Research on subway passenger flow combination prediction model based on RBF neural networks and LSSVM - 文章详情页

作者：

Wang, Pu (Wang, Pu.) | Wu, Cuixia (Wu, Cuixia.) | Gao, Xuejin (Gao, Xuejin.) (学者：高学金)

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EI Scopus

摘要：

In　view　of　the　subway　passenger　flow＇s　random　problem,　nonlinear　problem　and　so　on,　in　order　to　predict　the　subway　passenger　volume　more　accurately,　this　paper　designs　a　kind　of　parallel　variable　coefficient　weighted　combination　prediction　model　based　on　Radial　Basis　Function　Neural　Network　and　Least　Squares　Support　Vector　machines.　In　this　method,　firstly,　the　original　data　is　preprocessed.　Then,　this　paper　respectively　sets　up　RBF　Neural　Network　and　LSSVM　prediction　model　for　training　and　calculating　the　weighting　coefficient　with　the　results　of　the　training.　Finally　this　paper　separately　does　two　kinds　of　models＇　prediction,　and　weights　to　get　results.　This　article　uses　2012　passenger　flow　data　of　Beijing　DONGZHIMEN　Station　for　experiments,　which　shows　that　the　result　of　combination　prediction　model　is　more　accurate　than　the　result　of　single　prediction　model.　©　2016　IEEE.

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作者机构：

[ 1 ] [Wang, Pu]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
[ 2 ] [Wang, Pu]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
[ 3 ] [Wang, Pu]Beijing Laboratory for Urban Mass Transit, Beijing; 100124, China
[ 4 ] [Wang, Pu]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
[ 5 ] [Wu, Cuixia]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
[ 6 ] [Wu, Cuixia]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
[ 7 ] [Wu, Cuixia]Beijing Laboratory for Urban Mass Transit, Beijing; 100124, China
[ 8 ] [Wu, Cuixia]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
[ 9 ] [Gao, Xuejin]College of Electronic and Control Engineering, Beijing University of Technology, Beijing, China
[ 10 ] [Gao, Xuejin]Engineering Research Center of Digital Community, Ministry of Education, Beijing; 100124, China
[ 11 ] [Gao, Xuejin]Beijing Laboratory for Urban Mass Transit, Beijing; 100124, China
[ 12 ] [Gao, Xuejin]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China

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