A link model approach to identify congestion hotspots

TL;DR

This paper introduces an analytical link model to identify and analyze congestion hotspots in transportation networks, providing insights into congestion dynamics and potential mitigation strategies.

Contribution

The authors develop a novel analytical framework that models link-level congestion, applicable to real and synthetic networks, enhancing understanding of traffic flow and hotspot identification.

Findings

Strong agreement between analytical solutions and Monte Carlo simulations.

Reasonable correlation with observed travel times in 12 cities.

Framework can incorporate real trajectory data for detailed congestion analysis.

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 that can be solved analytically before and after the onset of congestion 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.