Highway traffic abnormal state detection based on PCA-GA-SVM algorithm

摘要

In the highway traffic abnormal state detection, Support Vector Machine (SVM) algorithm is widely researched in recent years, but it still has some limitations. Aiming at the problem of improper selection of feature vector, the space and time characteristics of highway traffic abnormal state data is summarized, and the feature vectors of SVM are selected by Principal Component Analysis (PCA) properly. To solve the model parameters selection problem, the theory of Genetic algorithm (GA) is used to select SVM model parameters effectively. Also two-class SVM classification is extended to multi-class SVM classification which has a better command of the traffic running state on highway. According to the severity of the traffic incident, the traffic is divided into the state of no event, the state of mild congestion and severe congestion. The test software of SVM algorithm and the improved one are developed by using MATLAB and LIBSVM tool. The experimental result shows that the improved algorithm has a higher accuracy and a higher detection rate.