Integrating GIS and statistical approaches to enhance allocation of highway patrol resources

AuthorKhaled Ksaibati,Mahdi Rezapour,Er Yue
Date01 March 2020
DOI10.1177/1461355719888939
Published date01 March 2020
Subject MatterArticles
PSM888939 84..95
Article
International Journal of
Police Science & Management
Integrating GIS and statistical
2020, Vol. 22(1) 84–95
ª The Author(s) 2019
approaches to enhance allocation
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of highway patrol resources
DOI: 10.1177/1461355719888939
journals.sagepub.com/home/psm
Mahdi Rezapour
Department of Civil & Architectural Engineering, University of Wyoming, USA
Er Yue
Wyoming Technology Transfer Center, USA
Khaled Ksaibati
Wyoming Technology Transfer Center, USA
Abstract
Large truck crashes undermine the contribution of trucks to the U.S. economy due to the economic costs of the crashes.
Wyoming has the highest truck crash rate and the lowest budget contribution for traffic enforcement in the USA. Because of the
state’s intensive truck corridors, the Wyoming Highway Patrol (WHP) might not be able to use their resources efficiently.
Previous studies have indicated that WHP performed better when they allocate their resources efficiently at the right locations
and towards the right enforcements. This study used 4-year historical crash and enforcement data along Interstate 80 (I-80),
which has the highest truck-related crash rate in Wyoming. Crash data were filtered to include truck crashes only. However,
both truck and no-truck enforcements were included in the data because both could be at fault in truck crashes. This study used
two approaches to help state policy-makers improve traffic safety on I-80. First, a statistical method was used to identify
geometric variables contributing to allocated enforcement and truck crashes. Second, truck crashes and related enforcements
were visually assessed using Geographical Information Systems (GIS) mapping. Crash data were disaggregated into the main
driver actions of no improper driving, following too closely, improper lane change and driving too fast for the conditions. These
driver actions accounted for more than 70% of all truck crashes on I-80. Related enforcements were also identified and
disaggregated by driver actions. Disaggregated enforcements and crashes were visualized along the I-80 corridor using GIS
maps to see if WHP allocated their resources efficiently. Cluster index, enforcement spatial coverage and mean density are some
of the parameters used for the analyses. This study aimed to contribute to research on police effectiveness in reducing truck
crashes, police innovation and the use of GIS applications in enforcement. This methodology can be used by other agencies to
better allocate resources to improve traffic safety in most efficient ways.
Keywords
Geographic information system (GIS), enforcement, truck crashes, driver action, enforcement coverage, resource
allocation, spatial coverage
Submitted 23 Mar 2019, Revise received 03 Sep 2019, accepted 03 Oct 2019
Introduction
Corresponding author:
Every day, around the world, thousands of people are killed
Mahdi Rezapour, Department of Civil & Architectural Engineering, Uni-
versity of Wyoming, 1000 E University Ave, Dept. 3295, Laramie, WY
or injured due to road crashes (Peden et al., 2004). Between
82071, USA.
2015 and 2016, the number of highway fatalities increased
Email: mrezapou@uwyo.edu

