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Relocating shared automated vehicles under parking constraints: assessing the impact of different strategies for on-street parking

Author

Listed:
  • Konstanze Winter

    (Technion - Israel Institute of Technology)

  • Oded Cats

    (Delft University of Technology)

  • Karel Martens

    (Delft University of Technology)

  • Bart Arem

    (Delft University of Technology)

Abstract

With shared mobility services becoming increasingly popular and vehicle automation technology advancing fast, there is an increasing interest in analysing the impacts of large-scale deployment of shared automated vehicles. In this study, a large fleet of shared automated vehicles providing private rides to passengers is introduced to an agent-based simulation model based on the city of Amsterdam, the Netherlands. The fleet is dimensioned for a sufficient service efficiency during peak-hours, meaning that in off-peak hours a substantial share of vehicles is idle, requiring vehicle relocation strategies. This study assesses the performance of zonal pro-active relocation strategies for on-demand passenger transport under constrained curbside parking capacity: (1) demand-anticipation, (2) even supply dispersion and (3) balancing between demand and supply of vehicles. The strategies are analysed in regard to service efficiency (passenger waiting times, operational efficiency), service externalities (driven mileage, parking usage) and service equity (spatial distribution of externalities and service provision). All pro-active relocation strategies are outperformed by a naïve remain-at-drop off-location strategy in a scenario where curbside parking capacity is in abundance. The demand-anticipation heuristic leads to the highest average waiting times due to vehicle bunching at demand-hotspots which results in an uneven usage of parking facilities. The most favourable results in regard to service efficiency and equity are achieved with the heuristics balancing demand and supply, at the costs of higher driven mileage due to the relocation of idle vehicles. These results open up opportunities for municipalities to accompany the introduction of large fleets of shared automated vehicles with suitable curbside management strategies that mitigate undesired effects.

Suggested Citation

  • Konstanze Winter & Oded Cats & Karel Martens & Bart Arem, 2021. "Relocating shared automated vehicles under parking constraints: assessing the impact of different strategies for on-street parking," Transportation, Springer, vol. 48(4), pages 1931-1965, August.
    • Handle: RePEc:kap:transp:v:48:y:2021:i:4:d:10.1007_s11116-020-10116-w
    DOI: 10.1007/s11116-020-10116-w
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    Cited by:

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    2. Imen Haj Salah & Vasu Dev Mukku & Malte Kania & Tom Assmann & Hartmut Zadek, 2022. "Implications of the Relocation Type and Frequency for Shared Autonomous Bike Service: Comparison between the Inner and Complete City Scenarios for Magdeburg as a Case Study," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    3. Levin, Michael W., 2022. "A general maximum-stability dispatch policy for shared autonomous vehicle dispatch with an analytical characterization of the maximum throughput," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 258-280.

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