Repositori Institucional de la Universitat Rovira i Virgili
oai:urv.cat:PC:4027
Otros (other). 202

A LINK MODEL APPROACH TO IDENTIFY CONGESTION HOTSPOTS

Repositori Institucional de la Universitat Rovira i Virgili
  • Bassolas Esteban, Aleix
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.




Repositori Institucional de la Universitat Rovira i Virgili
oai:urv.cat:imarina:9285733
. 2022

A LINK MODEL APPROACH TO IDENTIFY CONGESTION HOTSPOTS

Repositori Institucional de la Universitat Rovira i Virgili
  • Bassolas, Aleix; Gomez, Sergio; Arenas, Alex
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.




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