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Approximating the Performance of a “Last Mile” Transportation System

Author

Listed:
  • Hai Wang

    (Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Amedeo Odoni

    (Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

The Last Mile Problem refers to the provision of travel service from the nearest public transportation node to a home or office. We study the supply side of this problem in a stochastic setting, with batch demands resulting from the arrival of groups of passengers who request last-mile service at urban rail stations or bus stops. Closed-form approximations are derived for the performance of Last Mile Transportations Systems (LMTS) as a function of the fundamental design parameters of such systems. An initial set of results is obtained for the case wherein a fleet of vehicles of unit capacity provides the Last-Mile service, and each delivery route consists of a simple round trip between the rail station or bus stop and a single passenger’s destination. These results are then extended to the general case in which the capacity of a vehicle is a small number (up to 20). It is shown through comparisons with simulation results that the approximations perform consistently well for a broad and realistic range of input values and conditions. These expressions can therefore be used for the preliminary planning and design of an LMTS, especially for determining approximate resource requirements, such as the number of vehicles/servers needed to achieve some prespecified level of service, as measured by the expected waiting time until a passenger is picked up from the station or delivered to her destination.

Suggested Citation

  • Hai Wang & Amedeo Odoni, 2016. "Approximating the Performance of a “Last Mile” Transportation System," Transportation Science, INFORMS, vol. 50(2), pages 659-675, May.
    • Handle: RePEc:inm:ortrsc:v:50:y:2016:i:2:p:659-675
    DOI: 10.1287/trsc.2014.0553
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    References listed on IDEAS

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    Cited by:

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    2. Ma, Tai-Yu & Rasulkhani, Saeid & Chow, Joseph Y.J. & Klein, Sylvain, 2019. "A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 417-442.
    3. Zhu, Zheng & Qin, Xiaoran & Ke, Jintao & Zheng, Zhengfei & Yang, Hai, 2020. "Analysis of multi-modal commute behavior with feeding and competing ridesplitting services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 713-727.
    4. Rui Yao & Kenan Zhang, 2023. "How would mobility-as-a-service (MaaS) platform survive as an intermediary? From the viewpoint of stability in many-to-many matching," Papers 2310.08285, arXiv.org.
    5. Konrad Steiner & Stefan Irnich, 2018. "Strategic Planning for Integrated Mobility-on-Demand and Urban Public Bus Networks," Working Papers 1819, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.

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