Abstract
One of the common services of telematics is the car navigation that finds the shortest path from source to target. Until now, some routing algorithms of the car navigation do not consider the real-time traffic information and use the static shortest path algorithm. In this paper, we proposed the method to predict the traffic flow in the future. This prediction combines two methods. The former is an accumulated speed pattern, which means the analysis results for all past speeds of each road by classifying the same day and the same time interval. The latter is the Kalman filter. We predicted the traffic flows of each segment by combining the two methods. By experiment, we showed our algorithm gave a better precise prediction than only an accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.
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© 2006 Springer-Verlag Berlin Heidelberg
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Cho, MG., Yu, Y.J., Kim, S. (2006). The System for Predicting the Traffic Flow with the Real-Time Traffic Information. In: Gavrilova, M., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3980. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751540_98
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DOI: https://doi.org/10.1007/11751540_98
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34070-6
Online ISBN: 978-3-540-34071-3
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