TITLE:
Exploring Crowdsourced Hard—Acceleration and Braking Event Data for Evaluating Safety Performance of Low-Volume Rural Highways in Iowa
AUTHORS:
Shoaib Mahmud, Christopher M. Day
KEYWORDS:
Connected Vehicle, Low Volume Highway, High-Risk Crash Sites, Hard Acceleration and Braking Events, Geographic Information System
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.13 No.2,
April
13,
2023
ABSTRACT: There are over four
million miles of two-lane roadways across the United States, of which a
substantial portion is low-volume roads (LVR). Traditionally, most traffic
safety efforts and countermeasures focus on high-volume high-crash urban
locations. This is because LVRs cover an extensive area, and the rarity of
crashes makes it challenging to use crash data to monitor the safety
performance of LVRs regularly. In addition, obtaining up-to-date roadway
information, such as pavement or shoulder conditions of an extensive LVR
network, can be exceptionally difficult. In recent times, crowdsourced
hard-acceleration and braking event data have become commercially available,
which can provide precise geolocation information and can be readily acquired
from different vendors. The present paper examines the potential use of this
data to identify opportunities to monitor the safety of LVRs. This research examined approximately 12 million
hard-acceleration and hard-braking events over a 3-months period and
26,743 crashes, including 9373 fatal injuries over the past 5-year period. The
study found a moderate correlation between hard acceleration/hard-braking
events with historical crash events. This study conducted a hot spot analysis
using hard-acceleration/hard-braking and crash datasets. Hotspot analysis
detected spatial clusters of high-risk crash locations and detected 848 common
high-risk sites. Finally, this paper proposes a combined ranking scheme that
simultaneously considers historical crash events and hard-acceleration/hard-braking
events. The research concludes by suggesting that agencies can potentially use
the hard-acceleration and hard-braking event dataset along with the historical
crash dataset to effectively supervise the safety performance of the vast network
of LVRs more frequently.