A machine learning approach to urban traffic state detection

Abstract

We propose an urban traffic state detection method based on support vector machine (SVM) and multilayer perception (MLP). Fusing the SVM and MLP classifiers into a cascade two-tier classifier improves the accuracy of the traffic state classification. A cascade two-tier classifier MLP-SVM, a single SVM classifier and a single MLP classifier are then fused to further improve the final detection accuracy. We also implement a Dempster-Shafer (D-S) theory of evidence based classifier. Finally, fusion strategies at the training and implementation phases are proposed to improve the detection accuracy.