Design And Implementation Of Adaptive Guidance System Based On Traffic State Characteristics

With the continuous increase in the number of motor vehicles and the increasing traffic pressure,how to adaptively guide vehicles to improve traffic efficiency has always been an urgent problem to be solved in smart transportation.In recent years,there have been adaptive guidance research methods based on traditional machine learning,which learn from historical traffic data,predict traffic conditions,and develop guidance strategies.However,given the multidimensional and time-varying nature of urban traffic conditions,guidance based on historical laws is difficult to achieve ideal guidance effects in practice.The paper focuses on the operational status of intersection traffic.By establishing a static traffic network model,dynamic traffic elements are organized and managed,and a traffic state feature composition system and calculation method are proposed.The clustering method is used to evaluate the traffic state in real time,thereby improving the real-time and accuracy of traffic state cognition and improving the efficiency of vehicle driving guidance.The paper provides a detailed analysis of the requirements of adaptive guidance systems,proposes methods for organizing and managing dynamic traffic elements,as well as calculating traffic state features.It excavates traffic state features from vehicle trajectories to construct feature datasets,and completes traffic state evaluation and adaptive guidance strategy development.Afterwards,the architecture design of the system functions was completed,the functional modules were clearly divided,and the system was designed and implemented in detail.By establishing an experimental environment to validate the research content of this article,the results show that the scheme can effectively identify the traffic conditions on the road and provide reasonable suggestions for speed.