Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0968-090X(03)00020-2
Title: Incident detection using support vector machines
Authors: Yuan, F.
Cheu, R.L. 
Issue Date: Jun-2003
Source: Yuan, F., Cheu, R.L. (2003-06). Incident detection using support vector machines. Transportation Research Part C: Emerging Technologies 11 (3-4) : 309-328. ScholarBank@NUS Repository. https://doi.org/10.1016/S0968-090X(03)00020-2
Abstract: This paper presents the applications of a recently developed pattern classifier called support vector machine (SVM) in incident detection. Support vector machine is constructed from a unique learning algorithm that extracts training vectors that lie closest to the class boundary, and makes use of them to construct a decision boundary that optimally separates the different classes of data. Two SVMs, each with a different non-linear kernel function, were trained and tested with simulated incident data from an arterial network. Test results have shown that SVM offers a lower misclassification rate, higher correct detection rate, lower false alarm rate and slightly faster detection time than the multi-layer feed forward neural network (MLF) and probabilistic neural network models in arterial incident detection. Three different SVMs have also been developed and tested with real I-880 Freeway data in California. The freeway SVMs have exhibited incident detection performance as good as the MLF, one of the most promising incident detection model developed to date. © 2003 Elsevier Ltd. All rights reserved.
Source Title: Transportation Research Part C: Emerging Technologies
URI: http://scholarbank.nus.edu.sg/handle/10635/65698
ISSN: 0968090X
DOI: 10.1016/S0968-090X(03)00020-2
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