Building a Smarter Intersection for Improved Road Safety and Congestion Busting

Like many other countries around the world, Australia is seeing significant population growth, particularly in our larger cities and built up urban environments. At the same time, there is a digital transformation underway in transportation, with Mobility-as-a-Service, smart phone ride-hailing and ride-sharing services, micro-mobility and other new forms of personal transport becoming increasingly available and adopted, altering the use of our core transport corridors and roads.

These two globally applicable mega trends are putting the existing transportation networks under increasing pressure, with the resultant traffic congestion outpacing the ability to invest in new infrastructure. At the same time, there is a worrying trend whereby vulnerable road users are increasingly at risk with a disproportionate increase in frequency of incidents for these road users.

As road trauma rates increase amongst all road users including the likes of pedestrians and cyclists and congestion impacts our daily commute – new technologies are emerging to create an accurate picture of the road environment in real-time. Thus, enabling intersection and corridor risk profiles to be created which can be used to intervene and mitigate future incidents from occurring or understanding traffic flow across multiple modes.

In this trial, the Internet of Things (IoT), Video Analytics, Deep Learning (DL) and Artificial Intelligence (AI), for the purpose of traffic flow assessment and insights into road user behaviour, were evaluated at an intersection at the AIMES testbed in Melbourne, Australia in partnership with: University of Melbourne, Department of Transport (DOT), IAG and Cisco.

Our major findings from this trial found that the technology provided: 90-95% accuracy in road user count in both day light and low light conditions 95% accuracy in classifying cars, trucks, buses and VRUs (pedestrians and cyclists combined).

Insights include detailed real-time directional traffic information that otherwise doesn’t exist today.

For example, it was observed that a typical traffic mix of the intersection includes high volume of VRUs. Observation showed on average a ratio of 78% cars, 12% VRUs, 8% trucks and 2% buses.

Learn more in the report: Smart Intersections – IoT Insights using Video Analytics and Artificial Intelligence