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
2019-06-01
10 pages
Article (Journal)
Electronic Resource
English
SVRGSA: a hybrid learning based model for short-term traffic flow forecasting
IET | 2019
|SVRGSA: a hybrid learning based model for short‐term traffic flow forecasting
Wiley | 2019
|