A mathematical model that could significantly reduce traffic congestion by combining data from existing infrastructure, remote sensors, mobile devices and their communication systems, has been developed by a research team at one of Australia’s leading universities.
The model has been developed by researchers at Swinburne University of Technology in Melbourne, Victoria, and it has potential industry impact as a state-of-the-art, integrated, efficient traffic network management system. As populations and economic activities increase, demand for greater mobility and rapid transportation has grown. Traffic management is problematic, however, resulting in frequent congestion on road networks, which brings economic, ecological and social challenges.
Swinburne’s ‘Congestion Breaker’ project uses intelligent transport systems (ITS) to combine information and data from a range of sources for effective traffic control. Led by Professor Hai L Vu, and developed in collaboration with VicRoads, the government body responsible for road management in the state of Victoria, through an Australian Research Council (ARC) Future Fellowships grant, the Congestion Breaker project has developed a mathematical approach that uses limited and incomplete data from existing operational traffic management systems to build a predictive control framework to minimize congestion.
The model optimizes the traffic flows over a finite period, taking 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 built-up and travel time on the road between intersections, and is capable of producing systems that would reduce congestion significantly.
An innovative distributed control mechanism created in this project is inspired by research developed for packet scheduling in wireless networks, which can handle a large network containing thousands of sensors and actuators in real time. The outcome is a comprehensive traffic management framework with computational flexibility accurate enough to reflect real urban traffic networks. It produces a scalable algorithm that can be integrated with current operating traffic management systems to reduce congestion and make better use of the existing road network infrastructure.
The model has potential industry impact as a state-of-the-art, integrated, efficient traffic network management system, as it acts as a smart, scalable and easily integrated solution to many current problems. Such work is already making a significant difference in the Netherlands, where so-called Integrated Network Management (INM) using the model predictive control principle was applied on a comprehensive scale to tackle the traffic congestion of Amsterdam.
“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.”
