Fuzzy data mining and grey recurrent neural network forecasting for traffic information systems
學年 94
學期 1
發表日期 2005-08-15
作品名稱 Fuzzy data mining and grey recurrent neural network forecasting for traffic information systems
作品名稱（其他語言）
著者 Wen, Yuh-horng; Lee, Tsu-tian; Lee, Tsu-tian
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 2005 IEEE International Conference on Information Reuse and Integration
會議地點 Las Vegas, Nevada, USA
摘要 This study presents a systematic process combining trajfic forecasting and data mining models for traffic information systems. Fuzzy c-means clustering model was developed for mining traffic flow-speed-occupancy relationships, then to extrapolate traffic information. The hybrid grey-based recurrent neural network (G-RNN) was developed for traffic parameter forecasting. G-RNN integrates grey modeling into recurrent neural networks that is capable of dealing with both randomness and spatial-temporal properties in trajfic data implicitly. Field data from Taiwan national freeway was used as an example for testing the proposed models. Study results were shown that the G-RNN model is capable of predicting traffic parameters with a high degree of accuracy. The application presents three clusters built from data and recognized three types of traffic conditions. Study results also showed feasibility of the method for advanced traffic information systems.
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 20050815~20050817
通訊作者
國別 TWN
公開徵稿 Y
出版型式 紙本
出處 2005 IEEE International Conference on Information Reuse and Integration, p.p 356-361
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