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.
Hybrid dual Kalman filtering model for short-term traffic flow forecasting
IET Intelligent Transport Systems ; 13 , 6 ; 1023-1032
2019-02-12
10 pages
Article (Journal)
Electronic Resource
English
Metadata by IET is licensed under CC BY 3.0
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