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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Qin, P., Xu, Z., Yang, W., Liu, G. (2018). Real-Time Road Traffic State Prediction Based on SVM and Kalman Filter. In: Li, J., et al. Wireless Sensor Networks. CWSN 2017. Communications in Computer and Information Science, vol 812. Springer, Singapore. https://doi.org/10.1007/978-981-10-8123-1_23
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DOI: https://doi.org/10.1007/978-981-10-8123-1_23
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