Green Light Optimal Speed Advisory System Designed for Electric Vehicles Considering Queuing Effect and Driver's Speed Tracking Error

Abstract

The GLOSA (Green Light Optimal Speed Advisory) system provides speed advice to drivers so that drivers can pass through congested intersections at right instant with shorter time and lower energy consumption. Traditional GLOSA system only considers the SPaT (Signal Phase and Timing) of traffic light. However, two another important factors, namely queuing effect and actual tracking error of drivers, are seldomly considered, which degrades the actual performance of the GLOSA system. Intelligent connected vehicles based on V2I (Vehicle to Infrastructure) have great application potential in solving this problem. In this study, firstly, a vehicle queue length estimation method based on V2I technology is proposed to predict the effective green light time. Secondly, a hierarchical GLOSA system is developed, where the upper layer provides the global recommended optimal speed aiming at minimizing energy consumption, while the bottom layer provides the modified recommended speed considering the driver's tracking error. Finally, the tracking error of the driver when executing the recommended speed is derived based on the real-world experiment. Corresponding simulation and field test platforms are also established. Results show that compared with the traditional GLOSA system, the improved GLOSA system considering the vehicle queuing effect and driving error can effectively improve the energy-saving performance of the vehicle.