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AN ANALYSIS OF A CONTINGENCY PROGRAM ON DESIGNATED
DRIVERS AT A COLLEGE BAR

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RICHARD R. KAZBOUR AND JON S. BAILEY

FLORIDA STATE UNIVERSITY

The present study evaluated the effects of prompts and incentives on designated drivers in a bar.
We defined the dependent variable as the percentage of customers either functioning as or riding
with a designated driver. We used an ABCA design to evaluate the effectiveness of prompts and
incentives on the dependent variable. Results indicated that the intervention was successful at
increasing the ratio of safe to unsafe passengers in a bar.

Key words: designated driver, incentives, prompts

_______________________________________________________________________________

In 2007, there were 3,221 traffic fatalities
reported in the state of Florida, 38.6% of which
were alcohol related, and a total 64,410 arrests
were made for driving under the influence
(DUI; Florida Department of Highway Safety
and Motor Vehicles [DHSMV], 2008, n.d.). In
the state, mandatory penalties for a conviction
of DUI consist of a minimum 50 hr of
community service, up to 1 year probation,
license revocation for a minimum of 180 days,
and 12 hr of DUI school (Florida DHSMV,
2009; Florida Department of Motor Vehicles,
2009). Solomon (2009) estimated that the cost of
a DUI arrest in the United States averages
$10,000 or more, not including costs associated
with the injury of involved parties, life insurance
premium increases, and loss of income.

According to the National Highway Traffic
Safety Administration (2009) a designated
driver (DD) is ‘‘a drinking-age adult who agrees
not to drink any alcoholic beverages and to
safely transport anyone else home.’’ Brigham,
Meier, and Goodner (1995) conducted research
aimed at increasing the number of DDs at a

bar, using visual prompts inside the bar and
nonalcoholic beverages as incentives for any
participating DD. The authors validated a self-
identified DD by following the DD to his or
her car and counted DDs as an individual who
got into the car and drove away. Elwood, Lloyd,
Morris, Tofte, and Zandecki (2005) used verbal
praise as an additional contingency for self-
identified DDs. Saksefski, Kazbour, Deller, and
Aboul (2008) used a portable breathalyzer
device to ensure a more accurate measure of
participants’ blood alcohol concentration
(BAC) and provided incentives for any person
under 0.05 BAC. All three studies provided
prompts inside the bar, and each demonstrated
an overall increase in the number of DDs across
phases. The purpose of the current study was to
expand on this research to increase the use of a
DD at a bar.

METHOD

Participation and Setting

We conducted the study at a bar located
across the street from a large southeastern
university. Over a 6-month period, there were
14 DUIs within a 0.5-mile radius of the bar of
interest (Tallahassee Police Department, 2008).
Any customer in the bar during the research was
eligible for participation in the study and could
declare him- or herself a DD. A bar employee
checked identification at the front door and
only allowed entrance for people 21 years and

Address correspondence to Richard Kazbour, 5220
Croyden Ave., Apt. 2302, Kalamazoo, Michigan 49009
(e-mail: richardkazbour@hotmail.com).

doi: 10.1901/jaba.2010.43-

273

We thank the following contributors to the research
because it would not have been possible without them:
Poor Paul’s Pourhouse, Hungry Howie’s Pizza, Tri-Eagle
Distributors, the Partnership for Alcohol Responsibility,
Caitlin Etherton, Ashley Scharbarth, Moody Sareini, Al
Lang, Christine Franzetti, and Kristen Black.

JOURNAL OF APPLIED BEHAVIOR ANALYSIS 2010, 43, 273–277 NUMBER 2 (SUMMER 2010)

273

older. For purposes of data collection, we gave
each customer who walked through the front
door of the bar a plain white wristband. Next,
we tallied the number of bands at the end of the
night, which indicated the total number of
patrons at the bar for the night.

Participants included any bar patron who
voluntarily submitted a breath sample and was
driving one or more people home. Although
0.08 BAC is currently the legal limit for
impaired driving in all 50 states, a BAC of 0
was used for the absolute safety of everyone
involved in the study. Research suggests that as
little as one alcoholic drink can impair driving
and produce some loss of judgment in adults
(California Department of Alcohol and Drug
Programs, 2008; Watson, Watson, & Batt,
1981). The legal limit is simply the BAC
number above which a driver is automatically
guilty of driving under the influence (or some
related statute) without any other evidence.
Many states allow for DUI charges and
conviction when a driver has a lower BAC
reading but fails field sobriety tests, drives
erratically, or otherwise shows signs of being
impaired (Florida DHSMV, 2009).

The authors administered breath tests and
handed out incentives after 1:30 a.m. to
minimize the possibility of people claiming to
be a DD, only to leave the bar and drink
elsewhere (county law prohibited the sale of
alcohol in bars after 2:00 a.m., and most bars
give a last call for alcohol well before 2:00 a.m.).
All interaction with participants took place at
the front door of the bar, which was used for
both entry and exit. One or two of the research
assistants on staff assisted in answering ques-
tions and managing consent forms. Breath test
administration took approximately 10 s. There-
fore, even with multiple participants, wait time
was minimal. A total of 22 customers claimed
to be a DD across all phases of the study. Data
collection occurred on Thursday and Friday
nights from 12:00 a.m. until 2:00 a.m. across
8 weeks for a total of 16 sessions. The

university’s institutional review board approved
all procedures.

Experimental Design

We used an ABCA experimental design in
which A was baseline, B was bar prompt plus
pizza, and C was advertisement for pizza and gas
to evaluate the effectiveness of prompts and
incentives on the percentage of bar patrons
functioning as or riding with a DD. Any bar
patron on any night could participate. Thus, the
unit of analysis did not necessarily involve a single
subject or group of the same people, but rather
any bar patron at any point during the study.

Data Collection and Interobserver Agreement
The device used to establish BAC was an Alco-

Sensor IV by Intoximeters Inc. (which may be
purchased at the company Web site, www.intox.
com). The device is an automated handheld
breath alcohol instrument that is approved by the
U.S. Department of Transportation. To ensure
accurate readouts during data collection, we
calibrated the instrument using a dry-gas method
before the first session of the study.

Each breath submission occurred at the front
door of the bar in clear view of all customers
and staff. Each participant blew into the
mouthpiece of the breathalyzer until a loud
and distinct clicking sound indicated the
completion of the breath submission. At this
time, Observer 1 recorded the digital readout
on the breathalyzer. Next, Observer 2 indepen-
dently viewed and recorded the readout. We
calculated interobserver agreement by dividing
agreements (intervals in which both observers
recorded the same readout) by agreements plus
disagreements (intervals where the two observ-
ers did not record the same readout) and
converted this ratio to a percentage. Interob-
server agreement was 100%.

General Method
Across all conditions, the researcher read the

following statement to any self-identified DD
whose BAC was over 0:

274 RICHARD R. KAZBOUR and JON S. BAILEY

Thank you for participation in our study and for
your willingness to submit to a breath-alcohol test.
Because your BAC was not zero, you have not met
our criterion to be identified as a DD. We appreciate
any effort you have made to minimize your drinking,
but want you to be aware that even breath-alcohol
levels below 0.08 (legal limit in the state of Florida),
can impair performance of complex tasks like driving
and might also leave you vulnerable to DUI charges
if you were to be observed violating traffic laws,
driving carelessly, or were involved in a crash. Thus,
we discourage you from driving until all the alcohol
in your system has been metabolized. If you need
transportation before that, we will help you to
arrange it.

During the intervention conditions, the
researcher told individuals whose BAC was over
0, ‘‘You are not eligible for the incentives.’’

Baseline

Any DD was eligible for free soft drinks
anytime by declaring him- or herself a DD to
the bartender, in accordance with an established
bar policy. Although this program had been in
place at the bar for over 2 years, all customers
may not have been aware of it. Therefore,
experimenters placed 12 tabletop signs (30.5 cm
by 15.2 cm) around the bar in clear view of the
customers, notifying them of the opportunity
for free soft drinks for DDs. The bartender used
the bar’s public address system at 12:30 a.m.
and at 1:30 a.m. to announce the opportunity
for free soft drinks by contacting the researchers
who stood near the front door. When approached
by an interested party, the researcher described
the study, provided a free soft drink (to all self-
identified DDs, irrespective of BAC), and asked
the person to participate by submitting a breath
sample before departure from the bar.

