Noise-Identified Kalman Filter for Short-Term Traffic Flow Forecasting

In this paper, we present a novel and effective technique for short-term traffic flow forecasting. Our main contribution is an extension of Kalman filter, such that it becomes to be able to identify the noise and then filter out it; we hence named the present technique as noise-identified Kalman filter. Our epistemological perspective is that the classic Kalman filter filters out not only the noise but also useful signals. We hence develop the Kalman filter for de-noising while preserving the useful signals by devising a cost function. By conducting extensive experiments on four benchmark data sets, the proposed technique is firmly verified to be effective for short-term traffic flow forecasting, outperforming not only the classic Kalman filter but also other frequently-used parametric and non-parametric techniques.