A pilot in Pittsburgh is using smart technology to improve traffic signals, thereby reducing the amount of time a vehicle is idled and stopped, as well as overall travel time. The system was designed by a Carnegie Mellon professor in robotics and integrates existing signals with sensors and artificial intelligence to improve the routing of urban roads.
Adaptive traffic signal control (ATSC) systems depend on sensors to monitor the real-time conditions at intersections and adjust the timing of signals and their phasing. They can be based upon a variety hardware, including radar, computer vision and inductive loops embedded in the pavement. They also can capture vehicle data from connected vehicles in C-V2X and DSRC formats, with data pre-processed at the edge device or technologytraffic.com/2021/12/29/generated-post-3/ sent to a cloud server to be further analyzed.
Smart traffic lights can regulate the idling time and RLR at busy intersections to ensure that vehicles are moving without slowed down. They also can detect and notify drivers of dangers, such as violations of lane markings, or crossing lanes. This helps to prevent injuries and accidents on city roads.
Smarter controls are also a way to overcome new challenges, including the popularity of ebikes, scooters, and other micromobility solutions which have increased during the epidemic. These systems can monitor the movements of these vehicles and employ AI to better control their movements at intersections with traffic lights, which aren’t ideal to their small size or mobility.