In-Bar Prompts plus Pizza

This condition consisted of prompts inside
the bar, advertising the opportunity for free
pizza (with the same 12 signs used in baseline,
except that the signs advertised free pizza and
soft drinks) to any group of 5 people who had a
DD. The bartender announced this on the
public address system at 12:30 a.m. and 1:30
a.m. Anyone who identified him- or herself as a

DD received a bright blue wristband and was
asked to submit a breath sample on the way out of
the bar. In the instance that a 0 readout was
indicated by the breathalyzer, the DD and up to 4
other people in the group received two large slices
of pepperoni or cheese pizza and free soft drinks.

Posters in the Community

One week before data collection during this
phase, the first author placed a total of 75
posters on the walls and windows of local
businesses and apartments within a 4-mile
radius of the bar. In addition to pizza, we
supplied $5 gas cards as a reinforcer for being a
DD for the remainder of Phase C. The posters
were each 61 cm by 30.5 cm and read,
‘‘DESIGNATED DRIVERS GET FREE GAS
& PASSENGERS ENJOY FREE PIZZA AT
[bar name] THURSDAY AND FRIDAY
NIGHTS NOW THROUGH NOVEMBER
21ST!’’ The posters remained in place until the
return to baseline.

Newspaper and Radio

This intervention consisted of the broadcast
of a radio interview with the first author, along
with a newspaper story. The 3.5-min interview
ran twice, 2 days before the first session of the
intervention. The author explained the research
and provided information regarding where and
when the listeners could participate. The
newspaper story, headlined ‘‘Designated Drivers
Rewarded,’’ ran on the front page of the local
university’s newspaper publication. The release
of the paper occurred 3 days prior to data
collection and contained the same information
as the radio interview.

RESULTS AND DISCUSSION

Figure 1 indicates the ratios of customers
either functioning as or riding with a DD across
all phases of the study. During baseline, the
percentage of customers functioning as or riding
with a DD was below 0.5% (1 customer)
during the 4 nights of baseline. During the 4

ANALYSIS OF A CONTINGENCY 275

nights of data collection during the in-bar
prompts plus pizza intervention, 5 customers
had a DD with a BAC of 0 (M 5 2%). In the 6
nights of data collection during the advertise-
ment for pizza and gas intervention, a total of
46 customers were either riding with or
functioning as a DD (M 5 12%, with a high
of 24%). During the second baseline, the
percentage of customers riding with a DD
dropped to 0. A total of 6 of the 22 participants
had BACs over the 0 criterion. Of those
participants, only 1 had a BAC over the legal
limit of 0.08.

The low cost of the program encourages its
use in establishments that serve alcohol. There
was virtually no cost to implement the program.
All pizza was donated by a local pizzeria, and all
gas cards were donated by a local distributor of
alcohol. Everyone involved supported and
applauded our efforts. The cost of the inter-
vention would have been approximately $150
without the support.

Although the DD program was an overall
success, there are some questions to consider.
First, our count of the percentage of safe
customers to total customers may not be
accurate, because we counted customers who

walked into the bar during data-collection hours
but not customers already in the bar. Next, we
removed the intervention at the end of the
study, presumably affecting the number of
customers who would have a DD from that
point forward. Transfer of the program to the
bar ownership would have required one to two
more employees scheduled per night at the bar
to fill the positions of the researchers. Because
participation was voluntary, there was no way to
determine how many individuals were DDs but
chose not to participate. Of the people who did
participate, there is some possibility that they
were simply consumers of the free products and
did not drive anyone home.

There is much room for continued research
on the topic of designated drivers. Our society
places a great deal of effort on applying
consequences such as negative reinforcement
and punishment as they pertain to drinking and
driving. Success might be obtained more easily
by providing positive reinforcement for the
responsible behavior associated with having a
DD, as opposed to providing those negatively
reinforcing and punishing consequences for the
irresponsible and potentially more deadly
behavior of drinking and driving.

Figure 1. Percentage of bar patrons functioning as or riding with a designated driver across all conditions including
baseline, in-bar prompts plus pizza, advertisement for pizza and gas, and the final baseline. Note that the y axis maximum
is 30%.

276 RICHARD R. KAZBOUR and JON S. BAILEY

REFERENCES

Brigham, T. A., Meier, S. M., & Goodner, V. (1995).
Increasing designated driving with a program of
prompts and incentives. Journal of Applied Behavior
Analysis, 28, 83–84.

California Department of Alcohol and Drug Programs.
(2008). Driving under the influence (DUI) statistics.
Retrieved June 11, 2009, from http://www.adp.state.
ca.us/FactSheets/DrivingUnderTheInfluenceStatistics.
pdf

Elwood, C., Lloyd, L., Morris, D., Tofte, A., & Zandecki,
M. (2005). Increasing pre-designated drivers: An
extension of a prompt and incentive intervention
package. OBM Network News, 19, 13.

Florida Department of Highway Safety and Motor
Vehicles. (2008). 2007 Florida uniform traffic citation
statistics. Retrieved January 15, 2009, from http://
www.flhsmv.gov/reports/2007UTCStats/State_County_
Totals

Florida Department of Highway Safety and Motor
Vehicles. (2009). Florida DUI and administrative
suspension laws. Retrieved January 15, 2009, from
http://www.flhsmv.gov/ddl/duilaws.html

Florida Department of Highway Safety and Motor
Vehicles. (n.d.). Traffic crash statistics report 2007.
Retrieved January 14, 2009, from http://www.flhsmv.
gov/hsmvdocs /CS2007

Florida Department of Motor Vehicles. (2009). Drunk
driving penalties in Florida. Retrieved January 14,
2009, from http://www.dmvflorida.org/florida-dui.
shtml

National Highway Traffic Safety Administration. (2009).
Designated driver safe ride program. Retrieved July 18,
2009, from http://www.nhtsa.dot.gov/people/injury/
alcohol/ Designated Driver/intro2.html

Saksefski, A., Kazbour, R., Deller, D., & Aboul, Y. (2008,
September). A public benefit analysis of designated drivers.
Paper presented at the Florida Association for Behavior
Analysis 27th annual conference, Bonita Springs.

Solomon, C. (2009). DUI: The $10,000 ride home. MSN
money, personal finance, insurance. Retrieved January
15, 2009, from http://articles.moneycentral.msn.com/
Insurance/InsureYourCar/DUIThe$10000RideHome.
aspx?page52

Tallahassee Police Department. (2008). City of Tallahassee
crime statistics report. Retrieved April 12, 2008, from
http://tlcgis6.co.leon.fl.us/tops/

Watson, P. E., Watson, I. D., & Batt, R. D. (1981).
Prediction of blood alcohol concentration in human
subjects. Journal of Studies on Alcohol, 42, 547–556.

Received March 23, 2009
Final acceptance September 28, 2009
Action Editor, Mark Dixon

ANALYSIS OF A CONTINGENCY 277

RESEARCH ARTICLE

Mobile phone applications use while driving in

Ukraine: Self-reported frequencies and

psychosocial factors underpinning this risky

behaviour

Tetiana HillID
1*, Amanda N. Stephens2, Mark J. M. Sullman3

1 Hertfordshire Business School, University of Hertfordshire, Hatfield, United Kingdom, 2 Monash University

Accident Research Centre, Monash University, Melbourne, Australia, 3 Department of Social Sciences,

School of Humanities and Social Sciences, University of Nicosia, Nicosia, Cyprus

* t.hill4@herts.ac.uk

Abstract

Despite the fact that mobile phones have been transformed over the last decade into infor-

mation and communication hubs that are fundamental to modern life, there is little informa-

tion on how this has impacted on mobile phone use while driving. The present study was

conducted in Ukraine, where this risky behaviour remains a common driving practice,

despite legislative bans. A total of 220 (male = 82%; mean age = 35.53; SD = 10.54) drivers

completed an online survey assessing frequency of engaging in a range of mobile

phone

applications while driving. Four variables of the theory of planned behaviour (general atti-

tude and intention towards phone use while driving, social norms towards

mobile phone

use, perceived behavioural control, the specific beliefs about being able to engage in dis-

tracting activities and drive safely), and type A behaviour pattern were also collected. The

results showed that, during the last year, 65% of drivers had read a text message and 49%

had written a text using mobile phone applications. Likewise, a substantial proportion of the

sample reported using social media while driving, by checking (34%), sending or typing a

post (25%) on social network applications. Hierarchical stepwise regressions showed that a

positive attitude towards mobile phone use while driving and beliefs about being able to

drive safely and write or read a text message were significantly associated with the

mobile

phone applications use while driving. No associations were found between the type A

behaviour pattern and mobile phone applications use.

