Abstract
This paper presents a new classification algorithm on traffic state of expressway which integrates the ensemble learning and fuzzy system, which consists of two fuzzy classifiers and a speed-based classifier. The fuzzy rules of two fuzzy classifiers are developed based on expert knowledge and how to optimize the parameters in fuzzy classifiers is given. While the outputs of individual classifier are inconsistent with one another, the output of the ensemble fuzzy classifier is deduced based on probability theory. Then this new ensemble fuzzy classifier was employed to identify the traffic state of a road link in Beijing urban freeway using the field traffic flow data and human judgment for the traffic status. The experimental results demonstrated that the accuracy of this algorithm went up greatly compared with the existing speed-based algorithm, and the robustness of the ensemble algorithm were better compared with any single classifier.
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