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Enhancing Neural Network Traffic Incident‐Detection Algorithms Using Wavelets
Author(s) -
Samant A.,
Adeli H.
Publication year - 2001
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/0885-9507.00229
Subject(s) - computer science , artificial neural network , constant false alarm rate , discrete wavelet transform , fuzzy logic , wavelet , algorithm , artificial intelligence , preprocessor , pattern recognition (psychology) , rate of convergence , data mining , wavelet transform , key (lock) , computer security
Researchers have presented freeway traffic incident‐detection algorithms by combining the adaptive learning capability of neural networks with imprecision modeling capability of fuzzy logic. In this article it is shown that the performance of a fuzzy neural network algorithm can be improved through preprocessing of data using a wavelet‐based feature‐extraction model. In particular, the discrete wavelet transform (DWT) denoising and feature‐extraction model proposed by Samant and Adeli (2000) is combined with the fuzzy neural network approach presented by Hsiao et al. (1994). It is shown that substantial improvement can be achieved using the data filtered by DWT. Use of the wavelet theory to denoise the traffic data increases the incident‐detection rate, reduces the false‐alarm rate and the incident‐detection time, and improves the convergence of the neural network training algorithm substantially.