In Pittsburgh the pilot program has been developed that uses smart technology to optimize timings of traffic signals. This helps reduce vehicle stop-and idle times and travel times. The system was traffic demand matrix designed by a Carnegie Mellon professor in robotics and combines existing signals with sensors and artificial intelligence to improve the routing of urban road networks.

Adaptive traffic signal control (ATSC) systems rely on sensors to track real-time conditions at intersections and adjust the timing of signals and phasing. They can be built on different types of hardware including radar computer vision, radar, as well as inductive loops that are embedded in the pavement. They also can collect data from connected vehicles in C-V2X and DSRC formats. Data is processed on the edge device, or transmitted to a cloud server for analysis.

By taking and processing real-time data about road conditions traffic, accidents, congestion and weather conditions, smart traffic lights can automatically adjust idle time, RLR at busy intersections, and recommended speed limits to allow vehicles to move freely without slowed down. They can also alert drivers to safety issues, like the violation of lane markings or crossing lanes, assisting to prevent injuries and accidents on city roads.

Smarter controls can also assist to overcome new challenges such as the rise of e-bikes and e-scooters and other micromobility options that have become more popular during the pandemic. These systems are able to monitor vehicles’ movements and employ AI to improve their movements at intersections that are not suitable for their size.