A pilot in Pittsburgh is utilizing smart technology to optimize traffic signals, thus reducing the amount of time a vehicle is idled and stopped, as well as overall travel times. The system was designed by a Carnegie Mellon professor in robotics and combines signals from the past with sensors and artificial intelligence to improve the efficiency of urban roads.
Adaptive traffic signal control (ATSC) systems depend on sensors to observe the real-time conditions at intersections and adjust signal timing and phasing. They may be based on various hardware such as radar, computer vision and inductive loops that are embedded into the pavement. They also can collect data from connected vehicles in C-V2X and DSRC formats. The data is processed at the technologytraffic.com/2021/07/08/generated-post edge device, or sent to a cloud storage location to be analyzed.
Smart traffic lights can adjust the idling speed and RLR at busy intersections to keep vehicles moving without slowing down. They can also identify and warn drivers of dangers, such as traffic violations, lane markings, or crossing lanes, helping to reduce accidents and injuries on city roads.
Smarter controls are also a way to tackle new challenges, such as the increasing popularity of ebikes, escooters and other micromobility solutions which have increased during the pandemic. These systems can track these vehicles’ movements, and utilize AI to better manage their movements at intersections that are not appropriate for their small size.