A Green Wave Ecological Global Speed Planning under the Framework of Vehicle–Road–Cloud Integration

Abstract

In response to energy consumption and traffic efficiency reduction caused by intersection congestion, a global speed planning that considered both ecological speed and green wave speed was conducted under the vehicle–road–cloud integration framework. After establishing an instantaneous energy consumption model for pure electric vehicles, a radial basis neural network model was used to estimate the queue length of traffic flow, and an isolated-intersection-based eco-approach and departure (I-EAD) plan was proposed based on a valid traffic signal light model. A two-stage optimization multi-intersections-based eco-approach and departure (M-EAD) strategy with multiple objectives and constraints was proposed to solve the optimal green light window and the optimal speed trajectory. The results of the SUMO/Matlab/Simulink/Python joint simulation platform show that the M-EAD strategy reduces the average travel energy consumption by 16.65% and 8.31%, and the average travel time by 26.33% and 12.53%, respectively, compared to the intelligent driver model (IDM) and I-EAD strategy. The simulation results of the typical traffic scenarios and random traffic scenarios indicate that the speed optimization strategies in this study have good optimization effects on energy conservation and traffic efficiency.