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Article Dans Une Revue Transportation Research Procedia Année : 2022

An agent-based model for modal shift in public transport

Résumé

Modal shift in public transport as a consequence of a disruption on a line has in some cases unforeseen consequences such as an increase in congestion in the rest of the network. How information is provided to users and their behavior plays a central role in such configurations. We introduce here a simple and stylised agent-based model aimed at understanding the impact of behavioural parameters on modal shift. The model is applied on a case study based on a stated preference survey for a segment of Paris suburban train network. We systematically explore the parameter space and show non-trivial patterns of congestion for some values of discrete choice parameters linked to perceived wait time and congestion. We also apply a genetic optimisation algorithm to the model to search for optimal compromises between congestion in different modes.
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Dates et versions

hal-03764833 , version 1 (30-08-2022)

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Citer

Thibaut Barbet, Amine Nacer-Weill, Changtao Yang, Juste Raimbault. An agent-based model for modal shift in public transport. Transportation Research Procedia, 2022, 62, pp.711-718. ⟨10.1016/j.trpro.2022.02.088⟩. ⟨hal-03764833⟩
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