Pedestrians can be categorized as the most vulnerable road users since they have less protection compared to other road users, which makes their safety of utmost importance for transportation agencies and safety researchers. To improve the safety of pedestrians and to reduce the associated costs, it is an important task to identify the factors that affect pedestrian injury severities in pedestrian-invovled crashes. Several studies have been conducted in this field, but quantitative studies have not examined the temporal stability and transferability of the variables influencing pedestrian injury severity over the years. In this research, using Los Angeles crash data from 2012 to 2017, a random parameters logit model was employed to determine the variables that significantly affect the degree of pedestrian injury and to investigate their stability over time. Moreover, to consider different layers of unobserved heterogeneity and to obtain better statistical fit, the distributions of random parameters are allowed to vary across the observations. Pedestrian injury severity levels are divided into severe, minor, no injuries. Two types of likelihood ratio tests were used to test the transferability of the estimated models over the seven years. The results obtained from the model estimation and likelihood ratio tests revealed that variables affecting the pedestrian injury severity over these years have changed significantly and are not stable. The instability of the variables affecting the pedestrian injury severities shows that it is a necessity to dynamically analyze the crash data and to consider the potential variations over different time periods.

行人可以被归类为最脆弱的道路使用者，因为与其他道路使用者相比，他们受到的保护较少，这使得他们的安全对交通机构和安全研究人员来说至关重要。为了提高行人的安全并降低相关成本，确定影响行人相关碰撞中行人伤害严重程度的因素是一项重要任务。在这个领域已经进行了几项研究，但定量研究没有检查多年来影响行人伤害严重程度的变量的时间稳定性和可转移性。在这项研究中，使用洛杉矶 2012 年至 2017 年的车祸数据，使用随机参数 logit 模型来确定显着影响行人伤害程度的变量，并研究它们随时间的稳定性。此外，为了考虑未观察到的异质性的不同层并获得更好的统计拟合，允许随机参数的分布随观察而变化。行人受伤的严重程度分为重伤、轻伤、无伤。使用两种似然比检验来检验估计模型在七年中的可转移性。从模型估计和似然比测试获得的结果表明，这些年来影响行人伤害严重程度的变量发生了显着变化并且不稳定。