Methodology for Evaluating Impact of Actuated Traffic Signal Control on Connected Vehicle Green Light Prediction

Connected vehicles (CV) that communicate with traffic signals and notify the driver of expected signal status changes have emerged in the last few years. For fixed-time operations it is a deterministic exercise to predict the signal state for a given movement. However, under actuated-coordinated operation, there are stochastic variations of phase start and end times. When traffic engineers enable additional traffic responsive logic to accommodate for changes in demand, the phase timings become more challenging to predict. This paper proposes a methodology to evaluate traffic signal prediction algorithms for vehicle-to-infrastructure CV application. Data was gathered using video footage on minor and major movement of an intersection that operated in actuated-coordination. Data was collected for 176 cycles over two days. Results showed that the CV application can predict the mean start of green within +/-2.7s. The paper concludes by recommending the evaluation methodology and graphical summaries proposed be used as tools for traffic engineers and vehicle manufactures to characterize the stochastic nature of actuated traffic signals and manage expectations of motorists and other stakeholders.