Research On The Urban Road Traffic State Identification Based On Fuzzy C-Means

With the rapid development of the economic and the continuous improvement of urbanization, the Contradiction between the transportation need and the traffic facilities becomes increasingly serious due to the fast increased need of road transportation and the relatively slow construction of road traffic facilities. Subsequently, transportation safety and efficiency have been going down gradually and leading to other traffic problems. As the basis of the traffic guidance and the traffic control, it is essential to get accurate traffic state identification.In this paper, the urban road traffic state identification is researched. As the traffic state is fuzzy and uncertain, the fuzzy c-means algorithm is employed to identify the traffic state. Traffic state identification are determined on the basis of the research of the traffic state identification indexes according to the traffic parameter selection principle. The traffic data has been pretreated to decrease the interference of noise, and then the identification results are obtained more accurately. In order to obtain the best clustering results, fuzzy weighted index m and the number of clusters c are researched aimed to get the best optimized m and c values. To make up the convergence slower shortcoming of the FCM algorithm, the HCM algorithm is combined with the FCM algorithm, and it turns out to be a good solution to this problem. In the end, this solution has been put into practice, the traffic state of the selected urban freeway has been obtained accurately and its identification has been evaluated reasonably.