Articles producció científica> Enginyeria Informàtica i Matemàtiques

A link model approach to identify congestion hotspots

  • Identification data

    Identifier: imarina:9285733
    Authors:
    Bassolas, AleixGomez, SergioArenas, Alex
    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, 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.
  • Others:

    Author, as appears in the article.: Bassolas, Aleix; Gomez, Sergio; Arenas, Alex
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Arenas Moreno, Alejandro / Bassolas Esteban, Aleix / Gómez Jiménez, Sergio
    Keywords: Road networks Network centrality Mobile phone Human mobility Congestion Complex networks road networks network centrality human mobility complex networks
    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, 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.
    Thematic Areas: 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
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: sergio.gomez@urv.cat alexandre.arenas@urv.cat
    Author identifier: 0000-0003-1820-0062 0000-0003-0937-0334
    Record's date: 2024-09-28
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://royalsocietypublishing.org/doi/full/10.1098/rsos.220894
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Royal Society Open Science. 9 (10): 220894-220894
    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
    Article's DOI: 10.1098/rsos.220894
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Journal Publications
  • Keywords:

    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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