Research On The Judgment Method Of Traffic State At Urban Signalized Intersections

In urban road networks,intersections are important nodes associated with roads,and their operating status is very important.The congestion at intersections can easily spread to road sections,and even the entire road network in severe cases.Therefore,the traffic status of intersections is judged Method research can provide a basis for decision-making for traffic management and control,so that it can greatly improve the current traffic congestion at the intersection and increase the efficiency of the intersection.This article takes urban signalized intersections as the research object,analyzes the characteristics of traffic flow parameters on the basis of summarizing the relevant research status at home and abroad and the basic theory of signalized intersections,and proposes the clustering of signalized traffic state based on GA-FCM algorithm Based on the division method,the intersection traffic state discrimination model based on GRNN algorithm is established.The specific work is as follows:(1)In the theoretical research of signalized intersections,the traffic parameters and commonly used state evaluation indicators of intersections are summarized,the influencing factors of the intersection’s traffic state are analyzed,and the saturation,average delay,and average queue length are taken as the characteristics of signalized intersection traffic state Parameters.By comparing the flow charts of different collection time intervals,the collection time interval of traffic flow parameters is determined to be 5 minutes,and then the relevant parameters are obtained through video shooting and manual extraction.The measured data and queuing theory are combined to analyze the intersection in detail The similar characteristics of traffic flow,as well as the blocking effect and queuing phenomenon of signalized intersections,illustrate the characterizing effect of each characteristic parameter on the traffic state.(2)In terms of traffic state clustering at intersections,this paper uses fuzzy C-means clustering to divide the states represented by traffic flow parameters.The number of traffic state classifications is determined by the clustering validity function,and the traffic state at the intersection is divided into There are four types of smooth flow,stable flow,congested flow,and congested flow.For the problem that the initial clustering center of the fuzzy C-means clustering algorithm is too random,it is easy to converge to the local extremum,the genetic algorithm is used to optimize,and the demonstration is through examples The GA-FCM algorithm proposed in this paper converges faster and has better stability than the traditional FCM algorithm.(3)In the aspect of intersection traffic state discrimination,a GRNN-based intersection traffic state discrimination model is proposed.To improve the accuracy of the model discrimination,cross-validation is used to determine the best smoothing factor of GRNN.Through an example,the accuracy of the GRNN-based traffic state discrimination method proposed in this paper reaches 94.82%.Compared with the BP network,the accuracy rate is improved by 6.88%,which proves that the GRNN-based traffic state discrimination method proposed in this paper is feasible.