Is this a case of a scientific paper that hits the right notes and gets a run in the mainstream?
Perhaps not – but what timing.
A joint Australian/US study published in the journal Nature Communications says the spread and dissipation of urban traffic congestion can be characterised using a model that predicts the spread of infectious disease.
The pattern of traffic jams on urban road networks, says the report, is usually viewed as a complex thing that can only be understood through intensive data modelling. But existing models carry huge computational burden, and modellers in developing countries are additionally hindered by a lack of road network data.
However, the research team notes that the development and deployment of mobile traffic sensors generates continuous, real-time spatial data, which can help modellers to estimate road traffic conditions in real time.
University of New South Wales engineer Meead Saberi and his colleagues have modelled traffic congestion in cities using an adapted version of the Susceptible-Infected-Recovered model, which is 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.
The study suggests that the model could be applied to develop optimal control strategies to minimise the total duration of congestion.
Sadly, the researchers can’t yet describe the situation where a traffic network recovers from a jam and becomes congested again at a later point. This is because of an assumption made in the epidemic model: that after infected individuals recover, they’re not infected again.
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