In Pittsburgh, a pilot program uses smart technology to optimize the timing of traffic signals. This can reduce the amount of time that vehicles stop and idle time and travel times. The system was developed by an Carnegie Mellon professor of robotics the system integrates signals from the past with sensors and artificial intelligence to improve routing in urban road networks.

Adaptive traffic signal control (ATSC) systems rely on sensors to monitor the conditions at intersections in real-time and adjust signal timing and phasing. They can be built on various hardware such as radar, computer vision, and inductive loops embedded in the pavement. They can also collect vehicle data from connected vehicles in C-V2X or DSRC formats and then process the data on the edge device, or transferred to a cloud location for further analysis.

By capturing and processing real-time data regarding road conditions, accidents, congestion, and weather conditions, smart traffic lights can automatically adjust idle time, RLR at busy intersections and speed limits that are recommended to ensure that vehicles can move around freely without slowing them down. They can also identify safety issues such as the violation of lane markings and crossing lanes and notify drivers, helping to prevent accidents on city roads.

Smarter controls also can help to overcome new challenges like the rise of e-bikes and e-scooters and other micromobility options that have become more popular during the pandemic. These systems are able to monitor vehicles’ movement and apply AI to better manage their movements at intersections that aren’t well-suited for their small size.

Deja una respuesta

Tu dirección de correo electrónico no será publicada. Los campos obligatorios están marcados con *