Hybrid dual Kalman filtering model for short-term traffic flow forecasting

Teng Zhou, Dazhi Jiang, Zhizhe Lin, Guoqiang Han, Xuemiao Xu, Jing Qin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

76 Citations (Scopus)


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 realworld 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.

Original languageEnglish
Pages (from-to)1023-1032
Number of pages10
JournalIET Intelligent Transport Systems
Issue number6
Publication statusPublished - 1 Jun 2019

ASJC Scopus subject areas

  • Transportation
  • General Environmental Science
  • Mechanical Engineering
  • Law


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