A pilot in Pittsburgh is utilizing smart technology to improve traffic signals, thereby reducing vehicle stop-and-idling time and overall travel time. Created by an Carnegie Mellon professor of robotics the system integrates existing signal systems with sensors and artificial intelligence to improve routing in urban roads.
Sensors are utilized by adaptive traffic signal control systems (ATSC) to monitor and adjust the timing and the phasing of signals in intersections. They can be based on different types of hardware including radar, computer vision, and 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 on the edge device, or dispatched to a cloud location for further analysis.
Smart traffic lights are able to adjust the idling time and RLR at busy intersections to ensure that vehicles are moving without slowing down. They also can detect and notify drivers of dangers, such as the violation of lane markings or crossing lanes, helping to reduce accidents and injuries on city roads.
Smarter controls can also be used to meet new challenges, like the growing popularity of ebikes Escooters, and other micromobility devices that have grown in popularity during the epidemic. These systems can monitor these vehicles’ movement and apply AI to better manage their movements at intersections that are not suitable for their size.