He, Suiyi

Running red traffic signals is a major cause of traffic collisions and resulting injuries and fatalities. Despite extensive prior work on systems to reduce red light violations, they continue to be a major problem in practice, partly because existing systems suffer from the flaw of providing the same guidance to all drivers. As a result, some violations are avoided, but other drivers ignore or respond inappropriately to red light running systems, resulting in safety issues overall. We present a novel method of providing accurate warnings to individual drivers to avoid the broad guidance approach of most existing systems. Recognizing if a driver will run red lights is highly dependent on signal phase and timing, traffic conditions along the road, and individual driver behavior, the proposed warning system contains three parts: a traffic prediction algorithm, an individual warning signal optimizer, and a driver warning display. The traffic prediction algorithm predicts future traffic states along the road towards the signalized intersections using the latest traffic conditions obtained through vehicle-to-vehicle and vehicle-to-infrastructure communications. Then, an optimization problem is formulated to compute the optimal warning signal based on predicted traffic states and driver reaction model. Finally, the optimal warning signal is shown on the display screen to advise driver on how much braking is needed to avoid running the red light. The results of both simulated driving scenarios and real-world road tests show that the proposed system provides more effective and accurate warning signals to drivers, helping them avoid running red lights.

The objective of this methodology is to refine the preliminary results from previous work (11% fuel savings for one vehicle, one intersection) to an entire corridor of SPaT signals, with different CV market penetration, and with driver awareness of fuel savings benefits. The research will proceed in three parts. First, several vehicles will be instrumented with DSRC receivers and GPS tracking to record SPaT data and the vehicle trajectories together. Offline, the project team will optimize the speed and powertrain control based on recorded SPaT data, using the recorded vehicle trajectories to identify the constraints of traffic flow. A living lab consisting of a GM car engine loaded by a transient hydrostatic dynamometer will be used to measure the fuel consumption with and without speed control. Second, the project team will conduct traffic flow simulations to study the impacts of higher market penetration on the overall fuel benefits, including the benefits to legacy vehicles which unintentionally use SPaT based speed controls by following CVs. Third, network models will be used to predict changes in route choices as drivers recognize the benefits of fuel savings in the route utility. The numerical predictions of fuel savings will be combined into cost/benefit analyses to inform MnDOT on the future deployment of SPaT on other corridors.