The Pennsylvania State University, 119 Health and Human Development Building, University Park, PA, 16802, United States.
Clemson University, 418 Brackett Hall, Clemson, SC, 29634, United States.
Accid Anal Prev. 2021 Mar;152:105986. doi: 10.1016/j.aap.2021.105986. Epub 2021 Jan 28.
Current methods of analyzing data from naturalistic driving studies provide important insights into real-world safety-related driving behaviors, but are limited in the depth of information they currently offer. Driving measures are frequently collapsed to summary levels across the study period, excluding more fine-grained differences such as changes that occur from trip to trip. By retaining trip-specific data, it is possible to quantify how much a driver differs from trip to trip (within-person variability) in addition to how he or she differs from other drivers (between-person variability). To the authors' knowledge, the current study is the first to use multilevel modeling to quantify variability in distracted driving behavior in a naturalistic dataset of older drivers. The current study demonstrates the utility of examining within-person variability in a naturalistic driving dataset of 68 older drivers across two weeks. First, multilevel models were conducted for three distracted driving behaviors to distinguish within-person variability from between-person variability in these behaviors. A high percentage of variation in distracted driving behaviors was attributable to within-person differences, indicating that drivers' behaviors varied more across their own driving trips than from other drivers (ICCs = .93). Then, to demonstrate the utility of personal characteristics in predicting daily driving behavior, a hypothetical model is presented using simulated daily sleep duration from the previous night to predict distracted driving behavior the following day. The current study demonstrates substantial variability in driving behaviors within an older adult sample and the promise of individual characteristics to provide better prediction of driving behaviors relevant to safety, which can be applied in investigations of current naturalistic driving datasets and in designing future studies.
目前分析自然驾驶研究数据的方法为深入了解与安全相关的实际驾驶行为提供了重要的见解，但这些方法在提供信息的深度上存在局限性。在研究期间，驾驶措施通常会被汇总到摘要水平，排除了更细微的差异，例如从一次行程到另一次行程的变化。通过保留特定行程的数据，可以量化驾驶员在每次行程中的差异（个体内变异性），以及他或她与其他驾驶员的差异（个体间变异性）。据作者所知，目前的研究首次使用多层次模型来量化自然驾驶数据集中年长驾驶员分心驾驶行为的变异性。目前的研究在两周内对 68 名年长驾驶员的自然驾驶数据集进行了个体内变异性分析，证明了其在自然驾驶数据集分析中的有效性。首先，对三种分心驾驶行为进行了多层次模型分析，以区分这些行为中的个体内变异性和个体间变异性。分心驾驶行为的很大一部分变化归因于个体内差异，这表明驾驶员的行为在自己的驾驶行程中变化更大，而不是与其他驾驶员的行为变化更大（ICC =.93）。然后，为了展示个人特征在预测日常驾驶行为方面的有效性，使用前一天晚上的模拟每日睡眠时间来预测第二天的分心驾驶行为，提出了一个假设模型。目前的研究表明，在年长成年样本中存在大量的驾驶行为变异性，并且个人特征有希望提供更好的安全相关驾驶行为预测，这可以应用于当前自然驾驶数据集的研究和未来研究的设计。
