Road Merging Area Traffic Capacity Modelling Using Simulation Under Vehicle-Infrastructure Cooperative Environment

With the development of vehicle-infrastructure cooperative and autonomous driving technology, travelers will get more convenient travel experience. However, due to the unbalanced development of technology and rules, automatic vehicles (AVs) cannot completely replace human-driven vehicles (HVs), and AVs and HVs will be mixed for quite a long period of time, which will significantly impact traffic efficiency. For some special road segment, such as on-ramp, off-ramp, and weaving area, the capacity drop will more significant. Therefore, road merging area traffic capacity under vehicle-infrastructure cooperative environment are focused on for analyzing. Firstly, according to determining the characteristic evaluation indicators of the lane changing process in the merging area, and analyze the main factors that affect the lane changing process. Then, by analyzing the mixed traffic flow characteristics of different types of automatic vehicles (AVs) and human-driven vehicles (HVs) in the merging area road under a vehicle-infrastructure cooperative environment, the dynamic acceleration and variable lane-changing probability were introduced to improve the traffic flow rules of a cellular automata model. Thirdly, traffic flow simulation environment are built by considering the coupling influence of factors such as penetration rate of AVs on the main road and on-ramp lane, traffic volume on the main road and on-ramp lane, and proportion of large vehicles on the main road, 13 types experiments are simulated for analyzing the relationship between lane-changing evaluation indexes (free lane-changing rate and merging length rate) and lane-changing characteristics influenced factors (penetration rate of AVs on the main road, penetration rate of AVs on the on-ramp lane, proportion of large vehicles on the main road and traffic volume on the on-ramp lane), traffic capacity of merging area are determined using simulation data. The results show that penetration rate of AVs on the main road and on-ramp lane are positively correlated with merging area capacity, while proportion of large vehicles on the main road and traffic volume on the on-ramp lane are negatively correlated with merging area capacity, and the research will play a huge role in the future development of autonomous driving technology.