TITLE:
Correlating Connected Vehicle Estimated Deceleration Events with Crash Incidents near Interstate Interchanges
AUTHORS:
Jairaj Desai, Jijo K. Mathew, Howell Li, Justin Mukai, Rahul Suryakant Sakhare, Darcy M. Bullock
KEYWORDS:
Connected Vehicles, Hard-Braking, Crashes, Safety, Deceleration
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.13 No.4,
September
28,
2023
ABSTRACT: Historical
roadway safety analyses have used labor and time-intensive crash data
collection procedures. However, crash reporting is often delayed and crash
locations are reported with varying levels of spatial accuracy and detail.
Recent advances in connected vehicle (CV) data provide an opportunity for
stakeholders to proactively identify areas of safety concerns in near-real time
with high spatial precision. Public and private sector stakeholders including
automotive original equipment manufacturers (OEM) and insurance providers may
independently define acceleration thresholds for reporting unsafe driver
behavior. Although some OEMs have provided fixed threshold hard-braking event data for a number of
years, this varies by OEM and there is no published literature on the best
thresholds to use for identifying emerging safety issues. This research
proposes a methodology to estimate deceleration events from raw CV trajectory
data at varying thresholds that can be scaled to any CV. The estimated
deceleration events and crash incident records
around 629 interstate exits in Indiana were analyzed for a three-month period from March 1-May 31, 2023. Nearly 20 million estimated deceleration
events and 4800 crash records were spatially joined to a 2-mile search radius
around each exit ramp. Results showed that deceleration events between -0.5 g
and -0.4 g had the highest correlation with an R2 of 0.69. This
study also identifies the top 20 interstate exit locations with highest
deceleration events. The framework presented in this study enables agencies and
transportation professionals to perform safety evaluations on raw trajectory
data without the need to integrate external data sources.