A pilot in Pittsburgh uses smart technology to improve traffic signals, thereby reducing the amount of time spent on stopping and idling vehicles and overall travel times. Designed by a Carnegie Mellon professor of robotics the system blends signals from the past with sensors and artificial intelligence to improve routing in urban road networks.
Adaptive traffic signal control (ATSC) systems depend on sensors to observe real-time conditions at intersections and adjust the timing of signals and phasing. They can be based on different types of hardware, including radar, computer vision and inductive loops that are embedded technologytraffic.com into the pavement. They can also gather data from connected vehicles in C-V2X and DSRC formats. The data is processed at the edge device, or transferred to a cloud for analysis.
Smart traffic lights can adjust the idle time and RLR at busy intersections to allow vehicles to move without sloweding them down. They also can detect dangers like violations of lane markings or crossing lanes and alert drivers, helping to reduce accidents on city roads.
Smarter controls can also help to address new challenges like the growth of e-bikes, escooters, and other micromobility options that have become more popular since the pandemic. These systems are able to monitor vehicles’ movements and use AI to better manage their movements at intersections that are not appropriate for their small size.