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The System for Predicting the Traffic Flow with the Real-Time Traffic Information
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- Authors
- Mi-Gyung Cho, Young Jung Yu, Sung Soo Kim
- Issue Date
- 2006-05
- Citation
- International Conference on Computational Science and Its Applications (ICCSA) 2006 (LNCS 3980), v.3980, pp.904-913
- Publisher
- Springer
- Language
- English
- Type
- Conference Paper
- 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. © Springer-Verlag Berlin Heidelberg 2006.
- KSP Keywords
- Car navigation, Dynamic Shortest Path, Real-time traffic information, Routing algorithm, Time interval, Traffic congestion, Traffic flow, Travel information, kalman filter, shortest path algorithm