Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/70503
Title: Hybrid neuro-fuzzy technique for automated traffic incident detection
Authors: Srinivasan, D. 
Sanyal, S.
Tan, W.W. 
Issue Date: 2006
Source: Srinivasan, D.,Sanyal, S.,Tan, W.W. (2006). Hybrid neuro-fuzzy technique for automated traffic incident detection. IEEE International Conference on Neural Networks - Conference Proceedings : 713-719. ScholarBank@NUS Repository.
Abstract: This paper proposes a novel technique for automatic incident detection on highways using a hybrid neuro-fuzzy system. The proposed neuro-fuzzy system employs a self rule generating algorithm that organizes the training data into clusters and automatically learns the fuzzy rules. Modified linear least squares regression models are employed for training of parameters. Real I-880 freeway traffic data is used to test the effectiveness of the proposed algorithm. The results obtained show high potential for the application of this neurofuzzy system to automated traffic incident detection. © 2006 IEEE.
Source Title: IEEE International Conference on Neural Networks - Conference Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/70503
ISBN: 0780394909
ISSN: 10987576
Appears in Collections:Staff Publications

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