  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... ...
