Hybrid models toward traffic detector data treatment and data fusion

doi:10.1109/ICNSC.2005.1461245

淡江大學機構典藏 > 商管學院 > 運輸管理學系暨研究所 > 會議論文 > Item 987654321/68723

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题名:	Hybrid models toward traffic detector data treatment and data fusion
作者:	Wen, Yuh-horng;Lee, Tsu-tian;Cho, Hsun-jung;Lee, Tsu-tian;Cho, Hsun-jung
贡献者:	淡江大學運輸管理學系
日期:	2005-03
上传时间:	2011-10-23 13:42:57 (UTC+8)
出版者:	IEEE Systems, Man, and Cybernetics Society
摘要:	This paper develops a data processing with hybrid models toward data treatment and data fusion for traffic detector data on freeways. hybrid grey-theory-based pseudo-nearest neighbor method and grey time-series model are developed to recover spatial and temporal data failures. Both spatial and temporal patterns of traffic data are also considered in travel time data fusion. Two travel time data fusion models are presented using a speed-based link travel time extrapolation model for analytical travel time estimation and a recurrent neural network with grey-models for real-time travel time prediction. Field data from the Taiwan national freeway no. 1 were used as a case study for testing the proposed models. Study results shown that the data treatment models for faulty data recovery were accurate. The data fusion models were capable of accurately predicting travel times. The results indicated that the proposed hybrid data processing approaches can ensure the accuracy of travel time estimation with incomplete data sets.
關聯:	Proceedings of the 2005 IEEE International Conference on Networking, pp.525-530
DOI:	10.1109/ICNSC.2005.1461245
显示于类别:	[運輸管理學系暨研究所] 會議論文

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