A generalized nonlinear model-based mixed multinomial logit approach for crash data analysis

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• 作者：Ziqiang Zeng^a ; ^b ; ^c ; Wenbo Zhu^c ; Ruimin Ke^c ; John Ash^c ; Yinhai Wang^c ; ^{yinhai@u.washington.edu} ; Jiuping Xu^b ; Xinxin Xu^a ; ^b
• 关键词：Generalized nonlinear model ; Mixed multinomial logit ; Crash frequency ; Crash severity ; Traffic safety
• 刊名：Accident Analysis & Prevention
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：99
• 期：part_PA
• 页码：51-65
• 全文大小：2224 K
• 卷排序：99

文摘

A generalized nonlinear model-based mixed multinomial logit approach is developed. The crash data in Washington State between 2011 and 2014 are collected. Thirteen contributing factors are identified to have significant effects. The new model is compared with the standard mixed multinomial logit model. The impacts of the contributing factors are analyzed.