Traffic Incident Detection Using Multiple-Kernel Support Vector Machine

Abstract

This paper presents applications of the multiple-kernel learning support vector machine (MKL-SVM) in traffic incident detection. The standard SVM was applied in traffic incident detection and achieved good results. However, the results depended greatly on the kernel function and parameters, and choosing the appropriate ones for the SVM was a procedure of trial and error. Unlike the SVM, the MKL-SVM used a convex combination of basic kernel functions instead of a single basic kernel function to construct an adaptive SVM model. This adaptive model could improve average performance in traffic incident detection just by randomly selecting the kernel function and parameters. As a result, the MKL-SVM avoided the burden of choosing the appropriate kernel function and parameters. The SVM ensemble algorithm trained many individual SVM classifiers to construct the classifier ensemble and then used this classifier ensemble to detect traffic incidents. Consequently, training occurred many times. Compared with the SVM ensemble algorithm, the training time cost of the MKL-SVM was much lower because training occurred only once. Extensive experiments were performed to evaluate the performance of three algorithms: standard SVM, SVM ensemble, and MKL-SVM. The experimental results showed that the performance of the MKL-SVM was much better than that of the standard SVM and slightly better than the SVM ensemble. More important, the performance of the MKL-SVM was stable.