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A Middleware-Based Approach for Multi-Scale Mobility Simulation

Author

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  • Xavier Boulet

    (Institut de Recherche Technologique SystemX, 91120 Palaiseau, France
    COSYS-GRETTIA, Université Gustave Eiffel, IFSTTAR, F-77454 Marne-la-Vallée, France)

  • Mahdi Zargayouna

    (COSYS-GRETTIA, Université Gustave Eiffel, IFSTTAR, F-77454 Marne-la-Vallée, France)

  • Gérard Scemama

    (COSYS-GRETTIA, Université Gustave Eiffel, IFSTTAR, F-77454 Marne-la-Vallée, France)

  • Fabien Leurent

    (LVMT, Université Gustave Eiffel, IFSTTAR, Ecole des Ponts, F-77454 Marne-la-Vallée, France)

Abstract

Modeling and simulation play an important role in transportation networks analysis. In the literature, authors have proposed many traffic and mobility simulations, with different features and corresponding to different contexts and objectives. They notably consider different scales of simulations. The scales refer to the represented entities, as well as to the space and the time representation of the transportation environment. However, we often need to represent different scales in the same simulation, for instance to represent a neighborhood interacting with a wider region. In this paper, we advocate for the reuse of existing simulations to build a new multi-scale simulation. To do so, we propose a middleware model to couple independent mobility simulations, working at different scales. We consider all the necessary processing and workflow to allow for a coherent orchestration of these simulations. We also propose a prototype implementation of the middleware. The results show that such a middleware is capable of creating a new multi-scale mobility simulation from existing ones, while minimizing the incoherence between them. They also suggest that, to have a maximal benefit from the middleware, existing mobility simulation platforms should allow for an external control of the simulations, allowing for executing a time step several times if necessary.

Suggested Citation

  • Xavier Boulet & Mahdi Zargayouna & Gérard Scemama & Fabien Leurent, 2021. "A Middleware-Based Approach for Multi-Scale Mobility Simulation," Future Internet, MDPI, vol. 13(2), pages 1-21, January.
  • Handle: RePEc:gam:jftint:v:13:y:2021:i:2:p:22-:d:483409
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    References listed on IDEAS

    as
    1. Francisco A. Ortega & Miguel A. Pozo & Justo Puerto, 2018. "On-Line Timetable Rescheduling in a Transit Line," Transportation Science, INFORMS, vol. 52(5), pages 1106-1121, October.
    2. Richard Connors & David Watling, 2015. "Assessing the Demand Vulnerability of Equilibrium Traffic Networks via Network Aggregation," Networks and Spatial Economics, Springer, vol. 15(2), pages 367-395, June.
    3. Martin Fellendorf & Peter Vortisch, 2010. "Microscopic Traffic Flow Simulator VISSIM," International Series in Operations Research & Management Science, in: Jaume Barceló (ed.), Fundamentals of Traffic Simulation, chapter 0, pages 63-93, Springer.
    4. Nourinejad, Mehdi & Roorda, Matthew J., 2017. "Impact of hourly parking pricing on travel demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 98(C), pages 28-45.
    Full references (including those not matched with items on IDEAS)

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