Introducti

Distracted driving is one of the major risk factors for road traffic injuries and fatalities for driv-

ers, passengers and other road user groups in the world. It often occurs when a driver divides

their attention, voluntarily or involuntarily, between driving and an unrelated secondary task.

According to the estimates provided by the National Highway Traffic Safety Administration

[1], it accounts for approximately 8% of all fatal crashes and 15% of all injury crashes, as well as

PLOS ONE

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 1 / 15

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OPEN ACCESS

Citation: Hill T, Stephens AN, Sullman MJM (2021)

Mobile phone applications use while driving in
Ukraine: Self-reported frequencies and
psychosocial factors underpinning this risky

behaviour. PLoS ONE 16(2): e0247006. https://doi.

org/10.1371/journal.pone.0247006

Editor: Feng Chen, Tongii University, CHINA

Received: October 31, 2020

Accepted: January 30, 2021

Published: February 17, 2021

Commons Attribution License, which permits

unrestricted use, distribution, and reproduction in

any medium, provided the original author and

source are credited.

Data Availability Statement: The data that support

the findings of this study are openly available in

Zenodo (DOI: 10.5281/zenodo.4115826).

Funding: The authors received no specific funding

for this work.

Competing interests: The authors have declared

that no competing interests exist.

https://orcid.org/0000-0003-4234-5771

https://doi.org/10.1371/journal.pone.0247006

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https://doi.org/10.1371/journal.pone.0247006

http://creativecommons.org/licenses/by/4.0/

https://doi.org/10.5281/zenodo.4115826

14% of all police-reported motor vehicle traffic crashes. Driving distraction may take different

forms and vary in terms of the level of interference it can cause, meaning that not all distrac-

tion activities have the same impairment on driving performance. Young et al. [2] categorised

driver distraction into four distinct types: visual (focusing one’s visual attention on a secondary

task), auditory (focusing one’s attention on auditory signals and misfocusing on the road envi-

ronment), physical (removing one’s hand(s) from the steering wheel to manipulate other

objects), and cognitive (thinking about something unrelated to the driving task). Each second-

ary task may cause one or more types of driver distractions depending on its complexity and

demands on driver mental workload [3,4].

There is ongoing debate regarding which secondary tasks have the most detrimental effect

on driving performance, however, mobile phone use while driving is considered as one of the

most important global road safety issues. This is primarily due to the fact that interactions with

mobile phones can encompass all four types of distractions (i.e. visual, auditory, manual, and

cognitive). By way of example, the results of the three-year Second Strategic Highway Research

Program Naturalistic Driving Study (SHRP 2 NDS), employing a sample of more than 3500

drivers, reported that browsing, handheld dialling and handheld text interactions with a

mobile phone are associated with an increased crash risk [5,6].

These findings are also supported by the results of the recent systematic review and meta-

analysis of the on-road and naturalistic studies [7,8], as well as epidemiological studies (e.g.,

[9,10]).

The concern over mobile phone crash risk is increasing, as mobile devices become informa-

tion and communication hubs often fundamental to modern life. For instance, mobile phone

functions are no longer limited to making/receiving calls and writing/sending text messages;

smart technology allows engagement in a wide range of functions (e.g., emails, social media,

personal data for fitness, banking etc.). Some functions may be beneficial for drivers, such as

Global Positioning System (GPS), navigation and real-time traffic updates and these are per-

mitted if the mobile phone is secured in an appropriate holder. However, it is well established

that some mobile phone applications may significantly draw attention from driving. This

includes both observational and naturalistic studies (e.g., [11–13]). For example, naturalistic

driving research conducted on 221 Israeli drivers found that drivers touched their smart-

phones’ screens at least 1.71 times per minute while driving for different purposes including to

use applications [12]. Furthermore, another naturalistic study in Finland tracked the frequen-

cies of use of different mobile phone applications while driving in a sample of 30 drivers. The

results showed that drivers most commonly used WhatsApp messaging application, with one

instance of use having a median duration of 35 seconds and a median of 8 screen touches [14].

An interesting contrast was the fact that navigation application use had a median duration of 3

screen touches and lasted for 11 seconds. The authors concluded that messaging

applications

(as opposed to image- or audio-based applications) pose the greatest threat to a driver’s ability

to control a vehicle. A similar finding was also reported by McNabb and Gray [15] who found

that image-based applications, such as Instagram and Snapchat, had no significant effect on

brake reaction time (BRT) and time headway (TH) variability in a driving simulator study.

More specifically, the findings showed that scrolling through and reading the updates from a

Facebook account considerably increased BRT and TH variability in a sample of 18 drivers in

the USA. Similarly, another driving simulator research in Spain found that texting on What-

sApp while driving significantly impaired driving performance for all age groups, and espe-

cially among older drivers [12]. Collectively, most of the abovementioned studies conclude

that the introduction of mobile phone applications has not only dramatically increased mobile

phone use among the general population [16] but has also affected the frequency of mobile

phone use among drivers (e.g., [11,12,17]). Consequently, and despite legislation strictly

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https://doi.org/10.1371/journal.pone.0247006

prohibiting handheld mobile phone use in most of the countries, many drivers continue to

interact with their phones while driving [3,6].

A number of studies have shown that enforcement and penalties are not effective in reduc-

ing the use of mobile phones while driving (e.g., [18,19]). Research in to other dangerous and

illegal driving behaviours, such as speeding [20] and drink-driving [21] has found the same.

That is, drivers will engage in these behaviours more frequently when they do not feel they will

get caught, do not believe there is a risk of crash and when they have friends and family who

also engage in the behaviour. This suggests that it is important to understand the

attitudes

toward the behaviour in order to address the problem.

Previous studies have also examined driver attitudes toward mobile phone use. These have

focussed on predicting use based on psychosocial variables, such as attitudes (e.g., [22]), beliefs

(e.g., [23]), levels of mobile phone involvement (e.g., [24]), and self-reported frequency of text-

ing and calling behaviours (e.g., [25]). Most of these studies report that the prevalence of

mobile phone use while driving is directly related to positive attitudes drivers have

towards

this behaviour and the perceived benefits their use may have [26].

There is also research that has investigated the attitude-behaviour relationship for driver

mobile phone use using the Theory of Planned Behaviour (TPB; [27]). Based on this model,

mobile phone use can be predicted by understanding one’s intentions to engage in it, which

are predetermined by attitudes (positive / negative evaluation of the target behaviour), subjec-

tive norms (perceived approval / disapproval of the target behaviour by significant others), and

perceived behavioural control (PBC; perceived ease / difficulty of performing the target behav-

iour). The TPB has been successfully applied in previous studies to explain texting and calling

behaviour while driving (e.g., [22–24,28,29]). In addition, using an extended TPB, which

included the additional components of anticipated regret, moral norm, mobile phone involve-

ment, and cognitive capture, Gauld et al. [30] explored university student drivers’ intentions to

engage in initiating, monitoring/reading, and responding to social interactive technology on a

smartphone. However, none of these have attempted to explore the associations between the

TPB variables and the proliferation of mobile phone applications use.

Personality is another factor that can underlie the decision to use a mobile phone

while

driving. For example, Bianchi and Phillips [31] found that problematic mobile phone use was

more prevalent among extraverted drivers as they tend to make more calls while

driving.

Sween et al. [32] identified that greater emotionality, less conscientiousness, openness to expe-

rience and honesty/humility were strongly associated with frequent mobile phone

use while

driving. No previous research, however, has been carried out to explore the associations

between the frequency of mobile phone use while driving and personality traits/types that may

explain the tendency to multitask, such as the type A behaviour pattern (TABP). TABP has

been recognised as one of the individual variables increasing the risk of road traffic accidents

(RTAs; e.g., [33–35]).

