A pilot in Pittsburgh is using smart technology to optimize traffic signals, reducing the time it takes for vehicles to stop and idle as well as overall travel times. The system was developed by an Carnegie Mellon professor of robotics The system combines signals from the past 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 the timing of signals and phasing. They can be based on a variety hardware, including radar, computer vision and inductive loops embedded in the pavement. They can also capture vehicle data from connected vehicles in C-V2X and DSRC formats and then process the data on the edge device or sent to a cloud storage location to be further analyzed.

Smart traffic lights are able to adjust the idling time and RLR at busy intersections to allow vehicles to move without slowed down. They can also detect safety issues such as crossing lanes, and alert drivers, helping to prevent accidents on city roads.

Smarter controls can also be technologytraffic.com/2021/12/29/generated-post-4/ used to address new challenges, including the popularity of ebikes, escooters and other micromobility solutions which have increased during the epidemic. Such systems can monitor the movement of these vehicles and apply AI to control their movements at traffic light intersections, which aren’t well-suited due to their small size and maneuverability.

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