A pilot in Pittsburgh is utilizing smart technology to optimize traffic signals, reducing the amount of time a vehicle is idled and stopped, as well as overall travel time. Created by an Carnegie Mellon professor of robotics The system combines signals from the past with sensors and artificial intelligence to improve the routing in urban road networks.

Sensors are utilized by adaptive traffic signal control systems (ATSC) to monitor and adjust the timing and timing of signals at intersections. They can be built on a variety hardware, like radar, computer vision and inductive loops embedded within the pavement. They can also collect data from connected vehicles in C-V2X and DSRC formats. The data is processed at the resource edge device, or sent to a cloud for analysis.

By recording and processing real-time data regarding road conditions, accidents, congestion, and weather, smart traffic lights can automatically adjust idle time, RLR at busy intersections and speed limits recommended by the authorities to keep vehicles moving freely without slowed down. They can also detect and alert drivers of safety issues such as violations of lane markings, or crossing lanes, helping to reduce injuries and accidents on city roads.

Smarter controls are also a way to overcome new challenges, including the popularity of ebikes, Escooters, and other micromobility devices which have increased during the pandemic. These systems monitor vehicles’ movements, and utilize AI to help manage their movements at intersections that aren’t suitable for their size.