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|Title:||Incident detection using support vector machines||Authors:||Yuan, F.
|Issue Date:||Jun-2003||Citation:||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|
|Appears in Collections:||Staff Publications|
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