In Pittsburgh the pilot program has been developed that uses intelligent technology to optimize the timing of traffic signals. This can reduce the amount of time that vehicles stop and idle time as well as travel times. The system was designed by an Carnegie Mellon professor in robotics technologytraffic.com/2021/07/08/generated-post-2/ and combines existing signals with sensors and artificial intelligence to improve the flow of traffic on urban roads.
Sensors are used 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, including radar computers, computer vision, and inductive loops incorporated into the pavement. They also can capture vehicle data from connected vehicles in C-V2X and DSRC formats and then process the data on the edge device, or sent to a cloud server to be further analyzed.
By capturing 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 speed limits that are recommended to allow vehicles to move freely without slowed down. They also can detect dangers such as violations of lane markings or crossing lanes and notify drivers, helping to reduce accidents on city roads.
Smarter controls can also be used to overcome new challenges, including the rise of ebikes scooters and other micromobility options that have risen during the pandemic. These systems are able to monitor the movement of these vehicles and use AI to control their movements at intersections for traffic lights, which aren’t ideal because of their size or maneuverability.
