Dynamic Discrimination Of Urban Freeway Traffic State Based On Fuzzy C-Means

Due to the continuous development of human society, Urban sprawl, the rapid development of Socio-economic, Urban transport problems become global issues. The identification of road traffic state in a timely manner, traffic managers can real-time grasp the overall operation of the road, on the other hand could induce travel traffic participants, so as to keep away from traffic jams, reducing travel time, improving the operating conditions of the road network, improve the effective utilization of road. Many large and medium-sized cities in China already have a certain scale of traffic information collection system, collecting a large quantity of traffic data. How to extract useful traffic information from a large quantity of traffic data, real-time identification of the traffic state has certain research value.Currently, the urban expressway is a majority part of the urban road network, undertaking the most dominant of traffic travel of city, urban road network traffic status and travel quality are largely reacted by Freeway state. In this paper, by using of a microwave sensor data, I make some research on freeway traffic state identification methods. Following to main content:Foremost, analysis, select the parameters that affect the urban freeway; researching urban freeway traffic flow data acquisition and pre-processing, according to the actual mined traffic data analysis the time characteristics of traffic flow, provides a theoretical support and data base for the classification and identification of traffic state.Secondly, urban freeway state has many characteristics, such as fuzziness, randomness, uncertainty. After summarizes and analyzes the previous using of fuzzy clustering analysis discriminate the state of the traffic flow, researching the method that based on FCM to discriminate urban freeway traffic state, Solving FCM is sensitive to the initial cluster centers and determined the optimal number of clusters and the value of the weight coefficient.Ultimate, papers using MTLAB algorithm to classify the microwave sensors measured the actual traffic flow data, identification the state of unsorted traffic flow data from the transport state cluster center, cross-identification methods for testing algorithm false positives, compared with the traditional FCM algorithm. The results show that the thesis is accurate and the credibility is higher than traditional algorithms.