A pilot in Pittsburgh is using smart technology to improve traffic signals, thereby reducing the amount of time spent on stopping and idling vehicles and overall travel time. The system was developed by a Carnegie Mellon professor in robotics and combines signals that are already in use with sensors and artificial intelligent to improve routing on urban road networks.
Sensors are used by adaptive traffic signal control systems (ATSC) to monitor and adjust the timing and phasing of signals at intersections. They can be based on a variety of hardware, including radar computer vision, radar, and inductive loops installed in the pavement. They also can capture vehicle data from connected vehicles in C-V2X and DSRC formats with data processed on the edge device or sent to a cloud storage location for further analysis.
By collecting and processing real-time information regarding road conditions and traffic congestion, accidents, and weather conditions, smart traffic lights can automatically adjust idling times, RLR at busy intersections and speed limits recommended by the authorities so that vehicles can continue to move without slowing them down. They can also detect and notify drivers of safety issues, like violations of lane markings, or crossing lanes, helping to prevent injuries and accidents on city roads.
Smarter controls are also a way to meet new challenges, such as the growing popularity of ebikes scooters and other micromobility options which have increased during the epidemic. These systems can track the movements of these vehicles and employ AI to improve their movements at intersections with traffic lights, which are not well technologytraffic.com/2020/05/01/modern-traffic-technologies-by-board-room suited because of their size or maneuverability.