Materia: Mobilitat
Derechos de acceso: info:eu-repo/semantics/openAccess
Identificador del investigador: 0000-0001-5588-2117
Publicado por (editorial): Universitat Rovira i Virgili (URV)
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 "
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.
Departamento: Enginyeria Informàtica i Matemàtiques
DOI: 10.5281/zenodo.6837557
Tipo de documento: info:eu-repo/semantics/other
DOI de la publicación relacionada: 10.1098/rsos.220894
Fecha alta repositorio: 2022-07-15
Autor: Bassolas Esteban, Aleix
Palabras clave: Urban congestion, mobility
Grupo de investigación: Network and Data Science
Año de publicación de la dataset: 2022
Título del conjunto de datos: A link model approach to identify congestion hotspots
Repositori URV