A pilot in Pittsburgh is utilizing smart technology to improve traffic signals, thereby reducing vehicle stop-and-idling time and overall travel times. It was designed by a Carnegie Mellon professor of robotics the system blends existing signal systems with sensors and artificial intelligence to improve the routing in urban roads.
Adaptive traffic signal control (ATSC) systems rely on sensors to track the real-time conditions at intersections and adjust signal timing and phasing. They may be based on different types of hardware, such as radar, computer vision and inductive loops embedded within 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.
Smart traffic lights are able to adjust the idling time and RLR at busy intersections to keep vehicles moving without sloweding them down. They also can detect safety issues such as lane marking violations and crossing lanes and alert drivers, which can help reduce accidents on city roads.
Smarter controls can also help to tackle new challenges like the growth of e-bikes and e-scooters and other micromobility options that have become increasingly popular since the pandemic. Such systems can monitor the movements of these vehicles and employ AI to improve their movements at intersections for traffic lights, which are not well suited to their small size or maneuverability.
technologytraffic.com/2022/04/28/turning-to-data-room-to-gain-a-competitive-advantage-in-ma
