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A link model approach to identify congestion hotspots
Aleix Bassolas1,2, Sergio Gómez1, Alex Arenas1
1Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona 43007, Spain.
Royal Society Open Science
|October 28, 2022
Summary
This study presents a new analytical model to understand traffic congestion by analyzing vehicle flow, infrastructure, and routing. The model accurately predicts congestion hotspots and travel times in real-world transportation networks.
Area of Science:
- Transportation Science
- Network Analysis
- Urban Planning
Background:
- Traffic congestion arises from high demand overwhelming transportation systems.
- Understanding spatial demand, route choices, and infrastructure is key to managing traffic delays.
- Identifying congestion hotspots requires sophisticated modeling approaches.
Purpose of the Study:
- To develop an analytical model for understanding traffic congestion dynamics.
- To provide insights into global and local congestion patterns.
- To offer a framework for dynamic traffic management and mitigation strategies.
Main Methods:
- Developed an analytical model where transportation network links process vehicles.
- The model is solvable analytically both before and after congestion onset.
- Applied the model to synthetic and real-world transportation networks.
Main Results:
- Achieved strong agreement between analytical solutions and Monte Carlo simulations.
- Demonstrated reasonable agreement with observed travel times in 12 cities during congestion.
- Validated the model's effectiveness on diverse transportation networks.
Conclusions:
- The analytical framework offers a robust method for analyzing traffic congestion.
- The model can integrate real trajectory data for enhanced congestion description.
- Findings support dynamic road capacity adaptation and congestion pricing strategies.
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