论文分享 - 1 Random paramters approach with heterogeneity in means and variances

Miao Yu, Jinxing Shen, Changxi Ma. Factors Affecting Driver Injury Severity in the Wrong-Way Crash: Accounting for Potential Heterogeneity in Means and Variances of Random Parameters. Transportation Research Record, 2021, 1- 10. https://doi.org/10.1177/03611981211009882

摘要： 

Because of the high percentage of fatalities and severe injuries in wrong-way driving (WWD) crashes, numerous studies have focused on identifying contributing factors to the occurrence of WWD crashes. However, a limited number of research effort has investigated the factors associated with driver injury-severity in WWD crashes. This study intends to bridge the gap using a random parameter logit model with heterogeneity in means and variances approach that can account for the unobserved heterogeneity in the data set. Police-reported crash data collected from 2014 to 2017 in North Carolina are used. Four injury-severity levels are defined: fatal injury, severe injury, possible injury, and no injury. Explanatory variables, including driver characteristics, roadway characteristics, environmental characteristics, and crash characteristics, are used. Estimation results demonstrate that factors, including the involvement of alcohol, rural area, principal arterial, high speed limit (>60 mph), dark-lighted conditions, run-off-road collision, and head-on collision, significantly increase the severity levels in WWD crashes. Several policy implications are designed and recommended based on findings.

 

论文应用随机参数logit模型分析了Wrong-way事故中，影响司机受伤严重程度的因素。文章进一步探究了哪些观测变量能够影响模型中随机变量的均值和方差。