论文标题
利用环境传感和自然主义驾驶系统了解驾驶波动和学校区域崩溃倾向之间的联系：广义的层次混合logit框架
Harnessing Ambient Sensing & Naturalistic Driving Systems to Understand Links Between Driving Volatility and Crash Propensity in School Zones: A generalized hierarchical mixed logit framework
论文作者
论文摘要
随着看似非结构化的大数据的出现，通过计算和物理组件的无缝集成，网络物理系统（CPS）为提高运输基础设施的安全性和弹性提供了一种创新的方式。这项研究的重点是现实世界中的微观驾驶行为及其与学校区域安全性的相关性，从而通过数据分析扩大了动态物理系统的能力，可用性和安全性。驾驶行为和学校区安全是公共卫生的问题。瞬时驾驶决策的顺序及其在参与安全关键事件之前的变化（定义为驱动波动率）可能是安全的主要指标。通过利用独特的自然主义数据，以超过41,000个正常，崩溃和近磨碎的事件，这些事件的现实世界驾驶方式超过940万个时间样本，在学校和非学校区域中寻求了微观驾驶决策中波动性的特征。提出了一种大数据分析方法，用于量化微观现实世界中驾驶决策中的驱动波动。然后将八种不同的波动率措施与详细的事件特定特征，健康历史，驱动历史，经验和其他因素有关，以检查学校区域的崩溃倾向。使用全面但完全灵活的最先进的广义混合logit框架来充分说明尺度和随机异质性的不同方法学问题，其中包含多项式logit，随机参数logit，缩放logit，层次级别标度logit，层次缩放logit和层次结构的层次概括。结果表明，在学校和非学校地点，驾驶员在安全至关重要事件之前表现出更大的故意波动... ...
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... ...
