A pilot in Pittsburgh is utilizing smart technology to optimize traffic signals, thus reducing the amount of time spent on stopping and idling vehicles and overall travel times. The system was designed by a Carnegie Mellon professor in robotics and integrates existing signals with sensors and artificial intelligence to improve the flow of traffic on urban roads.

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 hardware options, including radar, computer vision, and inductive loops installed in the pavement. They can also gather data from connected vehicles in C-V2X and DSRC formats. The data is processed at the edge technologytraffic.com/2020/05/21/the-benefits-of-using-modern-traffic-technologies-by-data-room/ device or sent to a cloud location for analysis.

By collecting and processing real-time information about road conditions and traffic congestion, accidents, and weather conditions, smart traffic lights can automatically adjust idle time, RLR at busy intersections and speed limits that are recommended so that vehicles can continue to move without slowing them down. They also can detect and notify drivers of safety issues such as the violation of lane markings or crossing lanes, helping to prevent injuries and accidents on city roads.

Smarter controls can also help to tackle new challenges like the growth of ebikes, escooters and other micromobility options that have become more well-known during the pandemic. These systems can monitor these vehicles’ movements and employ AI to manage their movements at intersections that are not suitable for their size.

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