TABP was originally conceptualised by two cardiologists, Friedman and Rosenman [36,37],

who noted that individuals with heart disease exhibit different behavioural patterns to those

with no heart disease. As such, those individuals who have a greater competitive need for

achievement, time urgency, aggressiveness, and hostility have overall higher odds of having

coronary heart disease. When applied to the driving context, it can be predicted that drivers

who exhibit TABP may approach various driving situations with a heightened sense of urgency

and impatience, which can be a crucial factor leading to RTAs [38]. For example, West et al.

[35] found that TABP was strongly associated with speeding behaviour. The results of a study

employing a sample of bus drivers in the USA and India showed that drivers exhibiting TABP

reported overall higher traffic accident rates per month, in comparison to those who exhibit

the opposite type B behaviour pattern (i.e., these individuals tend to enjoy working steadily

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https://doi.org/10.1371/journal.pone.0247006

and do not experience stress due to a lack of achievements; [39]). Lastly, using a large sample

of 11.965 French employees aged 39–52 years, Nabi et al. [33] identified that type A drivers

had an increased risk of RTAs. It can be concluded that the implications of TABP for traffic

safety may be quite severe, meaning that it is important to investigate whether and how TABP

is associated with other safety-critical driving behaviours, such as mobile phone use while

driving.

The present study

The present study was conducted with a sample of Ukrainian drivers, for whom mobile phone

use while driving is a prevalent behaviour. The main aim of this study was to explore the fre-

quencies of use of different mobile phone applications while driving and their underpinning

psychosocial factors. In particular, we investigated the associations between mobile phone

applications use and the prevalence of TABP in the Ukrainian drivers, their beliefs about being

able to engage in secondary tasks and drive safely, as well as the four TPB components, such as

the general attitude and intention towards phone use while driving, social norms towards

mobile phone use, and perceived behavioural control.

Method

Participants

The sample consisted of 220 fully licenced drivers in Ukraine. To be eligible to take part in the

study, participants were required to hold a current driver’s licence, own a mobile phone, and

report driving at least once in the past six months. Most participants were males (82%), aged

19–70 years old (SD = 10.54, mean age = 35.53). Participants reported holding a driving licence
for an average of 4.01 years (SD = 1.17, range: 1–5) and driving approximately 17.02 kilometres
per week (SD = 18.01, range: 0–150).

Procedure

Firstly, the survey was translated into Ukrainian by a professional translator and checked for

consistency by one of the authors (TH) who is fluent in both languages. Secondly, the survey

was hosted online using the Qualtrics web surveying platform. Data were collected via a link

sent to the personal e-mails of both staff and students of the National Aviation University in

Kyiv, Ukraine, using the existing contacts of one of the authors (TH), who is also an alumna of

this University. Approval for this study was granted by the University’s ethics committee. The

survey was also advertised on a national forum for Ukrainian motorists. All the study partici-

pants were encouraged to pass on the link to eligible friends and family (i.e. those who hold a

current driver’s licence, own a mobile phone, and had driven at least once in the past six

months). Before completing the online survey, the participants were asked to familiarise them-

selves with the information sheet outlining that the study was voluntary with no compensation

for the participation and that their data would be collected and held confidentially and

anonymously.

Measures

Demographic variables. Participants were asked to report their age, sex, marital and

work status, educational attainment as well as the number of hours spent driving on average

each week. They were also asked to indicate the driving purpose based on the ratio of

driving

for work versus leisure using a 7- point Likert scale (1 = primarily for work; 7 = primarily for

leisure).

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Frequency of mobile phone applications use while driving. To assess the frequency of

mobile phone application use while driving, participants were also asked “How often do you

do the following on your mobile phone while driving?”:

• Read a text message using mobile phone applications (e.g., Viber, WhatsApp).

• Send a text message using mobile phone applications (e.g., Viber, WhatsApp).

• Read/look at posts on social networks (e.g., Facebook, VK, Odnoklassniki).

• Send or make posts on social networks (e.g., Facebook, VK, Odnoklassniki).

• Make a video call (e.g., FaceTime, Skype).

• Make a call using mobile phone applications (e.g., Viber, WhatsApp).

• Write an email.

• Read an email.

• Type an address into maps (e.g., Google maps, Yandex).

• Use an intellectual assistant (e.g., Siri, Majel).

• Take photos “selfies”.

Participants were asked to rate each of the options using a 7-point Likert scale (1 = more

than once a day; 4 = one or two times a month; 7 = never). The mobile phone applications

included in the survey options were selected based on their popularity among the Ukrainian

population in 2015 when the study was conducted.

Theory of planned behaviour variables. To investigate the associations between the TPB

variables and various mobile phone applications use while driving, participants were asked to

rate four items extracted from the TPB questionnaire developed by Walsh et al. [29]. Using a

7-point Likert scale (1 = strongly disagree; 7 = strongly agree), participants were asked to indi-

cate their level of agreement with the following statements: In the next week, to what extent do

you agree or disagree that:

• It is likely you will use your mobile phone while driving (measuring general intention to use

a mobile phone while driving).

• Using your mobile phone while driving will be good (measuring general attitude towards

mobile phone use while driving).

• Those people who are important to you would want you to use your mobile phone while

driving (measuring social norms towards mobile phone use while driving).

• You have complete control over whether you use your mobile phone while driving (measur-

ing perceived behavioural control).

The Bortner Type A scale. This self-report scale was used to assess TABP [40]. It contains

14 items, each consisting of opposing statements placed on a continuum ranging from the

extreme TABP to the absence of TABP. Participants were asked to indicate their position

between the two extremes using an 11- point Likert scale (e.g., 1 = casual about appointments;

11 = never late). The Bortner Type A scale is a unidimensional scale, the reliability of which in

the current sample was acceptable, with Cronbach’s alpha of 0.74.

Beliefs about being able to engage in secondary tasks and drive safely. To assess partici-

pants’ beliefs about their ability to safely use a mobile phone while driving, four items were

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added to the survey that were retrieved from a questionnaire developed by White et al. [25].

The questionnaire was developed based on a beliefs-based TPB approach aimed at investigat-

ing the direct determinants of intentions to engage in various risky behaviours, such as mobile

phone use while driving. Similar to the original questionnaire, in the current study, partici-

pants were asked to rate these items (e.g., “I am able to drive safely and send a text message at

the same time”, “I am able to drive safely and talk on a handheld phone at the same time”)

using a 7-point Likert scale (1 = strongly disagree; 7 = strongly agree).

Data handling and analysis

All analyses were undertaken using SPSS v.22. There were no missing data. The variable for

the ratio for driving was recoded into a binary variable representing driving “primarily for

work” (scores of 1 to 3) and “primarily for leisure” (scores of 4 to 7). Mobile phone frequency

was recoded as a binary variable for hand-held calls (included making and received calls) and

text messaging (included writing or reading text messages) while driving. To explore the asso-

ciations between the variables Spearman correlations were conducted, which are robust to

ordinal data. Cohen’s effect sizes of � .29 as small, between .30 to .49 as medium and � .50 as

large, were used to describe the relationships [41]. Hierarchical stepwise regressions were used

to explore the psychosocial factors related to different types of mobile phone applications use

while driving.

Results

Frequency of mobile phone use

while driving

Table 1 presents the frequencies of self-reported interactions with the mobile phone applica-

tions while driving. The majority of the sample (65%) reported they would read a text

message

and 49% reported they would type one while driving. Making a call and/or a video call using

mobile phone applications were less common amongst the Ukrainian drivers, with nearly 25%

of the sample reporting they would make a call using the applications and 21% reporting they

would video call someone while driving. No less concerning, a significant percentage of the

sample reported engaging in social media while driving: 34% of drivers checked and 25% sent

or typed a post on social network applications. Notably, 32% of drivers reported dealing with

Table 1. Self-reported mobile phone applications usage while driving (N = 220).