Rezapour et al.
85
from 35,485 to 37,000. Moreover, between 2007 and 2016,
Background
the USA experienced the highest large-truck fatality rate;
Identification of geometric factors contributing to truck-
approximately 17% of fatalities were occupants of large
related crashes has a long tradition (Karlaftis and Golias,
trucks, 10.8% were non-occupants, and 72.4% were occu-
2002; Milton and Mannering, 1998). However, combined
pants of other vehicles (National Highway Traffic Safety
use of crash analysis and GIS analysis is limited (Li et al.,
Administration, 2017).
2007). Also, no study has investigated contributory geo-
Crashes can be attributed to four major factors: driver
metric variables to enforcement and truck crashes by
(e.g. distraction and driving too fast), vehicle (e.g. tires
implementing GIS mapping along with statistical model-
and brakes problems), roadway (e.g. roadway geometry
ling to help enforcement agencies allocate their resources
and wet roadway surface), and environmental conditions
in the most efficient ways. Based on the study methodol-
(e.g. rain and snow) (National Highway Traffic Safety
ogy, the following sections discuss implementing statistical
Administration, 2008). Different factors can be assigned
to the driver including recognition errors (e.g. inattention,
methods to identify geometric factors related to crashes, the
distraction, inadequate surveillance), decision errors (e.g.
importance of GIS mapping for enhancing enforcement,
driving too fast), and performance errors (e.g. improper
the enforcement halo impact, and the countermeasure
directional control) (National Highway Traffic Safety
impact of enforcement on crashes.
Administration, 2008). Although drivers’ actions in
Much research has been conducted on geometric con-
crashes can be assigned to subjective judgment, they can
tributory factors in crashes using statistical methods. A
be affected by road geometry, road conditions, warning
negative binomial model was used to identify risk factors
signs and enforcement. Therefore, it is important to pro-
that can impact the frequency of truck crashes (Dong et al.,
vide ideal conditions for drivers to minimize subjective
2016). The results indicated that annual average daily
judgment while driving.
traffic (AADT), percentage of trucks, segment length,
Wyoming has the highest rate of truck-related crashes in
degree of horizontal curvature, terrain type, median type,
the USA (Weber and Murray, 2014). This may be due to the
and posted speed limit are some of the factors that
large amount of truck through-traffic on Interstate 80 (I-
impact truck crashes. Poison regression and negative
80), and adverse weather conditions during the winter.
binomial models have been developed for truck crashes
However, traffic safety could be improved by taking appro-
on roadways with traffic signals (Daniel et al., 2002).
priate measures. Different countermeasures have been
The results indicated that segment length, traffic, degree
implementing in the USA, such as enforcement, engineer-
of horizontal curvature, crest curve length, and vertical
ing, and education. Based on previous research, enforce-
curve length are some of the factors that significantly
ment can lead to a reduction in the number of crashes,
impacted truck crashes.
especially if enforcement efforts are allocated to the right
One of the ways used in this study to improve truck
locations and are of the right type (Rezapour Mashhadi
safety is GIS mapping, which many studies have shown
et al., 2017; Terrill et al., 2016).
can help enforcement to allocate their resources in the most
In addition to having the highest rate of truck-related
efficient ways. One study (Smith, 2007) provided evidence
crashes, Wyoming is in the bottom 10 of states in terms
that GIS mapping can be used to improve traffic safety
of budget contribution towards enforcement (Weber and
efficiently. Different objectives have been defined for the
Murray, 2014). This high truck crash rate and low contri-
use of crime (crash) mapping by different studies. GIS
bution towards enforcement have led the Wyoming Depart-
mapping can be used to improve crime prevention, monitor
ment of Transportation (WYDOT) and Wyoming Highway
changes in crime and evaluate the effectiveness of crime
Patrol (WHP) to work jointly to identify how they could
prevention initiatives (Hirschfield and Bowers, 2003). GIS
make efficient use of their resources.
mapping has been shown to support enforcement goals,
This study had the primary objectives of improving
such as the allocation of resources to identified locations
truck safety through the development of Geographic
(Mamalian and La Vigne, 1999). Studies have also shown
Information System (GIS) maps and statistical models
that GIS mapping can be used to predict future events
for crashes and enforcement. Crash and enforcement
(Zehner, 2005). In addition, GIS mapping can be used to
data were disaggregated based on different driver
inform officers of crash locations, make decisions regard-
actions. In addition, based on different driver actions,
ing resource allocation, intervention evaluation, and inform
a GIS approach was used to visualize the locations
the community of crash locations (Kun, 2014). Point pat-
of different truck-related crashes and enforcements.
tern analysis is one of the most used tools in GIS applica-
GIS analyses can pinpoint where enforcement resources
tions. Possible applications of point density analysis
are currently concentrated and where they need to be
include finding the density of crimes, houses, road acci-
moved to.
dents, or utility lines. The study of high-risk road segments

86
International Journal of Police Science & Management 22(1)
using point density analysis provides transportation plan-
enforcement types individually in here. Moreover, single
ners with a better understanding of crash patterns and
hotspot analysis may not provide enough information for
enables improvements in road safety.
WHP who would not know the causes of the crashes.
Visibly automated enforcement (halo impact) has been
Therefore, different crash hotspots, based on different types
evaluated by previous research which found that its impact
of truck crash and enforcement were provided. Also, in a
was debilitated by distance from the...

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