Traffic signal engineering control has always been an important part of urban traffic management.Taking single-point intersection,the smallest unit in the urban network,as the research object,this paper introduces deep Q learning algorithm and puts forward a new motion space design method to solve the problem that the traditional method is difficult to deal with the different arrival rates of traffic in each direction of the intersection.The travel time prediction model is built by integrating Bayes and support vector machine.Collect vehicle routing data to predict travel path and phase requirements.The results show that in the simulation test of the two traffic conditions,when the simulation time of the proposed control method exceeds 400s in the normal peak period,the average delay of the vehicle is basically stable between 32 and 35s.The research is expected to improve the efficiency of road traffic and pro-vide convenient and sustainable travel experiences for city dwellers.
关键词
深度Q学习/交通信号/控制/贝叶斯优化/交通状态
Key words
deep Q learning/traffic signals/controls/bayesian optimization/traffic condition
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