Li-Min Meng, Lu-Sha Han, Hong Peng, Biaobiao Zhang, Ke-Lin Du
{"title":"A machine learning approach to urban traffic state detection","authors":"Li-Min Meng, Lu-Sha Han, Hong Peng, Biaobiao Zhang, Ke-Lin Du","doi":"10.1109/ICICIP.2014.7010332","DOIUrl":null,"url":null,"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.","PeriodicalId":408041,"journal":{"name":"Fifth International Conference on Intelligent Control and Information Processing","volume":"95 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fifth International Conference on Intelligent Control and Information Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICIP.2014.7010332","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
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.