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Feature Extraction for Traffic Incident Detection Using Wavelet Transform and Linear Discriminant Analysis
Author(s) -
Samant A.,
Adeli H.
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 - 10.1111/0885-9507.00188
Subject(s) - linear discriminant analysis , dimensionality reduction , computer science , feature extraction , pattern recognition (psychology) , artificial intelligence , artificial neural network , discrete wavelet transform , curse of dimensionality , wavelet , feature (linguistics) , data mining , wavelet transform , linguistics , philosophy
To eliminate false alarms, an effective traffic incident detection algorithm must be able to extract incident‐related features from the traffic patterns. A robust feature‐extraction algorithm also helps reduce the dimension of the input space for a neural network model without any significant loss of related traffic information, resulting in a substantial reduction in the network size, the effect of random traffic fluctuations, the number of required training samples, and the computational resources required to train the neural network. This article presents an effective traffic feature‐extraction model using discrete wavelet transform (DWT) and linear discriminant analysis (LDA). The DWT is first applied to raw traffic data, and the finest resolution coefficients representing the random fluctuations of traffic are discarded. Next, LDA is employed to the filtered signal for further feature extraction and reducing the dimensionality of the problem. The results of LDA are used as input to a neural network model for traffic incident detection.