Journal
IET INTELLIGENT TRANSPORT SYSTEMS
Volume 13, Issue 6, Pages 1023-1032Publisher
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-its.2018.5385
Keywords
forecasting theory; Kalman filters; road traffic; short-term traffic flow forecasting; traditional Kalman filter; hybrid dual Kalman filtering model; intelligent transportation systems
Funding
- Natural Science Foundation of Guangdong Province [2018A030313291]
- STU Scientific Research Foundation for Talents [NTF18006]
- Science and Technology Planning Project of Guangdong Province [2016B010124012]
- Natural Science Foundation of China [61772206, U1611461, 61472145]
- Special Fund of Science and Technology Research and Development on Application From Guangdong Province [2016B010124011]
- Guangdong High-level personnel of special support program [2016TQ03X319]
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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.
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