Study On Traffic State Discrimination Of Urban Expressway Based On Traffic Parameter Prediction

Urban expressway,as the main artery connecting various areas of the city,has the characteristics of fast,efficient and comfortable.In order to better understand the city intelligent traffic law,traffic state changes,alleviate the city traffic pressure and support for the parties providing the decision-making,judgment and has important significance for prediction of city expressway traffic running state.In this paper,the characteristics of road traffic are studied from two aspects:traffic parameter prediction and traffic state discrimination.The main research aspects of this paper are as follows:Firstly,this paper summarizes the research status of traffic state classification and discrimination,traffic parameter prediction and traffic state evolution at home and abroad,grasps a variety of research methods,and determines the technical route,chapter arrangement and research content of this paper.Secondly,based on the microwave traffic detector data of Beijing Second Ring Expressway,the relationship model between the three parameters of traffic flow is retrospectively analyzed,and the spatial and temporal characteristics of the three parameters of traffic flow are analyzed according to the collected traffic data,and the specific reasons for this phenomenon are comprehensively analyzed.Thirdly,in view of the traffic state changes studied in this paper,the fuzzy c-means clustering algorithm is used to input two groups of traffic three parameters into the data to obtain the clustering center and traffic state partition information;the accuracy of traffic state discrimination is calibrated,and the best combination of parameters is detemiined according to the accuracy,and the flow-speed is the best combination of parameters through the example verification.Fourthly,in order to better study the changes of traffic conditions and predict traffic parameters,and to ensure the accuracy of traffic parameters prediction,the least squares support vector machine(LSSVM)optimized by immune algorithm is used to establish traffic flow prediction model.The immune algorithm is used to optimize the penalty factor and the parameters of the kernel function in the trained LSSVM,and the optimal prediction model is obtained.Velocity and occupancy are taken as input and traffic flow as output.Fifthly,in order to better show the traffic state discrimination and the traffic state evolution,this paper uses the finite state machine model as the model.The predicted traffic parameters are input into the finite state machine to discriminate and evolve the traffic state.The experimental results show that the finite state machine can more intuitively show the evolution of traffic state,and also prove the availability of the finite state machine in the evolution of traffic state discrimination,which can better serve the urban road traffic management and control.