Research On Key Technologies Of Traffic Analysis Based On Ant Colony Algorithm And Nonparametric Regression Model

Food,clothing,shelter and transportation are the most common needs in human life,and "transportation" as one of human needs,occupies an important part.In modern human production activities,vehicles are the main means of transportation.The rapid growth of vehicles is accompanied by traffic congestion.Although cities are expanding,many cities have limited number of roads that can accommodate traffic flow due to insufficient early planning.Although changing road planning can fundamentally solve traffic congestion,it can not be improved in a short time.Faced with this problem,the design of traffic intelligent system came into being.This technology can not only alleviate the traffic congestion problem in a short time,make rational use of real resources,but also meet the development requirements of modern society.Traffic analysis technology is one of the cores of intelligent transportation system.It can realize intelligent control and guidance of road vehicles and alleviate congestion by analyzing key factors such as roads and traffic flow rates.In this paper,the key technologies related to traffic analysis are studied based on ant colony algorithm and nonparametric regression model,mainly short-term traffic flow prediction based on nonparametric regression model and path planning based on ant colony algorithm.The shortterm traffic flow rate prediction is to use the existing traffic flow data to predict the traffic flow rate in the next few minutes to more than ten minutes;Path planning is based on the road and road condition information,and reasonably plans the travel route according to the needs of travelers,so as to save time,energy consumption and other costs.In order to effectively combine the two,this paper takes the short-term traffic flow prediction results as the supporting conditions of path planning.Aiming at the time and space approximation of urban vehicle traffic flow,the historical traffic data of time series are fused,the concept of "historical state vector of time series" is proposed,and the historical state vector(HTSV)based on historical traffic data of time series is designed.Taking this vector as a new state vector of the k-nearest Neighbor Nonparametric Regression Model based on spatio-temporal similarity(STS-KNN),and using the distance measurement function to calculate the correlation threshold of this vector,a "K-Nearest Neighbor Nonparametric Regression Prediction Model Integrating historical time series"(HSTS-KNN)is proposed.Through the prediction experiment of this model,the prediction accuracy of hsts-knn model is about 5% higher than that of sts-knn model,which improves the prediction accuracy.For the problem of path planning,based on the analysis of the research status of path planning at home and abroad,through the comparison of various path planning algorithms,the ant colony algorithm is determined as the research algorithm of path planning in this paper,and some improvements are made on the basis of the traditional ant colony algorithm,adding "fallback method" and "memory table",and through Matlab simulation experiments,Compared with the basic ant colony algorithm,the average running time of the improved ant colony algorithm is reduced by 30.51%,and the average route is shortened by 34.64%,which verifies the feasibility of the algorithm.Finally,combined with the traffic flow prediction results of HSTS-KNN model,it is used as the path planning weight,and a Web GIS prototype system is built to carry out the path planning experiment of improved ant colony algorithm.The average traffic time of vehicles is reduced by about 11.5%.It is verified that the combination of the two technologies can support avoiding congestion and saving time to a certain extent..