In Pittsburgh the pilot program utilizes intelligent technology to optimize timings for traffic signals. This decreases the stop-and-go idle times and travel times. It was designed by a Carnegie Mellon professor of robotics the system integrates signals from the past with sensors and artificial intelligence to improve routing in urban road networks.
Adaptive traffic signal control (ATSC) systems rely on sensors to track the real-time conditions at intersections and adjust the timing and phasing of signals. They can be based on various hardware options, including radar, computer vision, and 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 sent to a cloud server for analysis.
By capturing and processing real-time data about road conditions and traffic congestion, accidents, and weather, smart traffic lights can automatically adjust idle time, RLR at busy intersections and speed limits that are recommended to allow vehicles to move freely without slowing them down. They can also detect safety issues like the violation of lane markings and crossing lanes, and alert drivers, helping to prevent accidents on city roads.
Smarter controls are also a way technologytraffic.com/2021/12/29/generated-post-4/ to overcome new challenges, including the growing popularity of ebikes escooters and other micromobility solutions that have grown in popularity during the pandemic. These systems can track the movement of these vehicles and use AI to better control their movements at traffic light intersections, which are not well suited for their small size or mobility.