A pilot in Pittsburgh is utilizing smart technology to optimize traffic signals, thus reducing the time it takes for vehicles to stop and idle as well as overall travel times. The system was developed by a Carnegie Mellon professor of robotics the system blends signals from the past with sensors and artificial intelligence to improve the routing in urban roads.
Sensors are used by adaptive traffic signal control systems (ATSC) to monitor and adjust the timing and the phasing of signals in intersections. They can be based upon various types of hardware, such as radar, computer vision, and inductive loops embedded in the pavement. They can also collect vehicle data from connected vehicles in C-V2X or DSRC formats and have the data processed on the edge device, or sent to a cloud storage location for further analysis.
By capturing and processing real-time data about road conditions, accidents, congestion, and weather, smart traffic lights will automatically adjust the idling time, RLR at busy intersections, and recommended speed limits to allow vehicles to move freely without slowing them down. They also can detect and notify drivers of safety issues, like the violation of lane markings or crossing lanes. They can also help to prevent injuries and accidents on city roads.
Smarter controls are also able to address new challenges such as the rise of e-bikes, escooters, and other micromobility options that have become more popular since the pandemic. These systems can track the movements of these vehicles and employ AI to control their movements at traffic light intersections, which aren’t suited due to their small size and mobility.