In Pittsburgh, a pilot program utilizes intelligent technology to optimize traffic signal timings. This helps reduce vehicle stop-and idle time and travel times. Created by an Carnegie Mellon professor of robotics The system combines signals from the past with sensors and artificial intelligence to improve routing in urban roads.
Sensors are utilized by adaptive traffic signal control systems (ATSC) to monitor and adjust the timing and the phasing of signals at intersections. They can be built on various types of hardware, such as radar, computer vision, and inductive loops embedded in the pavement. They can also gather data from connected vehicles in C-V2X and DSRC formats. Data is pre-processed at the edge device, or sent to a cloud server to be analyzed.
By recording and processing real-time data regarding road conditions, accidents, congestion, and weather conditions, smart traffic signals can automatically adjust idling times, RLR at busy intersections, and recommended speed limits to keep vehicles moving freely without causing a slowdown. They also can detect safety issues such as the violation of lane markings and crossing lanes and alert drivers, helping to prevent accidents on city roads.
Smarter controls can also assist in tackling new challenges, such as the rise of e-bikes and e-scooters and other micromobility options that have become more popular since the pandemic. Such systems can monitor the movements of these vehicles, and utilize AI to control their movements at traffic light intersections, which are not well suited to their small size or mobility.