Distractions and motor vehicle accidents. Data mining application on fatality analysis reporting system (FARS) data files
| DOI | https://doi.org/10.1108/02635570510633257 |
| Published date | 01 December 2005 |
| Pages | 1188-1205 |
| Date | 01 December 2005 |
| Author | Wen‐Shuan Tseng,Hang Nguyen,Jay Liebowitz,William Agresti |
| Subject Matter | Economics,Information & knowledge management,Management science & operations |
Distractions and motor vehicle
accidents
Data mining application on fatality analysis
reporting system (FARS) data files
Wen-Shuan Tseng, Hang Nguyen,
Jay Liebowitz and William Agresti
Graduate Division of Business and Management, Department
of Information Technology, Johns Hopkins University,
Rockville, Maryland, USA
Abstract
Purpose – This research applies data mining techniques to discover the relationship between driver
inattention and motor vehicle accidents.
Design/methodology/approach – The data used in this research is obtained from the Fatality
Analysis Reporting System of the National Highway Traffic Safety Administration, focused on the
Maryland and Washington, DC area from years 2000 to 2003. The data are first clustered using the
Kohonen networks. Then, the patterns and rules of the data are explored by decision tree and neural
network models.
Findings – Results suggests that when inattention and physical/mental conditions take place at the
same time, the driver has a higher tendency of being involved in a crash that collides into static
objects. Furthermore, with regards to the manner of collision, the relative importance of colliding into a
moving vehicle as the first harmful event is two times higher relative to that of colliding into a fixed
object as the first harmful event in a crash.
Research limitations/implications – The data used in this research are limited to fatal crashes
that happened in Maryland and Washington, DC from years 2000 to 2003.
Originality/value – This is one of the first research papers utilizing data mining techniques to
explore the possible relationships between driver inattention and motor vehicle crashes.
Keywords Data collection,Road vehicles, Accidents
Paper type Research paper
1. Introduction
Driver distraction, which has evolved as a significant issue of highway safety, is a
class of inattentive behaviors and mental status of drivers as defined by the AAA
Foundation for Traffic Safety (Stutts et al., 2001). In 1996, a study published by the
National Highway Traffic Safety Administration (NHTSA) found that approximately
25-30 percent of the injuries caused by car crashes were contributed by driver
distraction (Utter, 2001). In 1999, 11 percent of fatal crashes, corresponding to 4,462
fatalities, were due to inattention of drivers according to the Fatality Analysis
Reporting System (FARS) (Utter, 2001). The use of cellular phones while driving has
The Emerald Research Register for this journal is available at The current issue and full text archive of this journal is available at
www.emeraldinsight.com/researchregister www.emeraldinsight.com/0263-5577.htm
The authors gratefully acknowledge the grant and support from the GEICO Educational
Foundation, David Schindler, and Karen Watson for the GEICO Scholarship in Discovery
Informatics.
IMDS
105,9
1188
Industrial Management & Data
Systems
Vol. 105 No. 9, 2005
pp. 1188-1205
qEmerald Group Publishing Limited
0263-5577
DOI 10.1108/02635570510633257
been given the most consideration. In 2003, the Harvard Center for Risk Analysis
(HCRA) revealed that the risk of using cell phones while driving alone caused 330,000
moderate to severe injuries and approximately 2,600 deaths each year (Sundeen , 2003).
In response, most states have considered legislation related to driver use of cell phone
while driving.
This research program, funded by the GEICO Educational Foundation and
coordinated with the Department of Information Technology at Johns Hopkins
University, is conducted with the hope of applying data mining techniques to explore
the patterns of distraction factors and traffic crashes. Particularly, we use data mining
as the main tool to understand and recognize the correlation of data from the crash
information provided by FARS. The data system FARS, initiated in 1975, was
developed by the National Center for Statistics and Analysis (NCSA) with the aim to
build up a safer traffic community within the 50 states, the District of Columbia, and
Puerto Rico. FARS contains data on fatal traffic crashes within 30 days of the crash
mainly from police crash reports. Traffic safety problems classified by FARS were
used in evaluating both motor vehicle safety standards and highway safety initiatives.
Data files of crashes collected by FARS were analyzed by using data mining.
This research applies three data mining techniques, including Kohonen network s,
decision trees, and neural networks to find combinations of distraction factors that help
to explain the high accident rates. Cluster detection of the collection of data is done
using Kohonen networks, in which inputs are topologically ordered to compete for a
signal output (Kohonen, 1995). Decision trees allow the exploration and classification of
data mathematically derived from the effect of each incident on successive events
(Marakas, 2003). The potential correlation of distraction and car accidents from the
FARS data files, in addition, is generated by the techniques of neural networks. Neural
networks are designed based on the knowledge of the foundation on biological models
of how the human brain works (Berry and Linoff, 2004). Thus, literally, it is a netw ork
of connected neurons, which imitate the biological counterpart and deliver a helpful
output by combining various inputs together (Berry and Linoff, 2004). The findings of
this research will be applied for future study.
2. Literature review
According to NHTSA, driver distraction is a form of inattention when drivers shift
their attention away from the task at hand. Driver distractions can be cl assified into
two types (internal distraction and external stimuli) and four categories: visual (e.g.
reading a map), cognitive (e.g. lost in thought), auditory (e.g. respond to a ringing cell
phone) and biomechanical distraction (e.g. manually adjusting the radio volume)
(Ranney et al., 2000). Although it is possible that a driver can be engaged in one or more
activities at the same time, a distraction occurs when a driver “is delayed in the
recognition of information needed to safely accomplish the driving task because some
event, activity, object, or person within or outside the vehicle compels or induces the
driver, shifting attention away from the driving task” as defined by the AAA
Foundation for Traffic Safety (AAAFTS) (Stutts et al., 2001).
Driver distraction is not a new category of causations in car accidents. In fact,driver
inattention is one of the most prevalent causes of traffic crashes (Wang et al., 1996).
However, the concept of driver distractions was so subtle to the public that the
government and legislators did not pay much attention to driver distractions until the
Distractions and
motor vehicle
accidents
1189
To continue reading
Request your trial