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

TL;DR

This paper models the spread of traffic jams in urban networks using a simple contagion process inspired by epidemiology, introducing new parameters to describe congestion dynamics and validating the approach with empirical data.

Contribution

It presents a novel contagion-based framework for traffic congestion dynamics, incorporating new macroscopic parameters and validated through multi-city empirical analysis.

Findings

The contagion model accurately describes congestion spread and dissipation.

New parameters ta and mma effectively capture congestion dynamics.

Model can be used for monitoring, predicting, and controlling traffic jams.

Abstract

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 \b{eta} and congestion dissipation rate {\mu}. We describe the dynamics of congestion propagation and dissipation using these new parameters, \b{eta}, and {\mu}, 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.