In Pittsburgh, a pilot program is using intelligent technology to optimize timings of traffic signals. This reduces vehicle stop-and idle time and travel times. The system was developed by an Carnegie Mellon professor of robotics the system integrates existing signal systems with sensors and artificial intelligence to improve the routing within urban road networks.
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 hardware such as radar, computer vision or inductive loops embedded in the pavement. They can also record vehicle data from connected cars in C-V2X or DSRC formats with data processed on the edge device, or dispatched to a cloud location for further analysis.
By collecting and processing real-time information about road conditions such as accidents, congestion, and weather conditions, smart traffic lights can automatically adjust 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 concerns, such as traffic violations, lane markings, or crossing lanes. They can also help to reduce accidents and injuries on city roads.
Smarter controls are also able to address new challenges such as the rise of technologytraffic.com/2021/07/08/generated-post-2/ e-bikes, escooters, and other micromobility options that have become more popular since the pandemic. These systems are able to monitor the movements of these vehicles and apply AI to improve their movements at traffic light intersections which aren’t well-suited due to their small size and maneuverability.
