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

Meead Saberi,Vinayak Dixit,Ziyuan Gu,Marta C González,Divya J Nair,Homayoun Hamedmoghadam,Lauren Gardner,S Travis Waller,Seyed Amir Hosseini,Sajjad Shafiei,Mudabber Ashfaq

doi:10.1038/s41467-020-15353-2

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 macroscopic characteristics for network traffic dynamics, namely congestion propagation rate β and congestion dissipation rate μ. We describe the dynamics of congestion spread using these new parameters embedded within a system of ordinary differential equations, similar 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.

Highlights

The spread of traffic jams in urban networks has long been viewed as a complex spatiotemporal phenomenon that often requires computationally intensive microscopic models for analysis purposes
Despite the complex human behavior-driven nature of traffic, we demonstrate that urban network traffic congestion follows a surprisingly similar spreading pattern as in other systems, including the spread of infectious disease in a population or diffusion of ideas in a social network, and can be described using a similar parsimonious theoretical network framework
We use simulated data from a calibrated and validated mesoscopic dynamic traffic assignment model of Melbourne[1]. Using both empirical and simulation data, we demonstrate that the proposed modeling framework can successfully describe the dynamics of congestion propagation and dissipation in urban networks

Summary

Introduction

The spread of traffic jams in urban networks has long been viewed as a complex spatiotemporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. 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 propose and empirically demonstrate that traffic congestion in urban networks can be characterized using a simple contagion process, similar to the well-known susceptible-infected-recovered (SIR) model used to describe spread of infectious diseases in a population, wherein traffic spreads and recovers throughout the network over time. We propose that a network’s propagation and dissipation can be characterized by two average rates, namely, the congestion propagation rate β and the congestion recovery rate μ, which together can predict the number of congested links in the network over time These two macroscopic characteristics are critical in modeling congestion propagation and dissipation as a simple contagion process[23]. Similar to other macroscopic model-based control applications[24,25,26], we can employ the proposed SIR-based model to improve the traffic network performance by controlling and optimizing the network input through optimal metering of traffic flow or by increasing the network recovery rate through improved signal timing, bottleneck removal, and capacity expansion

Methods

Results

Conclusion