Traffic Congestion Index – Using Machine Learning to alleviate traffic congestion. [http://wordpress-ms.its.deakin.edu.au/dstil/wp-content/uploads/sites/69/2018/06/Website-logos_gcs_logo_small.png]

Traffic
Congestion Modelling

Using Machine Learning to alleviate traffic congestion.

Traffic congestion costs Australian business $3.5 billion annually

No one enjoys being stuck in traffic. Australia’s population is growing, and as the population grows and expands outwards from the city centre, more strain will be placed on infrastructure such as roads and public transport. For millions of Australians, driving to and from work each day is time spent being wasted stuck in traffic. Traffic congestion costs businesses billions of dollars annually and has negative impact on morale and the environment. The solution is not to build more roads, but instead understand the impact of roadworks, accidents and the flow of traffic.



Innovation Partners

“Being able to analyse and understand the ebb and flow of traffic within the network permits us to understand the impact of road upgrade projects.”

Professor Rajesh Vasa - DSTIL

Our Solution

The Traffic Congestion Index (TCI) measures the level of congestion in a geographical area (e.g. a suburb). The index is derived using a higher-order statistical model. The insights TCI provides can help decision makers assess which areas of infrastructure require attention and at what times congestion is at its peak. The TCI can potentially also be used to assess the long term impact of roadworks; such as the impact of the removal of railway crossings on congestion.

Are you interested in harnessing this technology in your city?