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Probabilistic Forecasting of Traffic Flow Using Multikernel Based Extreme Learning Machine
Joint Authors
Ban, Xiaojuan
Xing, Yiming
Guo, Chong
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-03-16
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Real-time and accurate prediction of traffic flow is the key to intelligent transportation systems (ITS).
However, due to the nonstationarity of traffic flow data, traditional point forecasting can hardly be accurate, so probabilistic forecasting methods are essential for quantification of the potential risks and uncertainties for traffic management.
A probabilistic forecasting model of traffic flow based on a multikernel extreme learning machine (MKELM) is proposed.
Moreover, the optimal output weights of MKELM are obtained by utilizing Quantum-behaved particle swarm optimization (QPSO) algorithm.
To verify its effectiveness, traffic flow probabilistic prediction using QPSO-MKELM was compared with other learning methods.
Experimental results show that QPSO-MKELM is more effective for practical applications.
And it will help traffic managers to make right decisions.
American Psychological Association (APA)
Xing, Yiming& Ban, Xiaojuan& Guo, Chong. 2017. Probabilistic Forecasting of Traffic Flow Using Multikernel Based Extreme Learning Machine. Scientific Programming،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1203319
Modern Language Association (MLA)
Xing, Yiming…[et al.]. Probabilistic Forecasting of Traffic Flow Using Multikernel Based Extreme Learning Machine. Scientific Programming No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1203319
American Medical Association (AMA)
Xing, Yiming& Ban, Xiaojuan& Guo, Chong. Probabilistic Forecasting of Traffic Flow Using Multikernel Based Extreme Learning Machine. Scientific Programming. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1203319
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1203319