A pilot in Pittsburgh uses smart technology to optimize traffic signals, thus reducing vehicle stop-and-idling time and overall travel time. The system was developed by an Carnegie Mellon professor in robotics and combines signals from the past with sensors and artificial intelligence to improve the routing of urban roads.
Adaptive traffic signal control (ATSC) systems rely on sensors to track the condition of intersections in real time and adjust the timing of signals and their phasing. They can be built on a variety of hardware, including radar computer vision, radar, and inductive loops that are installed on the pavement. They can also collect vehicle data from connected cars in C-V2X or DSRC formats with data processed at the edge device or sent to a cloud storage location to be further analyzed.
By taking and processing real-time data about road conditions, accidents, congestion, and weather conditions, smart traffic signals can automatically adjust idling time, RLR at busy intersections and speed limits recommended by the authorities to allow vehicles to move freely without slowed down. They also can identify and warn drivers of dangers, such as violations of lane markings, or crossing lanes, helping to technologytraffic.com/2022/04/28/turning-to-data-room-to-gain-a-competitive-advantage-in-ma prevent injuries and accidents on city roads.
Smarter controls are also a way to overcome new challenges, including the increasing popularity of ebikes, Escooters, and other micromobility devices that have risen during the epidemic. These systems monitor vehicles’ movements and employ AI to manage their movements at intersections that aren’t well-suited for their small size.
