A pilot in Pittsburgh is using technology that is smart to optimize traffic signals, thus reducing vehicle stop-and-idling time and overall travel time. It was designed by a Carnegie Mellon professor of robotics The system combines existing signal systems with sensors and artificial intelligence to technologytraffic.com/2021/12/29/generated-post-3/ improve routing within urban road networks.
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 their phasing. They can be based on a variety hardware, including radar, computer vision, and inductive loops embedded in the pavement. They can also capture data from connected vehicles in C-V2X and DSRC formats. Data is pre-processed at the edge device, or sent to a cloud storage location to be analyzed.
By collecting and processing real-time information regarding road conditions, accidents, congestion, and weather conditions, smart traffic signals can automatically adjust idle time, RLR at busy intersections, and recommended speed limits so that vehicles can continue to move without slowed down. They also can alert drivers to safety issues such as the violation of lane markings or crossing lanes. They can also help to reduce accidents and injuries on city roads.
Smarter controls are also a way to overcome new challenges, including the growing popularity of ebikes escooters and other micromobility solutions that have grown in the epidemic. These systems can track these vehicles’ movements and employ AI to better manage their movements at intersections that aren’t suitable for their size.