Short Term Traffic Flow Prediction Method based on Fuzzy Clustering Algorithm

With the rapid development of urbanization, the number of urban motor vehicles is rising rapidly, and the contradiction between supply and demand of urban traffic is increasingly tense. Nowadays, one of the effective ways to solve traffic problems is to build intelligent transportation system. As the key technology of this intelligent system, traffic prediction is the premise and key of traffic guidance and control. In the face of the above development, a short- term traffic flow prediction method based on fuzzy clustering algorithm is proposed. With the help of the basic characteristic parameters of traffic flow, the short-term traffic flow data are obtained and preprocessed. On this basis, the short-term traffic flow prediction evaluation index is defined, and the "distance" measurement conditions for the two criteria are established by publishing traffic information service, so as to realize the smooth application of the short-term traffic flow prediction method based on fuzzy clustering algorithm. The experimental results show that, with the support of fuzzy clustering algorithm, the accuracy of artificial short-term traffic flow prediction is greatly improved. Compared with the quadratic exponential smoothing method, this new prediction method has greater feasibility and application value.