In Pittsburgh the pilot program uses smart technology to optimize traffic signal timings. This helps reduce vehicle stop-and idle time and travel times. The system was developed by a Carnegie Mellon professor in robotics and combines signals from the past with sensors and artificial intelligence to improve the flow of traffic on urban road networks.
Sensors are utilized by adaptive traffic signal control systems (ATSC) to monitor and adjust the timing and phasing of signals at intersections. They can be built on technologytraffic.com/2022/04/28/turning-to-data-room-to-gain-a-competitive-advantage-in-ma different types of hardware, including radar, computer vision, and inductive loops embedded in the pavement. They can also record vehicle data from connected cars in C-V2X and DSRC formats and then process the data on the edge device, or sent to a cloud storage location for further analysis.
By recording and processing real-time data about road conditions such as accidents, congestion, and weather conditions, smart traffic signals will automatically adjust the idling time, RLR at busy intersections and speed limits that are recommended so that vehicles can continue to move without slowed down. They can also detect and alert drivers of safety issues, like the violation of lane markings or crossing lanes, helping to minimize injuries and accidents on city roads.
Smarter controls can also assist to address new challenges like the rise of e-bikes and e-scooters and other micromobility options that have become more popular during the outbreak. These systems monitor vehicles’ movements and employ AI to improve their movements at intersections that aren’t suitable for their size.