A pilot in Pittsburgh is using smart technology to optimize traffic signals, reducing the amount of time a vehicle is idled and stopped, as well as overall technologytraffic.com/2021/12/29/generated-post-3 travel time. The system was designed by an Carnegie Mellon professor in robotics and integrates existing signals with sensors and artificial intelligence to improve the flow of traffic on urban roads.
Adaptive traffic signal control (ATSC) systems depend on sensors to monitor the conditions at intersections in real-time and adjust the timing of signals and phasing. They can be built on various hardware options, including radar computer vision, radar, as well as inductive loops installed in the pavement. They also can capture vehicle data from connected vehicles in C-V2X and DSRC formats with data processed on the edge device, or dispatched to a cloud location to be further analyzed.
Smart traffic lights are able to adjust the idling speed and RLR at busy intersections to ensure that vehicles are moving without slowing down. They also can detect safety issues like lane marking violations and crossing lanes and notify drivers, helping to reduce accidents on city roads.
Smarter controls can also be used to meet new challenges like the growing popularity of ebikes scooters, and other micromobility solutions which have increased during the pandemic. These systems are able to monitor the movement of these vehicles and employ AI to help control their movements at intersections with traffic lights, which aren’t well-suited due to their small size and mobility.