Looking at disease spread helps predict traffic congestion

Embargoed until: Publicly released:
Peer-reviewed: This work was reviewed and scrutinised by relevant independent experts.

Simulation/modelling: This type of study uses a computer simulation or mathematical model to predict an outcome. The original values put into the model may have come from real-world measurements (eg: past spread of a disease used to model its future spread).

Looking at the spread of diseases like COVID-19 could have other unexpected applications – such as predicting traffic congestion. Aussie and international researchers found that an adapted version of a mathematical model used to describe the spread of disease, called the Susceptible-Infected-Recovered model, could also be used to describe traffic congestion in cities. The authors tested their model using computer simulations of the Melbourne road network and traffic data from six cities (Melbourne, Sydney, London, Paris, Chicago and Montreal), and found that despite their different geographies, the cities seemed to have consistent patterns of congestion spread. However, researchers can’t yet predict traffic congestion in roads that clear and then become congested again, because the infection model used assumes that an infected individual who recovers does not become infected again.

Journal/conference: Nature Communications

Link to research (DOI): 10.1038/s41467-020-15353-2

Organisation/s: The University of New South Wales, Monash University, Data61, CSIRO

Funder: No funders listed.

Media release

From: Springer Nature

2.  Physics: Traffic jams mapped using contagion model

The propagation and dissipation of traffic jams in cities can be characterized using a model that predicts the spread of an infectious disease, reports a study in Nature Communications.

The spread of traffic jams in urban networks is often viewed as a complex phenomenon, which requires computationally intensive models for analysis. However, the development and deployment of mobile sensors offers the opportunity to generate continuous spatial data, which can enable the estimation of road traffic conditions in real time to be used by modellers.

Meead Saberi and colleagues demonstrate that traffic congestion in cities can be characterized using an adapted version of the Susceptible-Infected-Recovered model used to describe the spread of an infectious disease in a population. The authors validated their model using a computer simulation of the Melbourne road network and traffic data from six cities (Melbourne, Sydney, London, Paris, Chicago and Montreal). They found that despite their different geographies, the cities tended to have consistent patterns of congestion spread. They suggest that the model could be applied to develop optimal control strategies to minimize the total duration of congestion. However, they cannot yet describe the situation where a traffic network may recover and become congested again at a later point because of an assumption made in the epidemic model, that after an infected individual recovers they are not infected again.

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