Abstract
Road traffic prediction offers traffic guidance for travelers and relieves traffic jams with effective information. In this paper, a real-time road traffic state prediction based on support vector machine (SVM) and the Kalman filter is proposed. In the proposed model, the well-trained SVM model predicts the baseline travel times from the historical trips data; the Kalman filtering-based dynamic algorithm can adjust travel times by using the latest travel information and the estimated values based on SVM. Experimental results show that the real-time road traffic state prediction based on SVM and the Kalman filter is feasible and can achieve high accuracy.
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