A pilot in Pittsburgh uses smart technology to optimize traffic signals, reducing the amount of time a vehicle is idled and stopped, as well as overall travel times. It was designed 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 road networks.
Adaptive traffic signal control (ATSC) systems depend on sensors to monitor 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 in the pavement. They can also gather data from connected vehicles in C-V2X and technologytraffic.com/2020/05/01/modern-traffic-technologies-by-board-room/ DSRC formats. Data is processed on the edge device, or transmitted to a cloud storage location for analysis.
By capturing and processing real-time data regarding road conditions, accidents, congestion, and weather, smart traffic lights can automatically adjust idling time, RLR at busy intersections and recommended speed limits to ensure that vehicles can move around freely without slowing them down. They also can detect and alert drivers of safety issues such as violations of lane markings, or crossing lanes, helping to minimize injuries and accidents on city roads.
Smarter controls can also be used to address new challenges like the popularity of ebikes, Escooters, and other micromobility devices that have grown in popularity 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.