Construct support vector machine ensemble to detect traffic incident

This study presents the applicability of support vector machine (SVM) ensemble for traffic incident detection. The SVM has been proposed to solve the problem of traffic incident detection, because it is adapted to produce a nonlinear classifier with maximum generality, and it has exhibited good performance as neural networks. However, the classification result of the practically implemented SVM depends on the choosing of kernel function and parameters. To avoid the burden of choosing kernel functions and tuning the parameters, furthermore, to improve the limited classification performance of the real SVM, and enhance the detection performance, we propose to use the SVM ensembles to detect incident. In addition, we also propose a new aggregation method to combine SVM classifiers based on certainty. Moreover, we proposed a reasonable hybrid performance index (PI) to evaluate the performance of SVM ensemble for detecting incident by combining the common criteria, detection rate (DR), false alarm rate (FAR), mean time to detection (MTTD), and classification rate (CR). Several SVM ensembles have been developed based on bagging, boosting and cross-validation committees with different combining approaches, and the SVM ensemble has been tested on one real data collected at the I-880 Freeway in California. The experimental results show that the SVM ensembles outperform a single SVM based AID in terms of DR, FAR, MTTD, CR and PI. We used one non-parametric test, the Wilcoxon signed ranks test, to make a comparison among six combining schemes. Our proposed combining method performs as well as majority vote and weighted vote. Finally, we also investigated the influence of the size of ensemble on detection performance.