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With the advent of seemingly unstructured big data, and through seamlessintegration of computation and physical components, cyber-physical systems(CPS) provide an innovative way to enhance safety and resiliency of transportinfrastructure. This study focuses on real world microscopic driving behaviorand its relevance to school zone safety expanding the capability, usability,and safety of dynamic physical systems through data analytics. Driving behaviorand school zone safety is a public health concern. The sequence ofinstantaneous driving decisions and its variations prior to involvement insafety critical events, defined as driving volatility, can be a leadingindicator of safety. By harnessing unique naturalistic data on more than 41,000normal, crash, and near-crash events featuring over 9.4 million temporalsamples of real-world driving, a characterization of volatility in microscopicdriving decisions is sought at school and non-school zone locations. A big dataanalytic methodology is proposed for quantifying driving volatility inmicroscopic real-world driving decisions. Eight different volatility measuresare then linked with detailed event specific characteristics, health history,driving history, experience, and other factors to examine crash propensity atschool zones. A comprehensive yet fully flexible state-of-the-art generalizedmixed logit framework is employed to fully account for distinct yet relatedmethodological issues of scale and random heterogeneity, containing multinomiallogit, random parameter logit, scaled logit, hierarchical scaled logit, andhierarchical generalized mixed logit as special cases. The results reveal thatboth for school and non-school locations, drivers exhibited greater intentionalvolatility prior to safety-critical events... ...

transportation research part c emerging technologies

Harnessing Ambient Sensing & Naturalistic Driving Systems to Understand  Links Between Driving Volatility and Crash Propensity in School Zones: A  generalized hierarchical mixed logit framework