As one crucial mobility problem for elderly drivers, vehicle crashes due to elderly drivers account for an increased ratio of total vehicle crashes in recent years in Japan. Mandating a driving lesson for elderly drivers was implemented and revised continuously to reduce the number of vehicle crashes. To perform a practical driving lesson for elderly drivers, it is essential to evaluate whether it can reduce vehicle crashes significantly or not. Most previous studies only investigated its effects on fatal or severe rates using rather simple methods, without consideration for the increasing number of elderly drivers and the variability of vehicle crashes in different months. To bridge these research gaps, this study examines the impact of mandating a driving lesson for elderly drivers by some advanced statistical methods based on a monthly level. Vehicle crash records from April 2005 to December 2019 collected in Toyota City, Japan are used for empirical analysis. Three types of count data models, i.e., the Poisson regression model, the Negative Binomial regression model, and the Poisson Integer-Valued Autoregressive (INAR) (1) model are proposed in this study. A comparison of three proposed models was implemented to indicate the similarity and distinction of estimation results. The significant findings of this study suggest that: 1) three proposed models have the same prediction accuracy referring to indexes of Root Mean Squared Error, Mean Absolute Error, and Mean Squared Error; 2) all of them indicate that the revision of license renewal legislation for elderly drivers in March 2017 is a significant factor negatively affecting the number of vehicle crashes at a 10 % significance level; 3) all of them indicate that the number of drivers aged 65 years or older and month-variability are significant factors affecting the number of vehicle crashes at least a 10 % significance level; 4) the time-series nature of vehicle crashes due to elderly drivers was not existing indicated by the result of the Poisson INAR (1) model. Statistical methods proposed in this study can be referred by researchers and engineers to evaluate the effects of traffic safety measures in the research field of traffic and transportation engineering.

作为老年人驾驶者的一个关键的机动性问题，近年来，由于老年人驾驶而导致的汽车事故占日本总汽车事故的比例增加。实施并为老年驾驶员规定了驾驶课程，并不断对其进行修订，以减少车辆撞车的次数。要为老年驾驶员提供实用的驾驶课程，必须评估它是否可以显着减少车辆碰撞事故。以前的大多数研究仅使用相当简单的方法就其对致命或严重事故率的影响进行了调查，而没有考虑到老年人驾驶员数量的增加以及在不同月份中发生的交通事故的可变性。为了弥合这些研究差距，本研究通过一些基于月度水平的高级统计方法，研究了强制驾驶课程对老年驾驶员的影响。2005年4月至2019年12月在日本丰田市收集的车辆碰撞记录用于实证分析。本文提出了三种类型的计数数据模型，即泊松回归模型，负二项式回归模型和泊松整数值自回归（INAR）（1）模型。比较了三个提出的模型，以表明估计结果的相似性和区别。这项研究的重要发现表明：1）三种模型在参考均方根误差，均方根绝对误差和均方根误差的指标方面具有相同的预测精度；2）所有这些都表明，2017年3月修订的老年驾驶员执照更新法规是一个重要因素，对车祸数量产生了负面影响，显着性水平为10％; 3）所有这些都表明年龄在65岁以上的驾驶员数量和月变化是影响车辆碰撞次数的重要因素，其显着性水平至少为10％；4）Poisson INAR（1）模型的结果表明，不存在因年长驾驶员而导致的汽车碰撞的时间序列性质。研究人员和工程师可以参考本研究提出的统计方法，以评估交通安全措施在交通运输工程研究领域中的作用。4）Poisson INAR（1）模型的结果表明，不存在因年长驾驶员而导致的汽车碰撞的时间序列性质。研究人员和工程师可以参考本研究提出的统计方法，以评估交通安全措施在交通运输工程研究领域中的作用。4）Poisson INAR（1）模型的结果表明，不存在因年长驾驶员而导致的汽车碰撞的时间序列性质。研究人员和工程师可以参考本研究提出的统计方法，以评估交通安全措施在交通运输工程研究领域中的作用。