Conjunts de dades de producció científicaEnginyeria Informàtica i Matemàtiques

A link model approach to identify congestion hotspots

  • Datos identificativos

    Identificador:  PC:4027
    Handle: https://hdl.handle.net/20.500.11797/PC4027
    Autores:  Bassolas Esteban, Aleix
    Resumen:
    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.

  • Otros:

    Tipo de documento: info:eu-repo/semantics/other
    DOI: 10.5281/zenodo.6837557
    Publicaciones relacionadas: "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 "
    Grupo de investigación: Network and Data Science
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor: Bassolas Esteban, Aleix
    Fecha alta repositorio: 2022-07-15
    Año de publicación de la dataset: 2022
    Materia: Mobilitat
    Identificador del investigador: 0000-0001-5588-2117
    DOI de la publicación relacionada: 10.1098/rsos.220894
    Idioma: en
    Publicado por (editorial): Universitat Rovira i Virgili (URV)
    Derechos de acceso: info:eu-repo/semantics/openAccess
    Resumen: 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.