A research team at Swinburne University of Technology in Melbourne, Victoria, has developed a mathematical model that could help ease traffic congestion.

The new model combines data from remote sensors, mobile devices, existing infrastructure and other communication devices.

This model has been developed through the Congestion Breaker project utilising intelligent transport systems (ITS) that combine information and data from a range of sources.

The research team, led by Professor Hai L Vu, has developed the model through a collaboration with VicRoads, a government body responsible for road management in the state of Victoria.

"Our novelty is in developing an integrated traffic control scheme that combines linear model predictive control, with route guidance to manage urban traffic flows, and making it scalable for large networks."

The project has secured support from Australian Research Council’s (ARC) Future Fellowships grant.

This mathematic model makes use of limited data from current operational traffic management systems in order to develop a predictive control framework to cut down congestion, reported Traffictechnologytoday.com.

Congestion Breaker optimises the traffic flow for a certain time period and takes into account the short-term demand and traffic dynamic within links of the network.

The resulting algorithm explicitly considers any spillback due to a queue build-up and travel time on the road between intersections, and is capable of producing systems that would reduce congestion significantly.

"Our novelty is in developing an integrated traffic control scheme that combines linear model predictive control, with route guidance to manage urban traffic flows, and making it scalable for large networks." explained Vu.

"Similar pilot projects can be developed for many other cities around the world.

"And there are many possibilities for commercial applications in Australia and overseas in terms of smart mobility, sustainable cities for growing populations, and its concentration in big cities."