Two-vehicle driver-injury severity: A multivariate random parameters logit approach

IF 12.5 1区 工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Hongren Gong , Ting Fu , Yiren Sun , Zhongyin Guo , Lin Cong , Wei Hu , Ziwen Ling
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引用次数: 6

Abstract

Two-vehicle crashes have been dominating all types of traffic accidents, wherein the vehicle drivers have been sustaining the highest risk of injury among all vehicle occupants. To understand the critical factors to the drivers’ injury severity of two-vehicle crashes, we employed the random parameters multinomial logit model as a data analyzing tool. To capture the unobserved heterogeneity and potential temporal instability, we combined two strategies: Bayesian random parameter logit and explicitly correlated outcomes. The random parameter logit models were validated with a nine-year large-scale dataset compiled by combining the Crash Report Sampling System (CRSS) and General Estimates Sampling (GES) databases. The results underscore the importance of explicit modeling of inter-outcome correlation, which captured the potential transition probability between adjacent levels of injury severity and improved the model’s predictability. Our model also highlighted substantial per-case and per-driver heterogeneity, which respectively explained 22.8% and 29.4% of the total variance (minor injury) and 25.4% and 24.9% of the variance (severe injury). We found that the female drivers, old (65 years) drivers, unbuckled drivers, speeding drivers sustained a higher injury risk in their corresponding groups. Drivers in lighter and older vehicles suffer higher injury risks. Several other factors also considerably affect the injury severity outcomes, such as the road’s speed limit and variables that are proxies of traffic volume (intersection type, whether at the peak hours). Regarding Bayesian modeling, we observed that using weakly informative prior distribution has little effect on the parameter estimates. We also pointed to the directions to further improve the proposed modeling framework.

双车驾驶员损伤严重程度:多变量随机参数logit方法
两车碰撞在所有类型的交通事故中占主导地位,其中车辆驾驶员在所有车辆乘员中受伤的风险最高。为了了解影响双车碰撞驾驶员损伤严重程度的关键因素,采用随机参数多项logit模型作为数据分析工具。为了捕捉未观察到的异质性和潜在的时间不稳定性,我们结合了两种策略:贝叶斯随机参数logit和明确相关的结果。随机参数logit模型通过结合事故报告抽样系统(CRSS)和一般估计抽样(GES)数据库编制的9年大规模数据集进行验证。结果强调了结果间相关性的显式建模的重要性,它捕获了相邻损伤严重程度之间的潜在转移概率,并提高了模型的可预测性。我们的模型还强调了大量的个案和驾驶员异质性,分别解释了22.8%和29.4%的总方差(轻微伤害)和25.4%和24.9%的方差(严重伤害)。我们发现女性司机,年龄大于或等于65岁的司机,没有系安全带的司机,超速驾驶的司机在他们相应的群体中有更高的受伤风险。驾驶较轻和较旧车辆的司机受伤的风险更高。其他几个因素也会显著影响伤害严重程度的结果,例如道路的速度限制和交通流量的变量(十字路口类型,是否在高峰时间)。对于贝叶斯模型,我们观察到使用弱信息先验分布对参数估计的影响很小。我们还指出了进一步改进所建议的建模框架的方向。
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来源期刊
CiteScore
22.10
自引率
34.10%
发文量
35
审稿时长
24 days
期刊介绍: Analytic Methods in Accident Research is a journal that publishes articles related to the development and application of advanced statistical and econometric methods in studying vehicle crashes and other accidents. The journal aims to demonstrate how these innovative approaches can provide new insights into the factors influencing the occurrence and severity of accidents, thereby offering guidance for implementing appropriate preventive measures. While the journal primarily focuses on the analytic approach, it also accepts articles covering various aspects of transportation safety (such as road, pedestrian, air, rail, and water safety), construction safety, and other areas where human behavior, machine failures, or system failures lead to property damage or bodily harm.
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