A Novel Support Vector Machine Model of Traffic State Identification of Urban Expressway Integrating Parallel Genetic and C-Means Clustering Algorithm

The real-time discrimination of urban expressway traffic state is an important reference for traffic management departments to make decisions. In this paper, a parallel genetic fuzzy clustering algorithm is proposed to overcome the shortcomings of the fuzzy c-means clustering algorithm. A traffic state discrimination model is established by using the support vector machine, and the parameters of the support vector machine are optimized by using particle swarm optimization, network search and genetic algorithm, so as to obtain the parameter group that can make the training model reach the maximum accuracy. Finally, the model is verified by the measured data. The convergence speed and clustering efficiency of parallel genetic fuzzy clustering and original fuzzy c-means clustering are compared. The results show that each iteration can converge to the global minimum value, and the number of iterations is small, and the clustering efficiency is high, which lays a foundation for the subsequent training of SVM.