A pilot in Pittsburgh is using technology that is smart to improve traffic signals, thereby reducing vehicle stop-and-idling time and overall travel times. The system was developed by an Carnegie Mellon professor in robotics and integrates existing signals with sensors and artificial intelligence to improve the efficiency of urban road networks.

Sensors are utilized by adaptive traffic signal control systems (ATSC) to monitor and adjust the timing and phasing of signals at intersections. They can be based on various types of hardware, such as radar, computer vision or inductive loops embedded in the pavement. They can also capture vehicle data from connected cars in C-V2X or DSRC formats and have the data processed by the edge device or sent to a cloud storage location for further analysis.

Smart traffic lights can adjust the time of idle and RLR at busy intersections to keep vehicles moving without sloweding them down. They can also identify safety issues like crossing lanes and notify drivers, which can help reduce accidents on city roads.

Smarter controls technologytraffic.com/2021/12/29/generated-post-4 can also help in tackling new challenges, like the growth of ebikes, escooters and other micromobility options that have become more popular since the pandemic. These systems can track these vehicles’ movements and use AI to improve their movements at intersections that are not suitable for their size.