Today the City of Toronto launched a pilot project to implement smart traffic signals at 22 intersections across the city. The pilot project is actually two separate simultaneous pilots, with two different technologies being tested along two corridors. The results (cost, before vs after traffic volumes, and before vs after travel times) will be compared after one year of testing, with the City ultimately choosing one of these technologies for a larger-scale deployment.

This pilot project is part of the City's larger initiative to modernize its traffic signal infrastructure. While the City has been using earlier versions of Adaptive Traffic Signal Control at about 350 intersections since 1992, the technology is outdated, and far more advanced options are now available.

The first technology is called InSync, which "makes decisions based on video-analysis camera detection that measures queue lengths on the approach to the intersection and relays that data to the signal". InSync is being implemented at 10 intersections along Yonge Street, between Yonge Boulevard and Castlefield Avenue.

Vehicle detection using video analysisVehicle detection using video analysis, image courtesy of Smart CCTV Ltd

The second technology is called SCATS (Sydney Coordinated Adaptive Traffic System), which "makes decisions using radar detection to measure traffic flow up and downstream of the intersection." SCATS is being implemented at 12 intersections along Sheppard Avenue East between Neilson Road and Meadowvale Road.

Whether the detection method is radar or video analysis, the objective is the same—to reduce traffic congestion and delays at intersections by using adaptive algorithms to optimize traffic flow in real-time. Depending on the volumes on the intersection approaches, the system can make dynamic changes to offsets, splits, and cycle lengths at each intersection, in order to improve overall flow along the corridor.

Most current signal systems either use fixed signal timings (which can be programmed to vary by the time of day), or use in-pavement detection loops to perform minor modifications to the signal timings based on demand. While these technologies are useful, their limitations (both in terms of detection and computing power) limit the degree to which traffic patterns through an intersection can truly be adapted to.

The latest generation of Smart Signals have the ability to not only detect whether or not there is a car in the cue, but how many cars. This distinction allows the system to perform more detailed calculations on the amount of time that would be required in that phase in order to clear the queue, amongst other things. The latest generation also allows for better communication between signals, as opposed to the current system in which most signals are programmed and operate relatively independently, with only limited communication between them.

Adaptive Signal System in Lancaster, PAAdaptive Signal System in Lancaster, PA, image courtesy of LancasterOnline

There are two scales of potential application of this technology. The first is Smart Signals at individual intersections, which (in theory) improves the throughput by adapting to traffic conditions in real-time. While this type of application is useful for select "problem" intersections, from a network perspective it won't have a substantial impact on travel times across a corridor.

The second, which is what the City's pilot project is trying to do, is implementation of this technology on a corridor or network scale. By implementing this technology across a large section of a corridor, the signals will be able to communicate traffic data amongst themselves, allowing for real-time synchronization to minimize wait times at intersections. For example, if Signal A knows how many cars it released heading eastbound, then Signal B immediately east of there can determine when its eastbound phase should turn green (based on the travel time between intersections), and how long it should stay green for in order to ensure that all vehicles that were released at the earlier intersection make it through that subsequent one.

This also has applications for transit. If a signal detects that a bus has passed through the intersection, it can relay that information to the next signal downstream to extend its green phase a few seconds longer in order to allow that bus to pass through the intersection, minimizing the delay for the bus and its passengers. By contrast, if a signal notices that a bus is waiting in the queue, it can adapt the cycle timing to reduce the amount of green time on the opposite phase, in order to give that bus a green faster.

Intersections included in the Meadowlands Adaptive Signal SystemIntersections included in the Meadowlands Adaptive Signal System for Traffic Reduction, image courtesy of MASSTR

An example of this type of system has been implemented in the Meadowlands area of New Jersey, across the Hudson River from New York City. Called the Meadowlands Adaptive Signal System for Traffic Reduction, the project incorporates 144 traffic signals into a self-adaptive network, which is monitored and controlled in real-time at the NJMC Traffic Management Center. The system handles in excess of 3 million vehicles each day, and it is estimated that it reduces vehicle delays by more than 1.2 million hours per year, and gasoline consumption by more than 1.2 million gallons per year.

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