A simple contagion process describes spreading of traffic jams in urban networks
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Abstract (arXiv)
The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. In this study, we present a framework to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. We introduce two novel macroscopic characteristics of network traffic, namely congestion propagation rate beta and congestion dissipation rate μ. We describe the dynamics of congestion propagation and dissipation using these new parameters, beta, and μ, embedded within a system of ordinary differential equations, analogous to the well-known Susceptible-Infected-Recovered (SIR) model. The proposed contagion-based dynamics are verified through an empirical multi-city analysis, and can be used to monitor, predict and control the fraction of congested links in the network over time.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 6; 2025: 0; 2026: 1.
9 papers.
- 2022On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical Solvercited by 9
- 2021Quantifying Spatial Homogeneity of Urban Road Networks via Graph Neural Networkscited by 8
- 2024Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learnercited by 7
- 2024Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehiclescited by 1
- 2024A Data-driven Resilience Framework of Directionality Configuration based on Topological Credentials in Road Networkscited by 0
- 2024Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Networkcited by 0
- 2024Enhancing Sustainable Urban Mobility Prediction with Telecom Data: A Spatio-Temporal Framework Approachcited by 0
- 2024Spatial-Temporal Generative AI for Traffic Flow Estimation with Sparse Data of Connected Vehiclescited by 0
- 2026Autonomous discovery of traffic laws with AI traffic scientistscited by 0
- 2026Autonomous discovery of traffic laws with AI traffic scientists2 Jul 2026
- 2024Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles10 Jul 2024
- 2024Spatial-Temporal Generative AI for Traffic Flow Estimation with Sparse Data of Connected Vehicles10 Jul 2024
- 2024Enhancing Sustainable Urban Mobility Prediction with Telecom Data: A Spatio-Temporal Framework Approach26 May 2024
- 2024Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network23 May 2024
- 2024Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner6 May 2024
- 2024A Data-driven Resilience Framework of Directionality Configuration based on Topological Credentials in Road Networks14 Jan 2024
- 2022On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical Solver7 Aug 2022
- 2021Quantifying Spatial Homogeneity of Urban Road Networks via Graph Neural Networks1 Jan 2021