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https://www.nationalacademies.org/trb/events

Development of a Scalable, Low-Cost, Environmentally-Friendly Adaptive Traffic Signal Control (SLE-ATSC) System

As an enhanced method for vehicle detection at signalized intersections, it is possible to use vehicle-probe data from smartphones, Global Navigation Satellite System (GNSS) receivers, and other types of mobile devices to complement existing traffic sensing and signal control, resulting in lower energy consumption. Using these additional data, it is now possible to estimate reliable traffic queue lengths at high-density traffic intersections. Given real-time reliable traffic queue lengths, it is possible then to dynamically adjust the signal phase and timing of an intersection, with the goal of minimizing traffic queues, waiting times, and energy use. Using UC Riverside’s Innovation Corridor as a target arterial roadway, the research team will develop a scalable, low-cost, environmentally-friendly adaptive traffic signal control (SLE-ATSC) system based on receiving real-time traffic data from sources such as TomTom and INRIX. The signal control system will be implemented for several of the key intersections along the corridor, using a calibrated state-of-the-art traffic simulation platform. Various metrics will be evaluated, comparing the existing traffic signal phase and timing to the new dynamic signal phase and timing resulting from the adaptive signal control system. Using the calibrated simulation model, traffic system metrics will be estimated. In addition, part of the research team (TSU) will utilize their driving simulators as part of a “Hardware-in- the-Loop” testing system for the proposed adaptive traffic signal control system. The traffic simulation model developed at UCR will interface directly with the TSU driving simulators, allowing the research team to see more realistic driving behavior operating in the simulation platform. This will provide more realistic measures of the overall system performance, with a focus on safety, mobility, and environmental metrics.