Please use this identifier to cite or link to this item:
|Title:||Hybrid neuro-fuzzy technique for automated traffic incident detection||Authors:||Srinivasan, D.
|Issue Date:||2006||Citation:||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|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Apr 20, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.