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

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.