This study explores the temporal instability of factors affecting motorcyclist-injury severities in single-vehicle motorcycle crashes in Florida. Two data sources are used; one covers the 2012 to 2016 crash histories of Florida motorcyclists who were newly licensed in 2012, and the second covers motorcycle crashes that occur on horizontal curves in Florida from 2005 to 2015. In the first dataset (2012 new riders), temporal changes may result from riders gaining experience as well as general temporal shifts. In the second dataset, rider experience is unknown (thus becoming a source of potential unobserved heterogeneity) but the temporal changes will be largely from general temporal shifts. With three possible motorcyclist injury severity outcomes (no visible injury, minor injury, and severe injury), random parameters multinomial logit models, that allow for heterogeneity in means and variances, were estimated for all possible annual time periods in each dataset. Likelihood ratio tests were conducted to examine the overall stability of model estimates across time periods, and marginal effects of each explanatory variable were also considered to investigate the temporal instability of the effect of individual parameter estimates on motorcyclist injury-severity probabilities. A wide range of variables was considered including motorcyclists’ attributes (such as ethnicity and age), roadway and environmental conditions (such as light and road surface conditions), motorcycle characteristics (such as motorcycle make and type of motorcycle), rider actions (such as speeding and improper driving actions), and roadway conditions (such as obstacles on the road and speed limits). The results show significant temporal instability in motorcyclist-injury severity models, which likely result from changes in motorcycle technology and performance, changes in macroeconomic conditions, changes induced by how riders respond to the changing behavior of other road users (whose behavior may be changing as a result of technology changes in their vehicles, evolving use of personal technologies in their vehicle, such as cell phones, etc.), and the changes in riders’ behavior and skills over time.

这项研究探讨了在佛罗里达州单车摩托车碰撞中影响摩托车骑士严重程度的因素在时间上的不稳定性。使用了两个数据源；第一部分涵盖了2012年新获得许可的佛罗里达州摩托车手的2012年至2016年的碰撞历史，第二部分涵盖了2005年至2015年在佛罗里达州的水平曲线上发生的摩托车碰撞。在第一个数据集中（2012年新骑手），可能会导致时间变化经验丰富的骑手以及一般的时间变化。在第二个数据集中，骑手的经历是未知的（因此成为潜在的未观察到的异质性的来源），但时间变化将主要来自一般的时间变化。利用三种可能的摩托车骑士伤害严重程度结果（无可见伤害，轻度伤害和重度伤害），随机参数多项式logit模型，对于每个数据集中所有可能的年度时间段，均值和方差均允许异质性进行估计。进行了似然比检验，以检验整个时间段内模型估计的总体稳定性，并且还考虑了每个解释变量的边际效应，以调查各个参数估计对摩托车手受伤严重性概率的时间不稳定性。考虑了广泛的变量，包括摩托车手的属性（例如种族和年龄），道路和环境条件（例如光照和路面状况），摩托车特性（例如摩托车品牌和摩托车类型），骑手行为（例如例如超速驾驶和不当驾驶行为）以及道路状况（例如道路上的障碍物和速度限制）。