We sat down with Chew Men Leong, President of Urban Solutions at ST Engineering to dive more into the topic. He shares these valuable insights with us in an intimate chat.
A: Today’s systems on the roads are rule-based. If there’s an accident, the camera picks it up and the operator will generate an advisory to road and expressway users. Current traffic junctions today also mostly use time-based controls. As these junctions are unable to learn and respond to traffic situations in a proactive manner, it can result in challenges when it comes to optimising traffic flow.
A: Traffic management is all about optimising the available route network we have, managing that capacity, and encouraging people to take the most efficient routes. When we deploy smart digital junctions and push our AI and data to the nearest edge cloud with the support of SPTel, performance at that junction will improve day by day with machine learning. This will result in traffic light timings that are optimised based on real-time patterns.
A: Certainly. A simple example would be reducing start and stop for vehicles. We can analyse their speed, then inform the driver or bus system to maintain a certain speed so that by the time the bus reaches the traffic light it will be green. Another example would be using sensors to detect roadworks or an accident so that vehicles will be informed in advance to stay clear of the lane. This will help to improve efficiency and result in lesser carbon emissions for these vehicles.