车联网干线协调控制相位差自适应优化
Offset adaptive optimizing of arterial coordinating under internet of vehicle

摘要 为了提高车联网环境中干线的通行效率，提出一种车联网中干线协调控制相位差自适应优化方法.本文基于车辆换道延误模型建立了以上游交叉口车辆数为输入、下游交叉口信号相位差调节量为输出的模糊控制网络；为解决车流和路况变化造成的控制效果降低问题，以车辆交叉口平均等待时间最小为目标，对模糊规则进行自适应调节；通过基于Q-Paramics的车联网仿真平台对本文提出的方法进行验证.结果表明：本文提出方法控制效果明显优于传统干线协调控制方法；在车流大幅震荡和车流随机波动条件下，较固定规则的干线模糊控制方法能够分别降低车辆平均等待时间12.35%和9.7%.本文方法能够适应车流变化和路况变化，具有更优的适应性、可用性及工程价值.
Abstract：An offset adaptive optimizing method in arterial coordination control under internet of vehicle is proposed to improve traffic efficiency. Firstly, a fuzzy control network is established based on vehicle lane changing delay model. The input of the fuzzy network is the number of vehicle crossing the upstream intersection and the output is the downstream intersection offset. Then, Genetic Algorithm (GA) is used to automatically adjust the fuzzy rules in order to solve the inefficiency caused by the change of traffic flow and environment.At last, a Q-Paramics based vehicle internet simulation platform is built to verify the proposed method. The results show that the proposed method works better than traditional arterial coordination control. And under the simulating situations of fluctuated traffic flow, GA based fuzzy control method reduces vehicle average waiting time 12.35% and 9.7% respectively compared to the fuzzy control method.The methodology proposed in this paper can also adapt to shifty traffic flow and changing environment and has more optimal adaptability, availability and engineering value.