Coordinated optimization of traffic delay and risk at intersection under i-VICS

There are problems such as complex control objects, multiple optimization objectives and control variables in traffic control under intelligent Vehicle-Infrastructure Cooperative System(i-VICS). In order to overcome the above problems, a control algorithm considering the penetration rate of connected automated vehicle (CAV) was established in this paper. To be able to control spatiotemporal-right and signal timing as a whole, a spatiotemporal-right solving method based on decision tree, a high-dimensional based genetic (MV-A1) and a low-dimensional based enumerated (MV-A2) signal-solving method were proposed; In order to achieve coordinated optimization of traffic efficiency and safety at intersections, a solution method based on Pareto optimality was proposed; Considering the influence of human-driven vehicle(HV), a CAV lane change and speed control method under mixed traffic conditions is proposed. A simulation experiment platform was developed by python3.7 to simulate and analyze the algorithm proposed in the paper. The results show that when the flow intensity is 0.23-0.45, the control effect is the best; 50% CAV penetration rate is the best benefit point of the control effect; the control algorithm has better adaptability to the time-varying of traffic demand.