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A link model approach to identify congestion hotspots

  • Identification data

    Identifier: PC:4027
    Handle: https://hdl.handle.net/20.500.11797/PC4027
    Authors:
    Bassolas Esteban, Aleix
    Abstract:
    Congestion emerges when high demand peaks put transportation systems under stress. Understanding the interplay between the spatial organization of demand, the route choices of citizens, and the underlying infrastructures is thus crucial to locate congestion hotspots and mitigate the delay. Here we develop a model where links are responsible for the processing of vehicles, that can be solved analytically before and after the onset of congestion, and providing insights into the global and local congestion. We apply our method to synthetic and real transportation networks, observing a strong agreement between the analytical solutions and the Monte Carlo simulations, and a reasonable agreement with the travel times observed in 12~cities under congested phase. Our framework can incorporate any type of routing extracted from real trajectory data to provide a more detailed description of congestion phenomena, and could be used to dynamically adapt the capacity of road segments according to the flow of vehicles, or reduce congestion through hotspot pricing.

  • Others:

    Subject matter: Mobilitat
    Access rights: info:eu-repo/semantics/openAccess
    Researcher identifier: 0000-0001-5588-2117
    Published by (editorial): Universitat Rovira i Virgili (URV)
    Related publications: "Bassolas, A., Gómez, S., & Arenas, A. (2022). A link model approach to identify congestion hotspots. Royal Society Open Science, 9(10), 220894. https://doi.org/10.1098/rsos.220894 "
    Abstract: Congestion emerges when high demand peaks put transportation systems under stress. Understanding the interplay between the spatial organization of demand, the route choices of citizens, and the underlying infrastructures is thus crucial to locate congestion hotspots and mitigate the delay. Here we develop a model where links are responsible for the processing of vehicles, that can be solved analytically before and after the onset of congestion, and providing insights into the global and local congestion. We apply our method to synthetic and real transportation networks, observing a strong agreement between the analytical solutions and the Monte Carlo simulations, and a reasonable agreement with the travel times observed in 12~cities under congested phase. Our framework can incorporate any type of routing extracted from real trajectory data to provide a more detailed description of congestion phenomena, and could be used to dynamically adapt the capacity of road segments according to the flow of vehicles, or reduce congestion through hotspot pricing.
    Departament: Enginyeria Informàtica i Matemàtiques
    DOI: 10.5281/zenodo.6837557
    Document type: info:eu-repo/semantics/other
    Related publication's DOI: 10.1098/rsos.220894
    Repository ingest date: 2022-07-15
    Author: Bassolas Esteban, Aleix
    Keywords: Urban congestion, mobility
    Research group: Network and Data Science
    Dataset publication year: 2022
    Dataset title: A link model approach to identify congestion hotspots