While driving, how often do you. . . More than once a

day

(%)

Daily

(%)

1–2 times per

week (%)

1–2 times per

month (%)

1–2 times in six

months (%)

Once a year

(%)

Never

(%)

Read a text message using mobile phone

applications

15.9 12.3 25.0 5.5 2.7 3.6 35.0

Send a text message using mobile phone

applications

7.7 14.5 13.6 7.3 4.1 1.8 50.9

Read or look at posts on social networks 13.2 2.7 4.1 7.3 2.3 4.1 66.4

Send or make posts on social network 12.7 2.3 2.3 3.6 0.9 3.2 75.0

Make a video call 5.0 5.5 4.1 3.6 0.9 1.8 79.1

Make a call using mobile phone

applications

10.0 3.2 5.9 2.3 2.3 0.9 75.5

Write an email 10.0 5.0 8.2 3.2 2.7 2.7 68.2

Read an email 9.1 5.9 5.0 3.2 5.0 3.2 68.6

Type an address into maps 8.6 5.5 4.5 8.6 4.1 6.8 61.8

Use an intellectual assistant 8.2 0.9 2.7 1.4 2.3 0.0 84.5

Take photos “selfies” 8.6 7.7 5.5 7.7 3.2 8.6 58.6

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emails while driving and 38% typed an address into maps applications while driving. Just

under half of the participants (41.4%) reported taking selfies while driving at least once a year.

The least common behaviour amongst the drivers was using an intellectual assistant (15.5%).

Descriptive variables and intercorrelations between variables

Table 2 displays the correlations between engagement with a mobile phone while driving and

age, sex, hours driving per week, TABP, beliefs about being able to drive safely and interact

Table 2. Associations between engagement with different mobile phone applications while driving and age, sex, hours driven per week, TABP, specific beliefs about

being able to engage in secondary tasks and drive safely, and TPB vari

ables (N = 220).

While driving,

how often do

you. . .

Age Sex Hours

driving

per week

TABP

Able to

drive

safely

and read a

text

message
Able to
drive safely

and write a

text
message
Able to

drive

safely

and talk

on
phone

Able to drive

safely and

talk with

handsfree

unit

General

intention

towards
mobile phone
use while
driving
General
attitudes
towards
mobile

phone use

while driving

Social

norms

towards
mobile
phone use
while
driving

Perceived

behavioural

control

Read a text

message using

mobile phone
applications

.03 .07 -.18�� .10 -.21�� -.24�� -.13 -.05 -.05 -.09 -.07 .07

Send a text

message using
mobile phone
applications

.02 -.02 -.13 .07 -.22�� -.27�� -.14� -.05 -.11 -.12 -.14� .52

Read/look at

posts on social

networks

.02 -.07 -.04 .01 -.11 -.18�� -.06 .04 .12 -.16� -.11 -.01

Send or make

posts on social

network

-.01 -.05 .02 .01 -.11 -.14� -.04 .04 -.02 -.11 -.07 .01

Make a video

call

-.06 -.02 .01 .02 -.06 -.10 -.05 .01 -.05 -.12 -.08 .03

Make a call

using mobile

phone
applications

-.03 -.11 -.02 .02 -.11 -.16�� -.09 -.01 -.08 -.14� -.09 .06

Write an email -.05 -.13 -.01 -.01 -.13 -.16� .01 .07 -.08 -.18�� -.14� .06

Read an email -.06 -.12 .01 -.03 -.14� -.18�� -.01 .07 -.08 -.17� -.14� .04

Type an

address into

maps

.01 -.04 -.06 .01 -.19�� -.22�� -.06 .02 -.10 -.15� -.13� .03

Use an

intellectual

assistant

-.01 -.02 -.03 .01 -.07 -.06 -.01 .01 .03 -.05 -.01 .01

Take photos

“selfies”

-.03 -.10 .02 .06 -.20�� -.23�� -.06 -.04 -.03 -.03 -.11 .05

Variable Mean

(SD)

35.53

(10.54)

– 17.20

(18.03)

88.78

(17.57)

2.09 (1.56) 1.91 (1.50) 3.23

(2.07)

4.41 (2.25) 4.87 (2.20) 3.71 (2.12) 3.44 (2.14) 5.11 (1.96)

Variable range 19–70 – 0–150 27–134 1–7 1–7 1–7 1–7 1–7 1–7 1–7 1–7

Notes.
�� Significant at p < .001 �� p < .01 � p < .05. For sex, 1 = male, 2 = female.

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with a mobile phone, as well as the TPB variables. As can be observed from the table, very few

relationships emerged between these variables. There were no significant relationships

between any type of mobile phone interaction with age, TABP or hours driving per week.

Small but significant associations were found between beliefs about being able to drive safely

and read or write a text message with reading an email, typing an address into maps and taking

“selfie” photos while driving (rs ranged from .14 to .23, ps < .05). General attitudes towards using a mobile phone while driving and the belief about being able to safely drive and write a

text message were also weakly related to reading social media posts and making a call using

applications (rs ranged from .14 to .18, ps < .05). In all instances, drivers with stronger beliefs about their ability to engage in mobile phones use while driving and the benefits of doing so

reported more frequent interactions with their mobile phones while driving.

Table 3 displays the intercorrelations between demographic variables, specific beliefs about

being able to engage in secondary tasks and drive safely, attitudes, intentions, social norms and

perceived behavioural control. Age and hours driven were not significantly related to any of

these variables. TABP showed weak, positive relationships with the general attitude towards

mobile phone use while driving, social norms towards mobile phone use while driving, and

sex.

Relationships between interactions with mobile phones while driving and

perceptions about being able to do this safely

Hierarchical stepwise regressions were conducted on each of the selected interactions with a

mobile phone. These were taking “selfie” pictures while driving, typing an address into a map

application, reading or writing an email, making a video call and voice call using an applica-

tion, reading and writing a text message, as well as reading and writing social network posts.

In the first block, demographic information such as age, sex, driving purpose, and hours spend

driving per week were entered. In the second block, the Bortner (TABP) scale scores were

entered. In the third block, specific beliefs about being able to engage in secondary tasks and

Table 3. Associations between age, sex, hours driven per week, TABP, specific beliefs about being able to engage in secondary tasks and drive safely, and TPB vari-

ables (N = 220).

While driving, how often do you. . . 1 2 3 4 5 6 7 8 9 10 11

1. Age –

2. Sex .10 –

3. Hours driving per week .01 -.13 –

4. TABP .03 -.17� -.02 –

5. Belief about being able to drive safely and read a text message -.08 .10 -.01 -.01 –

6. Belief about being able to drive safely and write a text message -.10 .10 .05 -.03 .83�� –

7. Belief about being able to drive safely and talk on a mobile phone -.05 .02 .01 .02 .44�� .40�� –

8. Belief about being able to drive safely and talk with a handsfree unit .05 -.09 -.01 .05 .21�� .15� .60�� –

9. General intention towards mobile phone use while driving -.01 .02 .09 .10 0.3 .06 .17�� .09 –

10. General attitude towards mobile phone use while driving -.02 -.10 .08 .15� .05 .10 .08 -.02 .68�� –

11. Social norms towards mobile phone use while driving -.02 -.02 -.12 .16� .03 .05 .08 -.03 .60�� .80�� –

12. Perceived behavioural control -.03 -.05 -.06 .03 -.08 -.11 .05 .05 .18�� .20�� .23��

Note.
�� Significant at p < .001 �� p < .01 � p < .05.

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driving safely were entered. In the final block, the TPB variables such as the general attitude

and intention towards phone use while driving, social norms towards mobile phone use and

perceived behavioural control were entered. All were entered using the stepwise procedure,

the results of which are presented in Table 4.

As can be seen in Table 4, the only factors that were related to mobile phone usage were the

beliefs about being able to engage in secondary tasks and drive safely and positive attitudes

regarding the use of a mobile phone while driving. The regression analyses for using an intel-

lectual assistant, making a video call, writing posts on social networks, and reading / writing

text messages were not significant. With regard to taking “selfies”, typing an address onto a

map application and making a call using applications, drivers who believed they were more

able to drive safely while writing a text were more likely to engage in these behaviours. In

terms of writing an email and reading social network posts, a positive attitude towards the

behaviour was the key factor related to increased engagement in these activities. Age, sex and

type A behaviour were not significantly associated with any of the mobile phone interactions.

Discussion

The aim of the current study was to explore the frequencies of mobile phone applications use

while driving, as well as to examine the psychosocial factors that influence driver willingness to

use these applications despite the legislative bans in Ukraine. As such, we explored the associa-

tions between the TPB variables, TABP, as well as specific beliefs about being able to engage in

secondary tasks and drive safely and the frequencies of self-reported mobile phone applica-

tions usage while driving. Interestingly, no previous studies have attempted to investigate this

issue despite the fact that the functionality of mobile phones has rapidly increased over the last

10 years.

