Open AccessHybrid dual Kalman filtering model for short‐term traffic flow forecasting
Author(s) -
Zhou Teng,
Jiang Dazhi,
Lin Zhizhe,
Han Guoqiang,
Xu Xuemiao,
Qin Jing
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5385
Subject(s) - kalman filter , traffic flow (computer networking) , computer science , term (time) , dual (grammatical number) , extended kalman filter , ensemble kalman filter , intelligent transportation system , state space representation , parametric statistics , fast kalman filter , state space , software deployment , artificial intelligence , algorithm , engineering , mathematics , art , physics , computer security , literature , statistics , civil engineering , operating system , quantum mechanics
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‐KF 2 ) for accurate and timely short‐term traffic flow forecasting. To achieve this, the H‐KF 2 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‐KF 2 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‐KF 2 outperforms the state‐of‐the‐art parametric and non‐parametric models.