期刊
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
卷 536, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.physa.2019.122601
关键词
Intelligent transportation systems; Time series analysis; Traffic flow forecasting; Kalman filter
资金
- NSFC, China [61902232]
- Natural Science Foundation of Guangdong Province, China [2018A030313291, 2018A030313889]
- Education Science Planning Project of Guangdong Province, China [2018GXJK048]
- STU Scientific Research Foundation for Talents [NTF18006]
- Science and Technology Planning Project of Guangdong Province, China [2016B010124012, 2019B010116001]
- Hong Kong Polytechnic University, China [1ZE8J]
This paper formulates the traffic flow forecasting task by introducing a maximum correntropy deduced Kalman filter. The traditional Kalman filter is based on minimum mean square error, which performs well under Gaussian noises. However, the real traffic flow data are fulfilled with non-Gaussian noises. The traditional Kalman filter may rot under this situation. The Kalman filter deduced by maximum correntropy criteria is insensitive to non-Gaussian noises, meanwhile retains the optimal state mean and covariance propagation of the traditional Kalman filter. To achieve this, a fix-point algorithm is embedded to update the posterior estimations of maximum correntropy deduced Kalman filter. Extensive experiments on four benchmark datasets demonstrate the outperformance of this model for traffic flow forecasting. (C) 2019 Elsevier B.V. All rights reserved.
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