A pilot in Pittsburgh is using smart technology to optimize traffic signals, which is reducing the amount of time a vehicle is idled and stopped, as well as overall travel time. The system was developed by a Carnegie Mellon professor in robotics and combines existing signals with sensors and artificial intelligent to improve routing on urban road networks.
Adaptive traffic signal control (ATSC) systems rely on sensors to track real-time conditions at intersections and adjust the timing of signals and their phasing. They can be based on different types of hardware including radar computers, computer vision, and inductive loops that are installed on the pavement. They can also capture data from connected Learn More Here vehicles in C-V2X and DSRC formats. Data is pre-processed at the edge device, or sent to a cloud to be analyzed.
By recording and processing real-time data regarding road conditions traffic, accidents, congestion and weather conditions, smart traffic signals will automatically adjust the idling time, RLR at busy intersections and speed limits recommended by the authorities to ensure that vehicles can move around freely without causing a slowdown. They can also detect and notify drivers of safety concerns, such as violations of lane markings, or crossing lanes. This helps to prevent injuries and accidents on city roads.
Smarter controls can also help to tackle new challenges like the growth of e-bikes and e-scooters and other micromobility options that have become more popular during the pandemic. These systems can track the movement of these vehicles and use AI to improve their movements at traffic light intersections which aren’t ideal to their small size or mobility.
