A machine learning approach to urban traffic state detection

Li-Min Meng, Lu-Sha Han, Hong Peng, Biaobiao Zhang, Ke-Lin Du
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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.
城市交通状态检测的机器学习方法
提出一种基于支持向量机(SVM)和多层感知(MLP)的城市交通状态检测方法。将SVM和MLP分类器融合为级联两层分类器,提高了流量状态分类的准确率。然后将级联两层分类器MLP-SVM、单SVM分类器和单MLP分类器融合,进一步提高最终的检测精度。我们还实现了基于证据的分类器的Dempster-Shafer (D-S)理论。最后,提出了训练阶段和实施阶段的融合策略,以提高检测精度。
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