A pilot in Pittsburgh is using technology that is smart to optimize traffic signals, thus reducing the amount of time spent on stopping and idling vehicles and overall travel time. The system was designed by a Carnegie Mellon professor in robotics and combines existing signals with sensors and artificial intelligence to improve routing on urban roads.
Adaptive traffic signal control (ATSC) systems depend on sensors to observe real-time conditions at intersections and adjust signal timing and phasing. They can be built on different types of hardware, such as radar, computer vision, and inductive loops embedded within the pavement. They can also record vehicle data from connected cars in C-V2X and DSRC formats and have the data processed on the edge device or transferred to a cloud location to be further analyzed.
Smart traffic lights can regulate the idling speed and RLR at busy intersections to ensure that vehicles are moving without slowed down. They also can detect safety issues such as violations of lane markings or crossing lanes, and alert drivers, helping to reduce accidents on city roads.
Smarter controls are also a way to address new challenges like the popularity of ebikes, Escooters, and other micromobility devices that have grown in popularity during the pandemic. These systems can monitor these vehicles’ movements and employ AI to manage their movements at intersections that aren’t appropriate for their small size.