A machine learning approach to urban traffic state detection

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 S...

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Bibliographic Details
Published inFifth International Conference on Intelligent Control and Information Processing pp. 163 - 168
Main Authors Li-Min Meng, Lu-Sha Han, Hong Peng, Biaobiao Zhang, Du, K.-L
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2014
Subjects
Accuracy
Data models
Kernel
Roads
Support vector machines
Training
Vehicles
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Summary: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.
ISBN:1479936499
9781479936496
DOI:10.1109/ICICIP.2014.7010332