TY - GEN
T1 - Noise-identified kalman filter for short-term traffic flow forecasting
AU - Zhang, Shuangyi
AU - Song, Youyi
AU - Jiang, Dazhi
AU - Zhou, Teng
AU - Qin, Jing
N1 - Funding Information:
This work is supported by the NSFC (Grant No. 61902232), the Natural Science Foundation of Guangdong Province (No. 2018A030313291), the Education Science Planning Project of Guangdong Province (2018GXJK048), the grant from the Hong Kong Polytechnic University (No. YBZE), and the STU Scientific Research Foundation for Talents (NTF18006).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
KW - Active noise control
KW - Kalman filters
KW - Prediction theory
KW - Sensor networks
UR - http://www.scopus.com/inward/record.url?scp=85078273085&partnerID=8YFLogxK
U2 - 10.1109/MSN48538.2019.00093
DO - 10.1109/MSN48538.2019.00093
M3 - Conference article published in proceeding or book
AN - SCOPUS:85078273085
T3 - Proceedings - 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019
SP - 462
EP - 466
BT - Proceedings - 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019
Y2 - 11 December 2019 through 13 December 2019
ER -