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An Adaptive Conjugate Gradient Neural Network–Wavelet Model for Traffic Incident Detection
Author(s) -
Adeli H.,
Samant A.
Publication year - 2000
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 - 10.1111/0885-9507.00189
Subject(s) - computer science , artificial neural network , linear discriminant analysis , artificial intelligence , constant false alarm rate , pattern recognition (psychology) , preprocessor , wavelet , dimensionality reduction , data mining
Artificial neural networks are known to be effective in solving problems involving pattern recognition and classification. The traffic incident‐detection problem can be viewed as recognizing incident patterns from incident‐free patterns. A neural network classifier has to be trained first using incident and incident‐free traffic data. The dimensionality of the training input data is high, and the embedded incident characteristics are not easily detectable. In this article we present a computational model for automatic traffic incident detection using discrete wavelet transform, linear discriminant analysis, and neural networks. Wavelet transform and linear discriminant analysis are used for feature extraction, denoising, and effective preprocessing of data before an adaptive neural network model is used to make the traffic incident detection. Simulated as well as actual traffic data are used to test the model. For incidents with a duration of more than 5 minutes, the incident‐detection model yields a detection rate of nearly 100 percent and a false‐alarm rate of about 1 percent for two‐ or three‐lane freeways.