In general, our results showed that the Ukrainian drivers are most likely to use mobile

phone applications for reading text messages, followed by sending text messages, taking selfies,

typing an address into maps and writing / reading an email. The self-reported frequencies

overall suggest that participants regularly use mobile phone applications while driving, which

consequently may pose a safety risk not just to the drivers themselves, but also to other road

users. When exploring the frequencies of standard mobile phone functions usage, scholars

have previously reported that drivers most frequently use their mobile phones for answering /

making calls, with much smaller proportions of drivers reporting reading / sending text

Table 4. Factors related to different types of mobile phone applications use while driving (N = 220).

B (95% CI) β p
1. Take photos “selfies” R

2
0.03; F (1,218) = 7.03, p = 0.009

Belief about being able to drive safely and write a text message -.25 (-.44–.07) -.18 .009

2. Type an address into maps R
2

0.04; F (1,218) = 11.18, p = 0.001
Belief about being able to drive safely and write a text message -.31 (-.49–.13) -.22 .001

3.Write an email R
2

0.01; F (1,218) = 4.15, p = 0.04
General attitude towards mobile phone use while driving -.14 (-.28–.01) -.14 .043

4.Read an email R
2

0.02; F (1,218) = 4.15, p = 0.04
Belief about being able to drive safely and read a text message -.19 (-.37–.01) -.14 .043

5. Make a call using mobile phone applications R
2

0.03; F (1,218) = 7.02, p = 0.009
Belief about being able to drive safely and write a text message -.18 (-.43–.06) -.18 .009

6. Read/look at posts on social networks R
2

0.03; F (2,217) = 4.24, p = 0.041
Belief about being able to drive safely and write a text message -.19 (-.39–.01) -.13 .06

General attitude towards mobile phone use while driving -.15 (-.28–.01) -.14 .04

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messages while driving (e.g., [22,42]). Our overall findings make an important contribution to

the existing knowledge, suggesting that the frequency of mobile phone applications usage

should be considered alongside the standard mobile phone functions to identify the different

dimensions of this risky behaviour.

Our findings indicated that a large proportion of the participants would read and write a

text message using mobile phone applications while driving. Because the process of writing /

reading text messages using the existing applications, such as Viber and WhatsApp, is exactly

the same as writing / reading using a traditional text messaging function, this finding is partic-

ularly concerning (e.g., [13,15]). This is because when drivers are actively engaged in texting

behaviour, they look 400 times more away from the road [25,43]. In addition, nearly one third

of the drivers read and/or wrote an e-mail at least once while driving in the previous year. This

is concerning, given that this secondary task is as demanding on cognitive resources as writing

a text message. What remains unknown and requires further research, however, is an under-

standing of what kind of emails/text messages (e.g., work-related or personal) drivers tend to

deal with while driving a vehicle. For example, a study conducted by Porter and Kakabadse

[44] on the association between the use of technology and work addiction showed that those

individuals who are workaholics routinely use technology outside work hours / environment.

When applied to the driving context, this could possibly mean that those drivers who tend to

access their work emails / read text messages from outside work hours / environment may also

have an urge to do so while driving. Understanding the motivations across different mediums

will help design strategies to reduce distraction behaviour.

Nearly one quarter of the participants in our study had made a call using mobile phone

applications at least once in the past year. In contrast to this, a previous study by Sullman et al.

[22] employing a sample of the Ukrainian drivers showed that approximately 90% of partici-

pants reported making or answering a call at least once in the past year using a standard mobile

phone function. Given that making a call via a mobile phone application while driving requires

a sufficient Wi-Fi network connection, drivers may choose to use a standard phone call func-

tion over the applications simply because of the practicality. In addition, making a standard

call while driving could be considered a preferable option for drivers in terms of ease of use.

Reading and / or writing posts on social networks were found to be relatively common

amongst drivers, although approximately 70% of the sample reported they had never engaged

in this behaviour. This finding aligns with research from the UK [45] that found 24% of young

adults and 8% of adults admitted to using their mobile phones for social networking while

driving. This behaviour is of particular concern, as the results of a driving simulator study con-

ducted jointly by the IAM RoadSmart charity and the Transport Research Laboratory (TRL)

indicated that using a mobile phone for social networking increases reaction times by 37.6%,

while driving under the influence of cannabis, for instance, increases reaction times by 21%

[17]. One possible explanation for this finding could be that those individuals, who overuse

social networking applications on mobile phones in a day-to-day life, may not be able to con-

trol this behaviour in the driving context. Previous research commonly explains this type of

problematic phone use behaviour within the terms of mobile phone dependency (e.g., [31,46])

social media addiction (e.g., [47,48]), and technology addiction (e.g., [49]). In addition, mak-

ing and / or reading posts on social networks could be used by drivers as a way to deal with

their driving experience or to regulate emotion. For instance, Stephens et al. [50] found that

across a 13-month period, over 80,000 twitter posts # road rage were made and seemingly

while driving. A large amount of these included a 7- second video clip, filmed by the drivers in

situ.

Rather alarmingly, almost half of the drivers in our study reported taking a selfie while driv-

ing at least once in the last year. However, this finding is somewhat unsurprising as it is

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estimated that at least one million selfies are taken per day worldwide [51], which highlights

how prevalent this phenomenon is these days. In order to explain the reasons why people take

selfies in general, previous research has mostly concentrated on finding the associations

between this behaviour and various personality traits, such as narcissism (e.g., [52]), attention

seeking and low self-esteem (e.g., [53]). In the driving context, drivers may possibly take selfies

to reinforce their self-concept given that having a vehicle in Ukraine may still be considered to

be prestigious. In addition, as previously suggested by Stephens et al. [50] taking selfies may

also play a role in the processing of the driving experience or emotion regulation. Future

research should further explore the goals/motivations behind using social networking applica-

tions by drivers and how individual differences may impact their use.

The results of this study indicate that both general positive attitudes towards using mobile

phone while driving and approval of this behaviour by significant others were significantly

related to frequent mobile phone application usage amongst the Ukrainian drivers. This

includes sending a text message and making a call using mobile phone applications, reading /

looking at posts on social networks, writing and reading an email, as well as typing an address

into a map application. The results also showed that a positive attitude towards mobile phone

use while driving was related to frequent writing of emails and reading posts on social net-

works. This is somewhat consistent with previous research, in that drivers who had positive

attitudes towards mobile phone use and who perceive that their significant others would

approve of this behaviour tended to use standard mobile phone functions more frequently

while driving (e.g., [27,54,55]). This finding may indicate that drivers form positive attitudes

towards this dangerous behaviour despite being aware of the risk associated with engaging in

it while driving. In fact, previous research has shown that most drivers are aware of the risk of

using a mobile phone while driving (e.g., [56,57]); however, they still engage in this behaviour

as the perceived benefits of it may outweigh the associated risks [58]. In addition, being aware

of the risks does not necessarily serve as a disincentive factor when it comes to engaging in this

behaviour (e.g., [59]). As for the association between social approval and frequencies of mobile

phone usage while driving, this highlights the necessity of incorporating themes of social influ-

ence when developing interventions to tackle this dangerous behaviour more efficiently (e.g.,

[25]).

Our results also showed that those drivers, who had stronger beliefs about being able to

drive safely and engage in secondary tasks, used their mobile phones while driving more fre-

quently. This finding was supported by the results of the hierarchical stepwise regressions,

indicating that beliefs about being able to drive safely and write or read a text message affect

the frequency of using a mobile phone to take a selfie while driving, as well as typing an

address onto maps and making a call. This finding can be explained in two ways. Firstly, it

suggests that drivers might not always be able to detect and accurately judge changes in

their driving performance caused by simultaneously using a mobile phone and driving. In

addition, De Craen et al. [60] report that those drivers who inaccurately assess their driving

performance and skills tend to engage in driving and secondary tasks that are too cogni-

tively demanding and potentially unsafe. Secondly, most drivers tend to be overconfident in

their driving skills, which may subsequently lead to overestimating their multitasking

abilities.

