A multivariate random-parameters Tobit model for analyzing highway crash rates by injury severity

详细信息    查看全文

• 作者：Qiang Zeng^a ; ^{zengqiang@scut.edu.cn} ; Huiying Wen^a ; ^{hywen@scut.edu.cn} ; Helai Huang^b ; ^{huanghelai@csu.edu.cn} ; Xin Pei^c ; ^{peixin@mail.tsinghua.edu.cn} ; S.C. Wong^d ; ^{hhecwsc@hku.hk}
• 关键词：Crash rate by severity ; Random parameters ; Multivariate Tobit model ; Bayesian inference
• 刊名：Accident Analysis & Prevention
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：99
• 期：part_PA
• 页码：184-191
• 全文大小：672 K
• 卷排序：99

文摘

A multivariate random-parameters Tobit model is proposed to analyze crash rate by injury severity. Crash, traffic and roadway geometric data from Hong Kong are used to demonstrate the proposed model. Heterogeneous effects on crash rate by severity are found in certain risk factors. A high and significantly positive correlation between KSI and slight injury crash rates is found. The proposed model substantially outperforms a multivariate fixed-parameters Tobit model on model fit.