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	Hybrid Multi-Metric K-Nearest Neighbor Regression For Traffic Flow Prediction
	Hong, Haikun ; Huang, Wenhao ; Xing, Xingxing ; Zhou, Xiabing ; Lu, Hongyu ; Bian, Kaigui ; Xie, Kunqing
	2015
关键词	NEURAL-NETWORK VOLUME ARIMA SVR
英文摘要	Traffic flow prediction is a fundamental component in Intelligent Transportation Systems (ITS). Nearest neighbor based nonparametric regression method is a classic data-driven method for traffic flow prediction. Modern data collection technologies provide the opportunity to represent various features of the nonlinear complex system which also bring challenges to fuse the multiple sources of data. Firstly, the classic Euclidean distance metric based models for traffic flow prediction that treat each feature with equal weight is not effective in multi source high-dimension feature space. Secondly, traditional hand-crafting feature engineering by experts is tedious and error prone. Thirdly, the traffic conditions in real-life situation are too complex to measure with only one distance metric. In this paper, we propose a hybrid multi-metric based k-nearest neighbor method (HMMKNN) for traffic flow prediction which can seize the intrinsic features in data and reduce the semantic gap between domain knowledge and handcrafted feature engineering. Experimental results demonstrate multi-source data fusion helps to improve the performance of traffic parameter prediction and HMMKNN outperforms the traditional Euclidean-based k-NN under various configurations. Furthermore, visualization of feature transformation clustering results implies the learned metrics are more reasonable.; EI; CPCI-S(ISTP); bkg@pku.edu.cn; 2262-2267; 2015-October
语种	英语
出处	2015 IEEE 18TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS
DOI标识	10.1109/ITSC.2015.365
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436526]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Hong, Haikun,Huang, Wenhao,Xing, Xingxing,et al. Hybrid Multi-Metric K-Nearest Neighbor Regression For Traffic Flow Prediction. 2015-01-01.

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