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Simulating Demand-responsive Transportation: A Review of Agent-based Approaches

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  • Nicole Ronald
  • Russell Thompson
  • Stephan Winter

Abstract

In light of the need to make better use of existing transport infrastructure, demand-responsive transportation (DRT) systems are gaining traction internationally. However, many systems fail due to poor implementation, planning, and marketing. Being able to realistically simulate a system to evaluate its viability before implementation is important. This review investigates the application of agent-based simulation for studying DRT. We identify that existing simulations are strongly focused on the optimisation of trips, usually in favour of the operator, and rarely consider individual preferences and needs. Agent-based simulations, however, permit incorporation of the latter, as well as capture the interactions between operators and customers. Several areas of future research are identified in order to unify future research efforts.

Suggested Citation

  • Nicole Ronald & Russell Thompson & Stephan Winter, 2015. "Simulating Demand-responsive Transportation: A Review of Agent-based Approaches," Transport Reviews, Taylor & Francis Journals, vol. 35(4), pages 404-421, July.
    • Handle: RePEc:taf:transr:v:35:y:2015:i:4:p:404-421
    DOI: 10.1080/01441647.2015.1017749
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    References listed on IDEAS

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    Cited by:

    1. Marcus Posada & Henrik Andersson & Carl H. Häll, 2017. "The integrated dial-a-ride problem with timetabled fixed route service," Public Transport, Springer, vol. 9(1), pages 217-241, July.
    2. Auke Hoekstra & Maarten Steinbuch & Geert Verbong, 2017. "Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation," Complexity, Hindawi, vol. 2017, pages 1-23, December.
    3. Ho, Sin C. & Szeto, W.Y. & Kuo, Yong-Hong & Leung, Janny M.Y. & Petering, Matthew & Tou, Terence W.H., 2018. "A survey of dial-a-ride problems: Literature review and recent developments," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 395-421.
    4. Yves Molenbruch & Kris Braekers & An Caris, 2017. "Typology and literature review for dial-a-ride problems," Annals of Operations Research, Springer, vol. 259(1), pages 295-325, December.
    5. Maggi,Elena & Vallino,Elena, 2017. "An Agent-Based Simulation of Urban Passenger Mobility and Related Policies. The Case Study of an Italian Small City," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201708, University of Turin.
    6. G. Dikas & I. Minis, 2018. "Scheduled Paratransit Transport Enhanced by Accessible Taxis," Transportation Science, INFORMS, vol. 52(5), pages 1122-1140, October.
    7. Vismara, Luca & Chew, Lock Yue & Saw, Vee-Liem, 2021. "Optimal assignment of buses to bus stops in a loop by reinforcement learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    8. Ballis, Haris & Dimitriou, Loukas, 2020. "Revealing personal activities schedules from synthesizing multi-period origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 224-258.

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