In Pittsburgh the pilot program is using smart technology to optimize timings for traffic signals. This can reduce the amount of time that vehicles stop and idle time as well as travel times. Created by a Carnegie Mellon professor of robotics The system combines existing signal systems with sensors and artificial intelligence to improve the routing in urban road networks.
Adaptive traffic signal control (ATSC) systems depend on sensors to observe the condition of intersections in real time and adjust the timing and phasing of signals. They can be built on various hardware options, including radar computer vision, radar, as well as inductive loops that are embedded in the pavement. They also can capture vehicle data from connected cars in C-V2X or DSRC formats and have the data processed by the edge device or dispatched to a cloud location to be further analyzed.
By taking and processing real-time data regarding road conditions, accidents, congestion, and weather conditions, smart traffic signals can automatically adjust idle time, RLR at busy intersections and speed limits that are recommended to allow vehicles to move freely without causing a slowdown. They can also identify safety issues like the violation of lane markings and crossing lanes and notify drivers, helping to reduce accidents on city roads.
Smarter controls are also a way to address new challenges, including the increasing popularity of ebikes, escooters and other micromobility solutions which have increased during the pandemic. These systems can track the movements of these vehicles, and utilize AI to improve their movements at traffic light intersections which aren’t ideal for their small size or maneuverability.
https://technologytraffic.com/2022/11/07/successful-ai-tools-for-business-to-test-now/
