As a key indicator of unsafe driving, driving volatility characterizes the variations in microscopic driving decisions. This study characterizes volatility in longitudinal and lateral driving decisions and examines the links between driving volatility in time to collision and crash-injury severity. By using a unique real-world naturalistic driving database from the 2nd Strategic Highway Research Program (SHRP), a test set of 671 crash events featuring around 0.2 million temporal samples of real-world driving are analyzed. Based on different driving performance measures, 16 different volatility indices are created. To explore the relationships between crash-injury severity outcomes and driving volatility, the volatility indices are then linked with individual crash events including information on crash severity, drivers’ pre-crash maneuvers and behaviors, secondary tasks and durations, and other factors. As driving volatility prior to crash involvement can have different components, an in-depth analysis is conducted using the aggregate as well as segmented (based on time to collision) real-world driving data. To account for the issues of observed and unobserved heterogeneity, fixed and random parameter logit models with heterogeneity in parameter means and variances are estimated. The empirical results offer important insights regarding how driving volatility in time to collision relates to crash severity outcomes. Overall, statistically significant positive correlations are found between the aggregate (as well as segmented) volatility measures and crash severity outcomes. The findings suggest that greater driving volatility (both in longitudinal and lateral direction) in time to collision increases the likelihood of police reportable or most severe crash events. Importantly, compared to the effect of volatility in longitudinal acceleration on crash outcomes, the effect of volatility in longitudinal deceleration is significantly greater in magnitude. Methodologically, the random parameter models with heterogeneity-in-means and variances significantly outperformed both the fixed parameter and random parameter counterparts (with homogeneous means and variances), underscoring the importance of accounting for both observed and unobserved heterogeneity. The relevance of the findings to the development of proactive behavioral countermeasures for drivers is discussed.

作为不安全驾驶的关键指标，驾驶波动率是微观驾驶决策变化的特征。这项研究描述了纵向和横向驾驶决策中的波动性，并研究了驾驶波动性与碰撞时间之间的关系以及碰撞伤害的严重程度。通过使用来自第二战略公路研究计划（SHRP）的独特的真实世界自然驾驶数据库，分析了671个碰撞事件的测试集，其中包含约20万个真实世界的时间采样。基于不同的驾驶性能指标，创建了16个不同的波动性指数。为了探究碰撞伤害严重程度结果与驾驶波动之间的关系，然后将波动指数与各个碰撞事件（包括碰撞严重程度信息，驾驶员的撞车前动作和行为，次要任务和持续时间以及其他因素。由于撞车之前的驾驶波动性可能具有不同的组成部分，因此使用汇总以及分段（基于碰撞时间）的实际驾驶数据进行深入分析。为了解决观察到的和未观察到的异质性的问题，估计了参数均值和方差具有异质性的固定和随机参数对数模型。经验结果提供了重要的见解，以了解碰撞时及时驾驶的波动性与碰撞严重性结果之间的关系。总体而言，在总体（以及细分的）波动率测度与崩溃严重性结果之间发现了统计学上显着的正相关。研究结果表明，及时发生碰撞时更大的驾驶波动性（纵向和横向）都增加了警方报告或最严重的撞车事件的可能性。重要的是，与纵向加速度的波动性对碰撞结果的影响相比，纵向减速的波动性的幅度要大得多。从方法上讲，具有均值异质性和方差的随机参数模型显着优于固定参数和随机参数对应物（均质均值和方差），强调了考虑观察到的和未观察到的异质性的重要性。讨论了研究结果与驾驶员主动行为对策发展的相关性。