How understanding disease spread can help traffic congestion
Could the need to track the coronavirus’ spread hold clues to non-health areas, such as traffic congestion?
PhD student, Mudabber Asfaq, is tackling this question in his Modelling traffic congestion as a contagion research project as part of the iMOVE PhD Program. It’s one of over 70 projects on a range of transport and mobility topics and issues being coordinated by iMOVE.
“When I tell people about my research to incorporate the disease model, they’re fascinated that this idea can be applied to transportation,” he says.
The University of NSW (UNSW) scholarship recipient is halfway through his research focusing on congestion in the metropolitan areas of Melbourne and, to a smaller degree, Sydney.
“The reason contagion models are so good is that they’re simple, easy to use and can give us the information for a large-scale network quite quickly. Other transportation models are complicated, require a lot of computational power and time or need many parameters to understand congestion behaviours,” says Mudabber.
He considered three models of disease, known before selecting the ‘SIR’ (the number of Susceptible, Infected and Recovered individuals) model for further investigation. This model fits the congestion issue best as its behaviour resembles the crucial morning traffic peak, he says. The model is explained more fully in this article by the Mathematical Association of America.
Mudabber says: “If we only consider the morning peak, we can see that traffic congestion increases, then afterwards, as traffic flow starts to recover, congestion eases. Using one peak closely resembles the SIR contagion model – it gets to the peak and starts recovering, but it does have waves.”
His model uses live data from Google Maps and traffic information to forecast congestion through the domains of space and time. He writes code in Matlab programming language mostly, but also in Python for certain functions and data analysis tools.
Mudabber’s work has already captured world attention, with articles about it in international press, including:
- A simple contagion process describes spreading of traffic jams in urban networks
- Traffic jams are contagious. Understanding how they spread can help make them less common
- Turns out, traffic spreads like the coronavirus
Ripple effect of the work
The work he and his colleagues are doing with the School of Civil and Environmental Engineering at UNSW will help urban and transport planners, particularly those working for government, work out the impact of new infrastructure developments on traffic flows.
“They can simulate the new development, run the model and near-instantly see the congestion pattern in the whole network city-wide as well as pace through different scenarios. They’ll be able to assess if the development will improve the congestion or not well before building work starts,” he says.
Mudabber considering making his programming open-source, so other research institutions, government departments and businesses could benefit.
And it has many potential applications. “This model isn’t limited to diseases. It could also show how a computer virus links with other computers and spreads or how information spreads via social media networks. They’re the same thing – a spreading phenomenon,” he says.
Mudabber is on track to complete his PhD by the end of 2022, while producing papers to share his findings on the way.
Could your organisation benefit from working with a PhD student?
If your organisation is interested in kick-starting your R&D on a particular transport or mobility-related topic, the iMOVE Industry PhD Program could be a good fit.
Each three-year PhD helps towards developing new technology, processes and thinking with a deep exploration into a defined area. iMOVE and the university can assist with refining the topics for organisations.
Research topics can cover a whole range of transport related topics and many delve into other computer science models, such as Mudabber’s.
For more information, visit the iMOVE Industry PhD Program page.