NDLI: Fuzzy data mining and grey recurrent neural network forecasting for traffic information systems

Content Provider	IEEE Xplore Digital Library
Author	Yuh-Horng Wen Tsu-Tian Lee
Copyright Year	2005
Description	Author affiliation: Nat. Chiao Tung Univ., Hsinchu, Taiwan (Yuh-Horng Wen)
Abstract	This study presents a systematic process combining traffic 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 traffic 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.
Sponsorship	IEEE Syst., Man and Cybernetics Soc. (IEEE SMC)
Starting Page	356
Ending Page	361
File Size	538702
Page Count	6
File Format	PDF
ISBN	0780390938
DOI	10.1109/IRI-05.2005.1506499
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Fuzzy systems Data mining Recurrent neural networks Telecommunication traffic Information systems Traffic control Vehicle detection Detectors Inductance
Content Type	Text
Resource Type	Article

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