Globally, more than 1.25 million people die in road traffic crashes every year and a further 50 million people are injured or disabled.¹ Meanwhile, congestion costs Australians $16.5 billion in 2015, according to the Bureau of Infrastructure, Transport and Regional Economics. The price of congestion is expected to double between $27.7 and $37.3 billion by 2030, without major policy changes.²

For the past few decades there has a been a steady reliance on infrastructure improvements, safety campaigns, speed limit regulations, more stringent law enforcement to improve safety of our roads. Today, advances in technology such as IoT, Fog/Edge Computing, wireless sensors, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications provide new powerful tools to tackle road safety, efficiency and sustainability.

Intersections represent a unique asset within a road transportation network and provide an opportunity for localised insights gathering through IoT technology (sensors and compute co-located inside road side infrastructure). For the first time we are able to piece together an accurate real-time picture of all road users, whether its vehicles, buses, cyclists, electric bike users or pedestrians at a lane level. As intersections become more intelligent and self-reliant, each tells its own unique story about performance, road user behaviour and risks without the need for any specialist equipment or applications in vehicles or other road users.

In a trial recently conducted with partners—University of Melbourne, Department of Transport (DOT), Cohda Wireless, IAG and Cisco—an Edge-hosted Wi-Fi solution was evaluated for the purpose of extracting insights into road user behaviour and performance at the intersection within the AIMES testbed in Melbourne, Australia.

Four use cases were conducted, focusing on a number of road usage patterns to evaluate the capability and accuracy of the technology solution:

  • Use Case 1:  Lane Level Traffic Flow (Vehicle Passing Rate): Start-lane / end lane flow at intersections for individual vehicles
  • Use Case 2:  Lane Level Traffic Flow (Speed Histogram): Detect average vehicle speed per lane at Short (5 min); Medium (15 min) and Long (Hour) duration.
  • Use Case 3:  Lane Level Traffic Flow (Stalled Detection Queue Length): Detect and report on queue length per lane
  • Use Case 4:  Pedestrian Traffic flow and Queue length

Learn more in the report, Smart Intersections – IoT insights using Wi-Fi.


1 Source: https://www.who.int/en/news-room/fact-sheets/detail/road-traffic-injuries
2 Bureau of Infrastructure, Transport and Regional Economics, Information Sheet 74: Traffic and congestion cost trends for Australian capital cities (2015).