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Urban Road Traffic State Identification Based on Fuzzy C-mean Clustering
Huang Yanguo, Xu Lunhui, Kuang Xianyan
2015, 34(2):
102-107.
DOI: 10.3969/j.issn.1674-0696.2015.02.22
Through the analysis of urban road traffic characteristics, traffic flow parameters under the same traffic state were dispersed in a two-dimensional region. The traffic condition was divided into four states, and state transitions of traffic flow were described. According to the ambiguity characteristics of urban road traffic state, a real-time traffic condition identification method based on the fuzzy c-means clustering was presented, and the flow, speed and occupancy were taken as feature attribute of sample data. Firstly, fuzzy C-means clustering technique was used to classify the sampled historical data, and the clustering center of different traffic condition was gotten with the method, then in the test module, the real-time traffic data were used to identify which state the traffic data belong to. Finally, the traffic condition of Wenming Avenue was tested and analyzed through the actual collected data with the method. The results are same with the measured results of traffic condition, and it verified the effectiveness of this method.
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