Traffic crash forensic analysis based on univariate feature selection - 文章详情页

作者：

Wang, S. (Wang, S..) (学者：王术) | Chen, Y. (Chen, Y..) | Huang, J. (Huang, J..) | Ma, J. (Ma, J..) | Lu, Y. (Lu, Y..) (学者：卢岳)

收录：

Scopus

摘要：

In　China,　determining　which　party　is　liable　for　damages　or　injuries　resulting　from　a　traffic　crash　involving　both　a　motor　vehicle　and　a　cyclist　can　be　challenging.　Based　on　an　analysis　of　traffic　crash　data,　this　paper　has　proposed　a　univariate　feature　selection　method　which　can　emulate　human　thinking　and　help　determine　the　moving　status　of　the　cyclist　prior　to　the　collision.　This　research　employed　support　vector　machines　(SVM),　LDA,　and　artificial　neural　network　(ANN)　to　classify　the　moving　status　of　the　cyclists.　According　to　the　analysis　results,　the　SVM　(kernel=linear)　had　the　highest　classification　accuracy　(81.84%).　It　could　be　used　to　determine　if　the　cyclist　was　walking　the　bicycle　prior　to　the　collision.　©　ASCE.

关键词：

Forensic analysis; Support vector machines (SVM); Traffic crash; Univariate feature selection

作者机构：

[ 1 ] [Wang, S.]Beijing Key Lab of Traffic Engineering, Beijing Univ. of Technology, Beijing ST, 100124, China
[ 2 ] [Chen, Y.]Beijing Key Lab of Traffic Engineering, Beijing Univ. of Technology, Beijing ST, 100124, China
[ 3 ] [Huang, J.]Beijing Transportation Information Center, Beijing ST, 100161, China
[ 4 ] [Ma, J.]Texas Dept. of Transportation, Austin, TX 78717, United States
[ 5 ] [Lu, Y.]Beijing Key Lab of Traffic Engineering, Beijing Univ. of Technology, Beijing ST, 100124, China

通讯作者信息：

[Chen, Y.]Beijing Key Lab of Traffic Engineering, Beijing Univ. of TechnologyChina

电子邮件地址：

cdyan@bjut.edu.cn

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