Smart Technologies for Traffic Signals
In Pittsburgh, a pilot program is using intelligent technology to optimize traffic signal timings. This helps reduce vehicle stop-and idle time as well as travel times. The system was designed by a Carnegie Mellon professor in robotics and combines existing signals with sensors and artificial intelligent to improve routing on urban road networks.
Adaptive traffic signal control (ATSC) systems rely on sensors to track the conditions at intersections in real-time and adjust the timing of signals and their phasing. They can be based on different types of hardware, such as radar, computer vision, and inductive loops embedded in pavement. They can also collect data from connected vehicles in C-V2X and DSRC formats. Data is processed on the edge device, or transmitted to a cloud location for analysis.
By capturing and processing real-time data regarding road conditions, accidents, congestion, and weather, smart traffic lights can automatically adjust idle time, RLR at busy intersections and speed limits that are recommended to keep vehicles moving freely without slowed down. They also can detect and alert drivers of safety concerns, such as traffic violations, lane markings, or crossing lanes, assisting to reduce injuries and accidents on city roads.
Smarter controls are also able to address new challenges like the rise of e-bikes, escooters, and other micromobility options that have become increasingly popular during the pandemic. Such systems can monitor the movements of these vehicles, and utilize AI to help control their movements at traffic light intersections, which aren’t ideal for their small size or maneuverability.