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

TL;DR

This study uses big data and advanced statistical modeling to analyze driving volatility from naturalistic data, linking it to crash risk in school zones and providing insights for safety improvements.

Contribution

It introduces a novel big data analytic framework with a hierarchical mixed logit model to quantify driving volatility and its relation to crash propensity in school zones.

Findings

Greater driving volatility observed before safety-critical events

Identified key factors influencing crash risk in school zones

Demonstrated effectiveness of hierarchical mixed logit in modeling driving behavior

Abstract

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... ...