A pilot in Pittsburgh is using smart technology to improve traffic signals, thereby reducing the amount of time a vehicle is idled and stopped, as well as overall travel time. Designed by a 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.

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 built on a variety of hardware, including radar computer vision, radar, as well as inductive loops that are embedded in the pavement. They can also collect data from connected vehicles in C-V2X and DSRC formats. Data is pre-processed at the edge device, or transmitted to a cloud server to be analyzed.

Smart traffic lights are able to adjust the idling speed and RLR at busy intersections to allow vehicles to move without sloweding them down. They also can detect safety issues like violations of lane markings or crossing lanes, and alert drivers, which can help reduce accidents on city roads.

Smarter controls are also able to address new challenges such as the rise of e-bikes, e-scooters, and other micromobility options that have become increasingly popular since the pandemic. These systems can monitor the movement of these vehicles and use AI to improve their movements at traffic light intersections, which aren’t well-suited because of their size or technologytraffic.com/2021/07/08/generated-post/ maneuverability.

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