Analysis of Mixed Vehicle Traffic Flow at Signalized Intersections Based on the Mixed Traffic Agent Model of Autonomous-Manual Driving Connected Vehicles

Signalized intersection is an important node of urban traffic network, but also the most likely place in the urban traffic network to generate congestion, complex traffic conditions; and relatively high traffic flow will cause the reduction of urban traffic network operating efficiency, or even cause the paralysis of the urban traffic network. With the participation of pedestrians, the situation at the intersection will be more complex and diverse. In recent years, with the rapid development of automobile and related technology, manual driving connected vehicle (MDCV) and autonomous driving intelligent connected vehicles (ADICV) are gradually entering people’s daily life. And ADICVs have coordinated and unified driving behaviors. In short, ADICVs have similar driving habits and can accelerate and decelerate at a systematic synchronization pace, which can help alleviate road traffic congestion and improve transportation network operation efficiency. Therefore, this paper uses the combination of ant algorithm and cellular automata to establish a pedestrian model, and constructs a traffic simulation model for the mixed traffic flow of the autonomous-manually driving vehicle agent at the intersection with/without pedestrian interference. Based on the assumption that the ADICVs have the coordinated driving behaviors, the paper analyzes the influence of green time ratio, mixed traffic ratio and average delay time on the traffic flow of MDCVs and ADICVs. Through the statistical analysis of the average velocity, traffic flow and other parameters, the mechanism and conclusion of the mixed traffic flow of MDCVs and ADICVs are obtained.