A New Lane-Changing Model with Consideration of Driving Style - 文章详情页

作者：

Ren, Guoqing (Ren, Guoqing.) | Zhang, Yong (Zhang, Yong.) (学者：张勇) | Liu, Hao (Liu, Hao.) | Zhang, Ke (Zhang, Ke.) | Hu, Yongli (Hu, Yongli.) (学者：胡永利)

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EI Scopus

摘要：

The　lane-changing　model　is　a　hot　spot　in　the　field　of　traffic　research,　and　there　are　already　a　lot　of　free　lane-changing　model　established　mathematical　statistical　methods　or　machine　learning　algorithm.　However,　these　models　don’t　consider　the　driver’s　driving　style　to　the　free　lane-changing,　and　the　accuracy　of　these　models　is　low.　This　paper　considers　the　driver’s　driving　style　and　proposes　a　new　free　lane-changing　model　based　on　machine　learning.　The　new　model　splits　the　sample　data　into　three　driving　styles:　cautious,　stable　and　radical.　This　paper　selects　the　most　effective　multilayer　perceptron　model　by　comparing　different　machine　learning　methods　based　on　the　NGSIM　trajectory　data.　In　the　analysis　of　the　final　accuracy　of　this　paper,　it　can　be　seen　that　the　new　model　has　a　great　improvement　in　accuracy.　©　2019,　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.

关键词：

K-means clustering Learning algorithms Multilayers Multilayer neural networks Learning systems Machine learning

作者机构：

[ 1 ] [Ren, Guoqing]Beijing University of Technology, Beijing, China
[ 2 ] [Zhang, Yong]Beijing University of Technology, Beijing, China
[ 3 ] [Zhang, Yong]Beijing Transportation Information Center, Beijing, China
[ 4 ] [Liu, Hao]Beijing Transportation Information Center, Beijing, China
[ 5 ] [Zhang, Ke]Beijing Transportation Operations Coordination Center, Beijing, China
[ 6 ] [Hu, Yongli]Beijing University of Technology, Beijing, China

通讯作者信息：

张勇
[zhang, yong]beijing university of technology, beijing, china;;[zhang, yong]beijing transportation information center, beijing, china

电子邮件地址：

zhangyong2010@bjut.edu.cn

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