# A simple contagion process describes spreading of traffic jams in urban networks

arXiv: https://arxiv.org/abs/1906.00585 · HTML: https://mlsanity.com/paper/1906.00585/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2019.

## Built on by (most cited first, 9 of 9)

- [On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical Solver](https://mlsanity.com/paper/2208.03680/) (2022)
- [Quantifying Spatial Homogeneity of Urban Road Networks via Graph Neural Networks](https://mlsanity.com/paper/2101.00307/) (2021)
- [Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner](https://mlsanity.com/paper/2405.03185/) (2024)
- [Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles](https://mlsanity.com/paper/2407.08047/) (2024)
- [A Data-driven Resilience Framework of Directionality Configuration based on Topological Credentials in Road Networks](https://mlsanity.com/paper/2401.07371/) (2024)
- [Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network](https://mlsanity.com/paper/2405.14079/) (2024)
- [Enhancing Sustainable Urban Mobility Prediction with Telecom Data: A Spatio-Temporal Framework Approach](https://mlsanity.com/paper/2405.17507/) (2024)
- [Spatial-Temporal Generative AI for Traffic Flow Estimation with Sparse Data of Connected Vehicles](https://mlsanity.com/paper/2407.08034/) (2024)
- [Autonomous discovery of traffic laws with AI traffic scientists](https://mlsanity.com/paper/2607.01639/) (2026)

## 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.