Finally, in the current sample of the Ukrainian drivers, type A behaviour pattern did not

significantly predict any type of mobile phone applications usage. Although previous research

has reported there to be a strong link between the TABP and RTAs (e.g., [33,34]), our study

revealed that the reasons drivers use mobile phone applications while driving may only be

slightly related to their impatience and time urgency.

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Limitations

This study has several limitations. Firstly, the data were collected using self-report question-

naires, which may have been affected by social desirability bias. However, in order to mitigate

its influence, participants were assured of anonymity and confidentially, which is considered

an efficient measure against this phenomenon (e.g., [61]). Secondly, the vast majority of the

participants were males, which may reduce the ability to generalise these findings to the driv-

ing population of Ukraine. However, given that only 22% of Ukrainian drivers are females,

according to the Marketing Index TNS Global report [62], the study sample was comparatively

representative of the general population, in terms of the sex ratio. In addition, the study partic-

ipants were relatively young, which means that they might have been more familiar with smart

phones applications and therefore would be more likely to use them while driving. Thirdly,

although some of the participants were invited to take part in the study using a snowballing

technique, a large proportion of the sample were students / staff from the National Aviation

University and members of the Ukrainian motorists’ forum. It can be presumed that they may

significantly differ from the general population in terms of their socio-economic status, access

to technology and types of the mobile phones they use (smart phones versus older mobile

phones). Considering these points, future research should aim to examine the psychosocial

factors relating to the mobile phone applications use among a more representative sample.

Summary and practical implications

Despite the growing functionality of mobile phones and continuous introduction of new appli-

cations that significantly increase an individuals’ level of mobile phone involvement in day-to-

day life, there is little information regarding how these innovations affect the prevalence of

mobile phone use while driving. To our knowledge, this study is one of the first to report the

frequencies of mobile phone applications use, which was conducted using a sample of Ukrai-

nian drivers. Our results revealed that the majority of drivers would frequently read / write a

text message, type an address into maps and write / read an email while driving. Given the

known risk associated with writing / reading a text message, the frequency of these behaviours

is concerning. In addition, a large proportion of drivers reported haven taken a selfie while

driving in the past year; suggesting social media may have infiltrated the driving experience.

The engagement with various types of applications may pose additional risks to individuals’

driving performance, thereby potentially increasing the chances of crash involvement. The

reported frequencies highlight the need to include mobile phone applications when exploring

the “real” levels of mobile phone use when driving. In addition, and in line with previous road

safety research, positive attitudes, perceived social approval and lack of risk acceptance were all

positively related to mobile phone use. This suggests that future research should further

explore the goals/motivations behind using mobile phone applications while driving and how

individual differences may impact their use. Lastly, future studies should also explore the per-

ceived risks related to mobile phone applications usage while driving.

Author Contributions

Conceptualization: Amanda N. Stephens, Mark J. M. Sullman.

Data curation: Mark J. M. Sullman.

Formal analysis: Tetiana Hill, Amanda N. Stephens.

Investigation: Tetiana Hill.

Methodology: Amanda N. Stephens, Mark J. M. Sullman.

PLOS ONE Mobile phone applications use while driving in Ukraine

PLOS ONE | https://doi.org/10.1371/journal.pone.0247006 February 17, 2021 12 / 15

https://doi.org/10.1371/journal.pone.0247006

Project administration: Tetiana Hill.

Supervision: Mark J. M. Sullman.

Writing – original draft: Tetiana Hill.

Writing – review & editing: Amanda N. Stephens, Mark J. M. Sullman.

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Driving experience and situation awareness in hazard detection

Geoffrey Underwood a,!, Athy Ngai a, Jean Underwood b
a School of Psychology, University of Nottingham, Nottingham NG7 2RD, UK
b Division of Psychology, Nottingham Trent University, Nottingham NG1 4BU, UK

a r t i c l e i n f o

Article history:
Available online 19 July 2012

Keywords:
Hazard perception
Driving experience
Situation awareness
Motorcycle riders
Attentional capture
Anticipation of danger

a b s t r a c t

How does driving experience help in the development of situation awareness? A comparison of detection
rates of roadway hazards that involved other road users by inexperienced and experienced car drivers
and experienced motorcycle riders with car driving experience was conducted under laboratory condi-
tions. Motorcycle rider-drivers, due to their greater breadth of road experiences, were predicted to have
an advantage over other road users in the detection of hazards. Two types of hazards abrupt-onset events
and gradual-onset hazards were examined. Abrupt-onset events capture attention by virtue of sudden
movement and risk of impending collision, as when a pedestrian runs into the roadway for example. It
was expected that these hazards would be detected regardless of experience or situation awareness.
Gradual-onset hazards require the anticipation of unfolding events as recognition of the danger from chil-
dren playing on the footpath, and as such are a test of advanced situation awareness. The results showed
no inexperienced–experienced driver differences in the detection of either type of hazard, eliminating
hazard-type as an explanation of previous inconsistencies. However, motorcycle rider-drivers detected
gradual-onset hazards faster than car drivers and they also perceived more hazards in relatively safe sec-
tions of roadway, that is, they showed a higher level of situation awareness.

1. Introduction

There is cause for increasing concern over motorcycle rider
safety. In the USA, roadway fatalities have been decreasing in re-
cent years (a 22% reduction between 2005 and 2009), but motor-
cycle fatalities are increasing (National Highway Traffic Safety
Administration, 2010). An overall reduction of 9.7% in the year
2007–2008 was accompanied by an increase of 2.2% in motorcycle
fatalities. While there is a low prevalence of motorcycle riders (less
than 4% of licenced vehicles in the UK) they have a disproportion-
ately high involvement in road crashes and account for more than
20% of fatalities. A crash, however, often includes other road users
and so understanding both driver and rider behaviour is important.
It is now accepted that the off-road assessment of the road users’
skills in assessing the dangers of a roadway scene is clearly desir-
able, and hazard perception testing has been suggested as an
important component of the driver licencing process (Quimby
and Watts, 1981; Sexton, 2000).

1.1. Gaining situation awareness

The more varied roadway experiences of motorcycle riders who
also drive cars might be expected to provide them with an en-

hanced situation awareness as well as increased levels of safety
compared to other road users. While experienced rider-drivers
may have a more developed awareness of the events around them,
their crash involvement does not suggest that situation awareness
invariably leads to safety. It might be expected that the rider-driv-
ers’ awareness of their own vulnerability would provide them with
a developed awareness of the behaviour of other road users and of
the manoeuvering capabilities of a greater range of vehicles, but
this alone is insufficient to ensure their safety. If situation aware-
ness in the roadway context, is taken to be perception of the salient
elements of the environment (other road users, the roadway con-
figuration) and the projection of their status in the immediate fu-
ture (see Endsley, 1995; Stanton et al., 2001; Underwood, 2007),
then an experienced rider-driver would be expected to have good
anticipation of roadway events that develop into hazardous situa-
tions. The present laboratory study compares rider-drivers and
drivers using hazard perception videos that present unfolding
events that can be anticipated if the road user has a developed
sense of situation awareness, and sudden events that abruptly cap-
ture the attention of all road users.

1.2. Developing situation awareness

Driving experience does result in safer driving – fewer crashes –
and we have previously related this development of skill to the
extent of roadway scanning (Crundall and Underwood, 1998;

0925-7535/$ – see front matter ! 2012 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.ssci.2012.05.025

! Corresponding author. Tel.: +44 (0) 115 951 5313; fax: +44 (0) 115 951 5311.
E-mail address: geoff.underwood@nottingham.ac.uk (G. Underwood).

