AI Case Study
Surtrac reduces intersection wait times by 40% and travel time by 25% on average by optimising traffic signals based on real-time data
The crossing of three major roads in Pittsburgh leads to changing traffic patterns throughout the day, making it difficult to manage congestion with conventional traffic signal control methods. Surtrac reduced travel and wait times by more than 25% and 40% on average, respectively.
Public And Social Sector
From IEEE Spectrum, Sutrac "relies on computerized traffic lights coordinating closely with each other. Radar sensors and cameras at each light detect traffic. Sophisticated AI algorithms use that data to build a timing plan 'that moves all the vehicles it knows about through the intersection in the most efficient way possible'... The computer also sends the data to traffic intersections downstream so they can plan ahead. Unlike other smart traffic-management systems, such as one used in Los Angeles, Smith emphasized that this one is decentralized. So each signal makes its own timing decisions, making it a truly smart system". Surtrac initially piloted its system in June 2012 on 9 intersections in Pittsburgh. It led to an travel-time reduction of 25%, an 40% wait-time reduction. Following that the system was expanded to 50 intersections in 2016.
In its pilot Surtrac reduced travel time by 25%, with more than a 40% reduction in waiting times at intersections and 30-40% fewer stops.
According to Surtrac, the "technology combines concepts from the fields of artificial intelligence and traffic theory, and is designed specifically for optimizing traffic flows in urban road networks, where there are multiple, competing dominant flows that shift dynamically through the day. In contrast to most commercial adaptive traffic control systems, Surtrac takes a totally decentralized approach to control of traffic in a road network: each intersection allocates its green time independently based on actual incoming vehicle flows, and then projected outflows are communicated to neighboring intersections to increase their visibility of future incoming traffic. Reliance on decentralized intersection control ensures maximum real-time responsiveness to actual traffic conditions, while communication of projected outflows to neighbors enables coordinated activity and creation of green corridors. The system is inherently scalable to road networks of arbitrary size, since there is no centralized computational bottleneck."
From IEEE Spectrum: "Idling in rush-hour traffic can be mind-numbing. It also carries other costs. Traffic congestion costs the U.S. economy $121 billion a year, mostly due to lost productivity, and produces about 25 billion kilograms of carbon dioxide emissions". In Pittsburgh, "the crossing of three major roads (Penn Circle, Penn Avenue, and Highland Avenue) leads to changing traffic patterns throughout the day, making it difficult to manage congestion with conventional traffic signal control methods" according to Sutrac.
Data from traffic light cameras