Hybrid models toward traffic detector data treatment and data fusion
學年 93
學期 2
發表日期 2005-03-19
作品名稱 Hybrid models toward traffic detector data treatment and data fusion
作品名稱（其他語言）
著者 Wen, Yuh-horng; Lee, Tsu-tian; Cho, Hsun-jung; Lee, Tsu-tian; Cho, Hsun-jung
作品所屬單位 淡江大學運輸管理學系
出版者 IEEE Systems, Man, and Cybernetics Society
會議名稱 Proceedings of the 2005 IEEE International Conference on Networking
會議地點 Tucson, Arizona, USA
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.
關鍵字
語言 en
收錄於
會議性質 國際
校內研討會地點
研討會時間 20050319~20050319
通訊作者
國別 USA
公開徵稿 Y
出版型式 紙本
出處 Proceedings of the 2005 IEEE International Conference on Networking, pp.525-530
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