time windows," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-19, May.
    • Handle: RePEc:plo:pone00:0195927
    DOI: 10.1371/journal.pone.0195927
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    Cited by:

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    2. Shaheen, Susan & Darling, Wesley & Broader, Jacquelyn & Cohen, Adam, 2021. "Understanding Curb Management and Targeted Incentive Policies to Increase Transportation Network Company Pooling and Public Transit Linkages," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6gz9w0v9, Institute of Transportation Studies, UC Berkeley.
    3. Lei Zhu & Zhouqiao Zhao & Guoyuan Wu, 2021. "Shared Automated Mobility with Demand-Side Cooperation: A Proof-of-Concept Microsimulation Study," Sustainability, MDPI, vol. 13(5), pages 1-17, February.
    4. Fielbaum, Andrés & Tirachini, Alejandro & Alonso-Mora, Javier, 2023. "Economies and diseconomies of scale in on-demand ridepooling systems," Economics of Transportation, Elsevier, vol. 34(C).
    5. Sepide Lotfi & Khaled Abdelghany, 2022. "Ride matching and vehicle routing for on-demand mobility services," Journal of Heuristics, Springer, vol. 28(3), pages 235-258, June.
    6. Yi, Xu & Lian, Feng & Yang, Zhongzhen, 2022. "Research on commuters’ carpooling behavior in the mobile internet context," Transport Policy, Elsevier, vol. 126(C), pages 14-25.
    7. Andres Fielbaum & Alejandro Tirachini & Javier Alonso-Mora, 2021. "New sources of economies and diseconomies of scale in on-demand ridepooling systems and comparison with public transport," Papers 2106.15270, arXiv.org, revised Jul 2021.

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