Articles producció científica> Enginyeria Informàtica i Matemàtiques

A link model approach to identify congestion hotspots

  • Dades identificatives

    Identificador: imarina:9285733
    Autors:
    Bassolas, AleixGomez, SergioArenas, Alex
    Resum:
    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, which can be solved analytically before and after the onset of congestion, and provide 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.© 2022 The Authors.
  • Altres:

    Autor segons l'article: Bassolas, Aleix; Gomez, Sergio; Arenas, Alex
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Arenas Moreno, Alejandro / Bassolas Esteban, Aleix / Gómez Jiménez, Sergio
    Paraules clau: Road networks Network centrality Mobile phone Human mobility Congestion Complex networks road networks network centrality human mobility complex networks
    Resum: 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, which can be solved analytically before and after the onset of congestion, and provide 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.© 2022 The Authors.
    Àrees temàtiques: Multidisciplinary sciences Multidisciplinary Medicina veterinaria Matemática / probabilidade e estatística Interdisciplinar Geociências Ciências biológicas i Ciências agrárias i Ciência da computação Biodiversidade Astronomia / física Antropologia / arqueologia
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: sergio.gomez@urv.cat alexandre.arenas@urv.cat
    Identificador de l'autor: 0000-0003-1820-0062 0000-0003-0937-0334
    Data d'alta del registre: 2024-09-28
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://royalsocietypublishing.org/doi/full/10.1098/rsos.220894
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Royal Society Open Science. 9 (10): 220894-220894
    Referència de l'ítem segons les normes APA: Bassolas, Aleix; Gomez, Sergio; Arenas, Alex (2022). A link model approach to identify congestion hotspots. Royal Society Open Science, 9(10), 220894-220894. DOI: 10.1098/rsos.220894
    DOI de l'article: 10.1098/rsos.220894
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2022
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Multidisciplinary,Multidisciplinary Sciences
    Road networks
    Network centrality
    Mobile phone
    Human mobility
    Congestion
    Complex networks
    road networks
    network centrality
    human mobility
    complex networks
    Multidisciplinary sciences
    Multidisciplinary
    Medicina veterinaria
    Matemática / probabilidade e estatística
    Interdisciplinar
    Geociências
    Ciências biológicas i
    Ciências agrárias i
    Ciência da computação
    Biodiversidade
    Astronomia / física
    Antropologia / arqueologia
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