SVM and KNN ensemble learning for traffic incident detection

Traffic incident detection is a very important research area of intelligent transportation systems. Many methods have obtained good performance in traffic incident detection. However, the robustness of these methods is not satisfactory. Namely, when one method is applied on another data set again, its performance is not always good, even it had obtained good performance on one data set once. In this paper, we propose an ensemble learning method to improve the robustness in traffic incident detection. The proposed method trains individual SVM and KNN models firstly. And then, it takes a strategy to combine them for better final output. Experimental results show that the propose method has achieved the best performance among all the compared methods. Also, the ensemble learning strategy in the proposed method has improved the robustness of the individual models. (C) 2018 Elsevier B.V. All rights reserved.