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

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

doi:10.1038/s41467-020-15353-2

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

Nat Commun. 2020 Apr 7;11(1):1616. doi: 10.1038/s41467-020-15353-2.

Authors

Affiliations

¹ Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering, University of New South Wales (UNSW), Sydney, NSW, 2032, Australia. meead.saberi@unsw.edu.au.
² Department of Civil Engineering, Institute of Transport Studies, Monash University, Melbourne, VIC, 3800, Australia.
³ Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering, University of New South Wales (UNSW), Sydney, NSW, 2032, Australia.
⁴ School of Electrical and Computer Engineering, K.N. Toosi University of Technology, Tehran, Iran.
⁵ Data61, CSIRO, Sydney, 2015, NSW, Australia.
⁶ Department of Civil Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
⁷ College of Environmental Design, University of California, Berkeley, CA, 94720, USA.

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.