Research on Real-time Decision Making for Traffic Lights Based on Deep Learning
In order to effectively alleviate the pressure of urban traffic and improve the intelligent level of traffic signal control,a real-time decision-making model method for multi-intersection traffic lights based on image recognition fusion deep learning is proposed.The image recognition method is used to identify the congestion status,a real-time decision-making mod-el for regional multi-intersection traffic lights is built,a deep learning network is constructed with the highest traffic score in the total scheduling period as the objective function,and the machine learning idea is used to optimize the decision-making scheme.The capacity of the decision-making scheme is increased through pre-training,and the on-site deci-sion-making time is shortened to achieve the purpose of real-time decision-making.The research model is applied to the Zhongshan Road Section of Wuchang District,Wuhan City,Hubei Province for model demonstration and result analysis.And the analysis results show that the model method established in the research can effectively solve the problem of joint scheduling of traffic lights at multiple intersections,and provide more reasonable decision-making schemes for traffic managers.
万方数据
维普
