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- W2912117247 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." @default.
- W2912117247 created "2019-02-21" @default.
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- W2912117247 date "2018-01-01" @default.
- W2912117247 modified "2023-09-23" @default.
- W2912117247 title "Real-Time Road Traffic State Prediction Based on SVM and Kalman Filter" @default.
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- W2912117247 doi "https://doi.org/10.1007/978-981-10-8123-1_23" @default.
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