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Data and codes for: A link model approach to identify congestion hotspots

Citation

Bassolas, Aleix; Gomez, Sergio; Arenas, Alex (2022), Data and codes for: A link model approach to identify congestion hotspots, Dryad, Dataset, https://doi.org/10.5061/dryad.x3ffbg7nv

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.

Methods

Synthetic and real network data used in the paper together with the code to perform the simulations.

Usage Notes

Networks are provided in .csv format.

Funding

ICREA Academia

James S. McDonnell Foundation

Spanish Ministry of Universities

European Union–Next Generation EU Recovery, Transformation and Resilience Plan

Universitat de les Illes Balears