With the advent of seemingly unstructured big data, and through seamless integration of computation and physical components, cyber-physical systems (CPS) provide an innovative way to enhance safety and resiliency of transport infrastructure. This study focuses on real-world microscopic driving behavior and its relevance to school zone safety – expanding the capability, usability, and safety of dynamic physical systems through data analytics. Driving behavior and school zone safety is a public health concern. The sequence of instantaneous driving decisions and its variations prior to involvement in safety critical events, defined as driving volatility, can be a leading indicator of safety. By harnessing unique naturalistic data on more than 41,000 normal, crash, and near-crash events featuring over 9.4 million temporal samples of real-world driving, a characterization of volatility in microscopic driving decisions is sought at school and non-school zone locations. A big data analytic methodology is proposed for quantifying driving volatility in microscopic real-world driving decisions. Eight different volatility measures are then linked with detailed event-specific characteristics, health history, driving history/experience, and other factors to examine crash propensity at school zones. A comprehensive yet fully flexible state-of-the-art generalized mixed logit framework is employed to fully account for distinct yet related methodological issues of scale and random heterogeneity, containing multinomial logit, random parameter logit, scaled logit, hierarchical scaled logit, and hierarchical generalized mixed logit as special cases. The results reveal that both for school and non-school locations, drivers exhibited greater intentional volatility prior to safety-critical events. Volatility in positive and negative vehicular jerk in longitudinal and lateral directions associates with increases the probability of unsafe outcomes (crashes or near-crashes) at school zones. A one-unit increase in intentional volatility measured by positive vehicular jerk in longitudinal direction associates with a 0.0528 increase in the probability of crash outcome. Importantly, the effect of negative vehicular jerk (braking) in longitudinal direction on the likelihood of crash outcome is almost double. Methodologically, Hierarchical Generalized Mixed Logit model resulted in best-fit, simultaneously accounting for scale and random heterogeneity. When accounted for separately, more parsimonious models accounting for scale heterogeneity performed comparably to the less parsimonious counterparts accounting for random heterogeneity. Importantly, even after accounting for random heterogeneity, substantial heterogeneity due to a “pure scale-effect” is still observed, underscoring the importance of scale effects in influencing the overall contours of variations in the modeled relationships. The study demonstrates the value of observational study design and big data analytics for understanding extreme driving behaviors in safe vs. unsafe driving outcomes. Implications for designing personalized school zone behavioral countermeasures are discussed.

随着看似非结构化的大数据的出现以及通过计算和物理组件的无缝集成，网络物理系统（CPS）提供了一种创新的方式来增强运输基础设施的安全性和弹性。这项研究关注于现实世界中的微观驾驶行为及其与学区安全性的关联性-通过数据分析来扩展动态物理系统的能力，可用性和安全性。驾驶行为和学区安全是公共健康问题。在涉及安全关键事件之前，瞬时驾驶决策的顺序及其变化（定义为驾驶波动）可能是安全性的主要指标。通过利用超过41,000个正常，崩溃和接近崩溃事件的独特自然数据，这些事件具有超过940万个实时驾驶的时间样本，在学校和非学校​​区域的位置都需要对微观驾驶决策中的波动性进行表征。提出了一种大数据分析方法，用于量化微观现实驾驶决策中的驾驶波动性。然后，将八种不同的波动性测度与详细的事件特定特征，健康历史，驾驶历史/经验以及其他因素相关联，以检查学区的撞车倾向。采用全面而灵活的最新通用广义logit框架来全面解决规模和随机异质性的不同但相关的方法问题，其中包括多项式logit，随机参数logit，缩放后的logit，分层缩放后的logit和分层广义混合logit作为特殊情况。结果表明，在学校和非学校​​地点，驾驶员在发生安全关键事件之前都表现出更大的故意波动性。纵向和横向方向上的正负车辆晃动都会增加在学区出现不安全结果（崩溃或接近崩溃）的可能性。在纵向方向上，由阳性车辆晃动测量的故意波动性增加一个单位，则碰撞结果的可能性增加0.0528。重要的是，纵向负加速度（制动）对碰撞结果可能性的影响几乎是两倍。从方法上讲，分层广义混合Logit模型产生了最佳拟合，同时考虑了规模和随机异质性。当单独考虑时，与考虑到随机异质性的较少次要对应的模型相比，考虑到尺度异质性的较多的简约模型的表现相近。重要的是，即使在考虑了随机异质性之后，仍会观察到由于“纯粹的比例效应”造成的实质性异质性，从而强调了比例效应在影响建模关系的整体变化轮廓方面的重要性。这项研究证明了观察性研究设计和大数据分析对于理解安全驾驶和不安全驾驶结果中的极端驾驶行为的价值。讨论了设计个性化学区行为对策的含义。即使在考虑了随机异质性之后，仍会观察到由于“纯粹的比例效应”造成的实质性异质性，从而突出了比例效应在影响建模关系的整体变化轮廓方面的重要性。这项研究证明了观察性研究设计和大数据分析对于理解安全驾驶和不安全驾驶结果中的极端驾驶行为的价值。讨论了设计个性化学区行为对策的含义。即使在考虑了随机异质性之后，仍会观察到由于“纯粹的比例效应”造成的实质性异质性，从而突出了比例效应在影响建模关系的整体变化轮廓方面的重要性。这项研究证明了观察性研究设计和大数据分析对于理解安全驾驶和不安全驾驶结果中的极端驾驶行为的价值。讨论了设计个性化学区行为对策的含义。不安全的驾驶结果。讨论了设计个性化学区行为对策的含义。不安全的驾驶结果。讨论了设计个性化学区行为对策的含义。