TITLE:
Scalable and Accessible Crash Hot Spot Detection for Traffic Law Enforcement
AUTHORS:
Beau Burdett, Ran Yi, Steven T. Parker, Andrea Bill, David A. Noyce
KEYWORDS:
Hot Spots, Law Enforcement, Traffic Data, Crash Data
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.11 No.2,
April
28,
2021
ABSTRACT: Law enforcement agencies have begun utilizing traffic and crash data to
improve traffic law enforcement delivery. However, many agencies often do not
have the resources or expertise to harness fully the benefits this data offers.
A free to use, scalable traffic crash hot spot detection tool was developed to
aid law enforcement agency decision makers, statewide to the local municipality
level. The tool was developed to identify crash hot spots algorithmically with a range of customizable parameters based on
location, date and time, and crash factors, enabling quick, dynamic
queries. These capabilities provide the ability for law enforcement agencies to
conduct “what if” analyses and make data-driven allocation decisions, placing
officer resources where they are most needed. The two-step algorithm first
identifies potential hot spots based on crash
density and then ranks each hot spot using a standardized z-score measure
of relative significance. To test the viability of the tool, a pilot was
conducted identifying 27 hot spots across Wisconsin where targeted enforcement
was then deployed. Despite officer skepticism, results from the pilot found
officers at sites targeted for speeding and seatbelt violations were nearly
twice as likely to initiate traffic stops compared to non-targeted hot spots.
Empirical Bayes before-and-after crash analyses found fatal and injury crashes
reduced significantly by nearly 11% during the months with targeted
enforcement, while property damage crashes and total crashes were unchanged.
Overall, the results show the algorithm can identify hotspots where, coupled
with targeted enforcement, traffic safety improvements can be made.