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• W2910982572 abstract "Short-term traffic flow forecasting is a fundamental and challenging task since it is required for the successful deployment of intelligent transportation systems and the traffic flow is dramatically changing through time. This study presents a novel hybrid dual Kalman filter (H-KF2) for accurate and timely short-term traffic flow forecasting. To achieve this, the H-KF2 first models the propagation of the discrepancy between the predictions of the traditional Kalman filter and the random walk model. By estimating the a posteriori state of the prediction errors of both models, the calibrated discrepancy is exploited to compensate the preliminary predictions. The H-KF2 works with competitive time and space to traditional Kalman filter. Four real-world datasets and various experiments are employed to evaluate the authors’ model. The experimental results demonstrate the H-KF2 outperforms the state-of-the-art parametric and non-parametric models." @default.
• W2910982572 created "2019-01-25" @default.
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• W2910982572 date "2019-02-12" @default.
• W2910982572 modified "2023-10-15" @default.
• W2910982572 title "Hybrid dual Kalman filtering model for short‐term traffic flow forecasting" @default.
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• W2910982572 doi "https://doi.org/10.1049/iet-its.2018.5385" @default.
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