Agent-based simulation of shared bikes and e-scooters: the case of Lyon

Azise Oumar Diallo (), Thibault Gloriot and Ouassim Manout ()
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Azise Oumar Diallo: LAET - Laboratoire Aménagement Économie Transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique
Thibault Gloriot: LAET - Laboratoire Aménagement Économie Transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique
Ouassim Manout: LAET - Laboratoire Aménagement Économie Transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique

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Abstract: Shared micro-mobility services are increasingly becoming popular in many cities. These services can help shape sustainable cities and reduce car dependency. To unlock their full potential, a deep understanding of these services' operation, adoption, and integration within existing mobility systems is required. This paper uses a newly introduced agent-based simulation framework to study the implications of shared bikes and e-scooters in Lyon, France. The paper describes data collection, processing, and input data generation for the simulation of shared bikes and e-scooters. Simulation outcomes evaluate the impact of introducing these services on existing travel modes. The introduction of shared bikes and e-scooters can compete against public transit and walking. The simulation framework can be used to assess various aspects of shared bikes and e-scooters and help policy-makers take advantage of their full potential to improve urban mobility.

Keywords: Agent-Based Model (ABM); Shared micro-mobility; Shared bike; Shared e-scooter; MATSim (search for similar items in EconPapers)
Date: 2023
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Published in Procedia Computer Science, 2023, Part of special issue: The 14th International Conference on Ambient Systems, Networks and Technologies Networks (ANT 2022) and The 6th International Conference on Emerging Data and Industry 4.0 (EDI40), 220, pp.364-371. ⟨10.1016/j.procs.2023.03.047⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04074824

DOI: 10.1016/j.procs.2023.03.047

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