A pilot in Pittsburgh is utilizing smart technology to improve traffic signals, thereby reducing vehicle stop-and-idling time and overall travel times. The system was developed by an Carnegie Mellon professor of robotics the system integrates signals from the past with sensors and artificial intelligence to improve the routing within urban road networks.
Adaptive traffic signal control (ATSC) systems rely on sensors to monitor the conditions at intersections in real-time and adjust signal timing and phasing. They can be built on a variety of hardware, including radar computer vision, radar, and inductive loops installed in the pavement. They technologytraffic.com/2022/04/28/turning-to-data-room-to-gain-a-competitive-advantage-in-ma also can capture vehicle data from connected cars in C-V2X or DSRC formats with data processed by the edge device or sent to a cloud storage location to be further analyzed.
By recording and processing real-time data regarding road conditions such as accidents, congestion, and weather conditions, smart traffic signals can automatically adjust idling times, RLR at busy intersections and recommended speed limits so that vehicles can continue to move without causing a slowdown. They can also spot safety issues such as violations of lane markings or crossing lanes, and alert drivers, helping to reduce accidents on city roads.
Smarter controls are also able to overcome new challenges like the rise of e-bikes, e-scooters, and other micromobility options that have become increasingly popular since the pandemic. These systems can track these vehicles’ movements, and utilize AI to manage their movements at intersections that are not well-suited for their small size.