Abstract

This study presented a method of real-time traffic condition identification based on the fuzzy c-means clustering and travel time prediction on urban expressway. The traffic flow characteristics of expressway were analyzed and the traffic flow states were divided into four classes. Then the fuzzy c-means clustering technique was used to classify the sampled historical data and the clustering center of different traffic condition was gotten. In the test module, the real-time traffic data were used to identify which state the traffic data belong to. Based on the analysis, a travel time prediction model of urban expressway was given by using fuzzy regression. According to the collecting real-time traffic dada and the result of traffic state identification, a method of predicting travel time was introduced. Finally, an urban expressway in Guanzhou was as an example and the result of traffic state identification was same with the results of actual measurement data and questionnaire survey through drivers. The result of travel time prediction show that the predicted results had better fitting degree and precision and the feasibility of this method was verified. It can provide the basis for urban expressway traffic control and traffic induction.

Keywords:

Fuzzy C-means clustering, fuzzy regression, traffic flow state, travel time prediction, urban expressway,

References

Competing interests

The authors have no competing interests.

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Copyright

The authors have no competing interests.