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Simulating a rich ride-share mobility service using agent-based models

Author

Listed:
  • Pau Segui-Gasco

    (Immense Simulations Limited)

  • Haris Ballis

    (Transport Systems Catapult)

  • Vittoria Parisi

    (Immense Simulations Limited)

  • David G. Kelsall

    (Immense Simulations Limited)

  • Robin J. North

    (Immense Simulations Limited)

  • Didac Busquets

    (Immense Simulations Limited)

Abstract

Mobility as a Service (MaaS) is the integrated and on-demand offering of new mode-sharing transport schemes, such as ride-share, car-share or car-pooling. MaaS schemes may solve some of the most pressing mobility problems in large conurbations like London. However, MaaS schemes pose significant implementation challenges for operators and city authorities alike. With the existing transport and traffic planning tools, even basic questions do not have easy answers: e.g. how many vehicles are needed; how should they be deployed; what infrastructure changes are needed, and what will happen with congestion? This paper reports on the novel integration, through co-simulation of two independent agent-based simulators: MATSim and IMSim. MATSim generates realistic transport demand for a city: allocating travellers to the best mobility option according to their preferences; while IMSim provides a highly realistic operational execution of autonomous and manually driven transport fleets. We show how the simulation tools complement each other to deliver a superior Autonomous Mobility on Demand (AMoD) modelling capability. By combining the two, we can evaluate the impact of diverse AMoD scenarios from different standpoints: from a traveller’s perspective (e.g. satisfaction, service level, etc.); from an operator’s perspectives (e.g. cost, revenue, etc.); and from a city’s perspective (e.g. congestion, significant shifts between transport modes, etc.). The coupled simulation methods have underpinned the extensive MERGE Greenwich project investigation into the challenges of offering ride-share services in autonomous vehicles in the Royal Borough of Greenwich (London, UK) for travellers, service-operators, the city, and vehicle manufacturers.

Suggested Citation

  • Pau Segui-Gasco & Haris Ballis & Vittoria Parisi & David G. Kelsall & Robin J. North & Didac Busquets, 2019. "Simulating a rich ride-share mobility service using agent-based models," Transportation, Springer, vol. 46(6), pages 2041-2062, December.
    • Handle: RePEc:kap:transp:v:46:y:2019:i:6:d:10.1007_s11116-019-10012-y
    DOI: 10.1007/s11116-019-10012-y
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    References listed on IDEAS

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    1. Nicole Ronald & Russell Thompson & Stephan Winter, 2017. "Simulating ad-hoc demand-responsive transportation: a comparison of three approaches," Transportation Planning and Technology, Taylor & Francis Journals, vol. 40(3), pages 340-358, April.
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    4. Agatz, Niels & Erera, Alan & Savelsbergh, Martin & Wang, Xing, 2012. "Optimization for dynamic ride-sharing: A review," European Journal of Operational Research, Elsevier, vol. 223(2), pages 295-303.
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    Cited by:

    1. Mehdizadeh, Milad & Nordfjaern, Trond & Klöckner, Christian A., 2022. "A systematic review of the agent-based modelling/simulation paradigm in mobility transition," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    2. May, Anthony D. & Shepherd, Simon & Pfaffenbichler, Paul & Emberger, Günter, 2020. "The potential impacts of automated cars on urban transport: An exploratory analysis," Transport Policy, Elsevier, vol. 98(C), pages 127-138.

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