Research On Expressway Traffic Condition Recognition Based On Fuzzy Clustering Analysis

Because of the rapid development of economy,the demand of expressway is expanding rapidly.The management of freeway is faced with the problem of more and more congested traffic and a large number of measured data resources being wasted.The management can not get reliable,timely and effective data support and passengers can not understand the state of highway traffic in a timely manner,to a certain extent.The development of the highway was hindered.In order to meet the increasing demand for highway traffic and rapid economic development,the management of the expressway needs to be further intelligentized and more accurate in control of the traffic state of the highway section,which first needs a reliable,timely and effective detection method for the state of the highway traffic.There are many factors affecting the state of highway traffic,including human,car,road,external environment and other factors.The traffic status recognition based on fuzzy clustering analysis synthetically considers the above factors to identify traffic data,and has a good ability to identify traffic status.In addition,because of the different traffic capacity of different regions,it is difficult to set up the fixed traffic congestion threshold accurately to describe the different highway traffic state,and the traffic state recognition method based on fuzzy clustering analysis algorithm is to classify different freeway traffic states,and has good adaptability.This method is discussed and improved in this paper.At present,there are a lot of active detection equipment for expressway.In this paper,according to the data obtained from various detection equipment,three kinds of data are used to identify them.Then the fuzzy clustering analysis method is used to classify the historical data of the target section to get the cluster center.Using the measured data and the Euclidean distance of the cluster center to get the real time traffic state of the expressway,it can effectively excavate the unused traffic data of the expressway,provide the reliable and timely traffic state data for the expressway management department,and provide the convenient travel decision information for the passengers to meet the road.The travel needs of the road users.In addition,this paper,aiming at the problems of fuzzy clustering analysis,proposes the use of simulated annealing algorithm and genetic algorithm to optimize the fuzzy clustering analysis algorithm.The main steps are that the simulated annealing algorithm and genetic algorithm are used to optimize the initial clustering center of random distribution,and the near value or approximate value of the optimal solution is obtained.Then the obtained solution is used as the initial cluster center of the fuzzy clustering analysis algorithm.Finally,the fuzzy clustering analysis algorithm is used to simulate the clustering.The center of clustering obtained by annealing and genetic algorithm is used as the initial cluster center forlocal optimization iteration.The experimental results show that the improved fuzzy clustering algorithm can effectively overcome the shortcomings of the traditional fuzzy clustering analysis algorithm,which results in excessive dependence on the initial clustering center and easily fall into the local optimality.It has a certain practical value for the recognition rate and timeliness of the highway traffic state.