Traffic State Identification for Urban Expressway Based on Projection Pursuit Dynamic Cluster Model

In order to improve the accuracy of traffic state identification for urban expressway based on the spot traffic parameters, a traffic state identification method based on projection pursuit dynamic cluster model was proposed using the mapping relationship between spot traffic parameters and traffic state. First, the projection index function was constructed by combined use of the projection pursuit technology and the dynamic cluster method, and the shuffled frog leaping algorithm was used to optimize the projection direction. Then, the traffic state identification threshold was determined using simulation data. Finally, validation and comparative analysis were carried out using both the simulated data and measured data. Experimental results indicate that the proposed model can effectively improve the accuracy of traffic state identification. The average identification rate is 97.01% and the average false identification rate is 0.86%. The average identification accuracy of proposed method is 8.9% and 4.5% higher than the BP neural network model and the fuzzy C-means clustering model, respectively.