  As a key indicator of unsafe driving, driving volatility characterizes the
variations in microscopic driving decisions. This study characterizes
volatility in longitudinal and lateral driving decisions and examines the links
between driving volatility in time to collision and crash injury severity. By
using a unique real-world naturalistic driving database from the 2nd Strategic
Highway Research Program (SHRP), a test set of 671 crash events featuring
around 0.2 million temporal samples of real world driving are analyzed. Based
on different driving performance measures, 16 different volatility indices are
created. To explore the relationships between crash-injury severity outcomes
and driving volatility, the volatility indices are then linked with individual
crash events including information on crash severity, drivers' pre crash
maneuvers and behaviors, secondary tasks and durations, and other factors. As
driving volatility prior to crash involvement can have different components, an
indepth analysis is conducted using the aggregate as well as segmented (based
on time to collision) real world driving data. To account for the issues of
observed and unobserved heterogeneity, fixed and random parameter logit models
with heterogeneity in parameter means and variances are estimated. The
empirical results offer important insights regarding how driving volatility in
time to collision relates to crash severity outcomes. Overall, statistically
significant positive correlations are found between the aggregate (as well as
segmented) volatility measures and crash severity outcomes. The findings
suggest that greater driving volatility (both in longitudinal and lateral
direction) in time to collision increases the likelihood of police reportable
or most severe crash events... ...
