This study intended to investigate the crash injury severity from the insights of the novice and experienced drivers. To achieve this objective, a bivariate panel data probit model was initially proposed to account for the correlation between both time-specific and individual-specific error terms. The geocrash data of Las Vegas metropolitan area from 2014 to 2017 were collected. In order to estimate two (seemingly unrelated) nonlinear processes and to control for interrelations between the unobservables, the bivariate random-effects probit model was built up, in which injury severity levels of novice and experienced drivers were addressed by bivariate (seemingly unrelated) probit simultaneously, and the interrelations between the unobservables (i.e., heterogeneity issue) were accommodated by bivariate random-effects model. Results revealed that crash types, vehicle types of minor responsibility, pedestrians, and motorcyclists were potentially significant factors of injury severity for novice drivers, while crash types, driver condition of minor responsibility, first harm, and highway factor were significant for experienced drivers. The findings provide useful insights for practitioners to improve traffic safety levels of novice and experienced drivers.

中文翻译：

---

新手和有经验的驾驶员对碰撞伤害严重性控制的见解：双变量随机效应概率分析

这项研究旨在从新手和经验丰富的驾驶员的角度调查碰撞伤害的严重程度。为了实现这一目标，最初提出了一个双变量面板数据概率模型来说明特定时间误差项和特定于个体误差项之间的相关性。收集了2014年至2017年拉斯维加斯都会区的地质灾害数据。为了估计两个（看似无关）的非线性过程并控制不可观测对象之间的相互关系，建立了双变量随机效应概率模型，在该模型中，新手和经验丰富的驾驶员的伤害严重程度等级通过双变量（看似无关）解决同时，不可变量之间的相互关系（即异质性问题）由双变量随机效应模型解决。结果显示，撞车类型，次要责任车辆类型，行人和摩托车手是新手伤害严重程度的潜在重要因素，而撞车类型，次要责任驾驶员状况，首次伤害和高速公路因素对于经验丰富的驾驶员而言很重要。研究结果为从业人员提高新手和有经验的驾驶员的交通安全水平提供了有用的见解。