A pilot in Pittsburgh is using smart technology to improve traffic signals, thereby reducing the amount of time spent on stopping and idling vehicles and overall travel times. The system was developed by an Carnegie Mellon professor in robotics and integrates existing signals with sensors and artificial intelligent to improve routing on urban road networks.
Adaptive traffic signal control (ATSC) systems depend on sensors to monitor the conditions at intersections in real-time and adjust the timing of signals and their phasing. They technologytraffic.com/2022/04/28/turning-to-data-room-to-gain-a-competitive-advantage-in-ma/ can be based on various types of hardware, including radar computer vision, radar, as well as inductive loops that are installed on the pavement. They also can collect data from connected vehicles in C-V2X and DSRC formats. Data is pre-processed at the edge device, or transmitted to a cloud storage location for analysis.
By capturing and processing real-time data about road conditions traffic, accidents, congestion and weather conditions, smart traffic signals can automatically adjust idle time, RLR at busy intersections and recommended speed limits to keep vehicles moving freely without slowing them down. They also can alert drivers to dangers, such as the violation of lane markings or crossing lanes, helping to minimize injuries and accidents on city roads.
Smarter controls can also assist to overcome new challenges such as the rise of e-bikes and e-scooters and other micromobility options that have become more popular during the outbreak. Such systems can monitor the movement of these vehicles and use AI to help control their movements at intersections for traffic lights, which are not well suited to their small size or maneuverability.