Safety Science 56 (2013) 29–35

Contents lists available at SciVerse ScienceDirect

Safety Science

journal homepage: www.elsevier.com/locate/ssci

Underwood et al., 2002, 2003). Inexperienced drivers search the
roadway to a lesser extent than more experienced drivers, possibly
as a function of their under-developed situation awareness. On
safer, uneventful roads the scanning of the two groups of drivers
has been shown to be similar, but on an urban motorway with
other road users merging from both directions, the experienced
drivers demonstrate their situation awareness by searching the
roadway around them more extensively. As drivers’ interactions
with other road users increase over time and they negotiate a
greater range of events, their situation awareness develops and
they learn when they need to be especially attentive and increase
their scanning. Accordingly, road users with more extensive and
varied experience should develop greater situation awareness,
and motorcycle riders with car driving experience should be one
such a group of road users, as they will have encountered traffic
situations from a greater range of perspectives. Using a motorcycle
simulator in which both dynamic and static hazards were pre-
sented, Hosking et al. (2010) reported that more experienced mo-
torcycle riders do indeed change their visual search behaviour
more than inexperienced riders when a hazard is present. Experi-
ence was also associated with faster responses to the hazards.
These differences in laboratory behaviour are reflected in results
of crash analyses. Magazzù et al.’s (2006) analysis of 742 crashes
found that car drivers holding a motorcycle licence tended to be
less responsible for car-motorcycle crashes than drivers who were
not motorcycle riders. Riders were more aware of the manoeuver-
ing capabilities of motorcycles but there may also be an effect of a
personal interest in motorcycles resulting in a greater awareness of
their presence. Drivers are more likely to be involved in car-motor-
cycle crashes than are car drivers who are also riders, suggesting
the two groups are differentially aware of roadway dangers.

1.3. Testing situation awareness

In the Hosking et al. (2010) study testing took place while vol-
unteers rode a motorcycle simulator. Although this is more realis-
tic than the traditional procedure for assessing hazard perception,
and goes someway to answering criticisms of ecological validity, it
may have caused some measure of distraction from the detection
task. More traditional tests of hazard perception characteristically
require the viewer to watch a video filmed from a vehicle travelling
along a roadway, and in which other road users move towards a
pathway that would result in a collision if avoiding action is not ta-
ken. The viewer signals the required action, such as braking or
steering away from the other road user, by pressing a response
button, or alternatively moving a lever on a sliding scale marked
‘‘safe’’ to ‘‘unsafe’’. While differences between inexperienced and
experienced drivers have been reported, the pattern of such differ-
ences is not consistent. One purpose of the present study is to dis-
tinguish between two types of hazards in an attempt to account for
this inconsistency. A second purpose is to include in the compari-
son a group of experienced motorcycle rider-drivers, as road users
who are sometimes described as being exceptionally skilled and
who would be expected to perform particularly well relative to
car-only road users.

Early studies suggested a relationship between performance on
a laboratory test of hazard perception and performance on the
road. Pelz and Krupat (1974), for example, found that individual
drivers who either self-reported as having been crash-involved or
who had received a traffic violation citation during the previous
year, behaved less cautiously in a hazard perception test. Safer
drivers responded sooner to the onset of a hazardous situation,
and they maintained a more cautious setting on a sliding scale that
required continuous adjustment according to the danger inherent
in the situation. Other studies reviewed by Horswill and McKenna
(2004) have found a similar relationship between crash involve-

ment and responses to hazard perception videos, giving validity
to this off-road assessment of driving ability.

Not all studies have found a relationship between accident lia-
bility and hazard detection, however, and this perhaps explains
why hazard perception testing has not been adopted widely. Com-
parisons of individuals with varying experience should report an
advantage for experienced drivers over newly qualified drivers,
but this is not always the case. While some studies have found fas-
ter response times to hazards for experienced drivers (e.g. Quimby
and Watts, 1981; McKenna and Crick, 1991; McKenna et al., 2006;
Wallis and Horswill, 2007; Smith et al., 2009) other studies have
failed to replicate this result (e.g., Chapman and Underwood,
1998; Crundall et al., 2003; Sagberg and Bjørnskau, 2006; Borow-
sky et al., 2010). The question arises, then, as to why this result is
difficult to replicate. One possibility involves the types of hazards
used in the videos, and the present study investigates differences
between abrupt hazards that capture attention, and gradually
developing hazards that involve anticipation of what might hap-
pen as events unfold. Abrupt hazards capture attention effortlessly
and immediately, whereas the recognition of gradual-onset haz-
ards is sensitive to situation awareness. The question of failed rep-
lications is important because if we can discriminate between
inexperienced drivers who are at greater risk in comparison with
those who are likely to be safer drivers, then we will have an
instrument for inclusion in the driver testing and licencing
process.

The videos used in our early studies had a mixture of hazard
types, having been filmed in preparation for inclusion in the re-
vised UK driving test (1999). As well as the range of roads reported
by Chapman and Underwood (1998), with country roads eliciting
slower responses than urban roads (a result confirmed by Hosking
et al., 2010), the videos varied in the extent to which they required
the viewer to anticipate upcoming events. McKenna et al. (2006)
placed emphasis on anticipation in the detection of hazards, but
many of the movie clips used in the Chapman and Underwood
(1998) study did not require any anticipation. These hazards can
be described as involving the abrupt capture of attention by an
exogenous event. Such hazards involved another road user appear-
ing suddenly – a pedestrian stepping from behind a parked vehicle,
or a car door opening, or a previously off-camera cyclist moving
rapidly into the path of the camera car (see Fig. 1b). Hazards such
as these were under consideration for inclusion in the UK driving
test and so they were included in our earlier evaluation, but they
are tests more of the viewer’s speed of reaction than of their ability
to assess a scenario and to predict how the situation will develop.
Other videos showed unfolding events involving, for example, one
car braking as a second car entered the road from a side-road, with
both cars being visible for several seconds before braking occurred,
or the appearance of another car heading towards the camera car
on a narrowing road. The example shown in Fig. 1a gives the view-
er sight of a pedestrian getting into a parked car in the distance,
and the car then indicating that it is about to enter the road ahead
of the camera car. In these cases the need to brake or steer away
from another road user is predictable, and the only decision con-
cerns when the action should be necessary. These hazards may
be characterised as being sensitive to high-order situation aware-
ness (see Horswill and McKenna, 2004; Underwood, 2007) in that
they require the driver to predict alternative future scenarios
emerging from the current interaction between road users and to
consider preparations for evasive action. By including some haz-
ards that required anticipation and others that captured attention
as they appeared, our earlier study inevitably increased the vari-
ance in the distributions of hazard response times, and this may
have masked differences between groups of drivers. One of the
purposes of the present comparison is to separate responses to
these different categories of hazards.

30 G. Underwood et al. / Safety Science 56 (2013) 29–35

Videos showing abrupt-onset hazards were compared with
those showing gradual-onset hazards, to assess differences be-
tween inexperienced and experienced drivers. Experienced driv-
ers have been found to make faster responses to hazards in
some studies (for a recent example, see Wetton et al., 2010),
and we predicted an advantage for experienced drivers but with
gradual-onset hazards only. A third group of road-users was
introduced, to extend the range of abilities tested. Motorcycle
rider-drivers have sometimes been claimed to have better haz-
ard judgement than car drivers, and have sometimes been
shown to perform better than experienced drivers in laboratory
tests. Underwood and Chapman (1998) found that motorcyclists
with 15 years of riding/driving experience responded to hazards
half a second faster than car drivers with similar experience, a
result confirmed by Horswill and Helman (2003) and by Hos-
king et al. (2010). Riders are regularly heard to claim, infor-
mally, that they have better roadway awareness than car
drivers, and we therefore predicted faster hazard detections
by experienced riders in the present study, and again predicted
that this difference would be apparent only with the gradual-
onset hazards.

2. Method

2.1. Design

This study used a mixed-model experimental design with one
between – groups factor of road users with three levels (inexperi-
enced drivers; experienced drivers; rider-drivers) and one within-
groups factor of type of hazard with two levels (abrupt onset; grad-
ual onset).

2.2. Participants

Drivers and riders were recruited from a range of sources,
including the University population, driving schools in Notting-
ham, and local motorcycle associations. Participants were all paid
an inconvenience allowance. The road users were allocated to
one of three groups: 25 inexperienced car drivers aged 18–24 years
and who had completed their preliminary training but who had a
maximum of two years of post-licence driving (mean of 1.25 years
of post-licence driving); 38 experienced car drivers aged 19–
75 years and with at least two years of driving (mean of
11.41 years driving), and 30 motorcycle riders aged 20–66 years
who had at least two years of riding (mean of 14.30 years of rid-
ing). None of the drivers had motorcycling experience, whilst all
of the motorcyclists also held a car driving licence (at least two
years of car driving).

2.3. Materials and procedure

The hazard perception videos were filmed from the driver’s
perspective in a car travelling along different road types (e.g. rural
scenarios, residential areas) and were sampled from the library of
videos previously used by Chapman and Underwood (1998),
(Crundall et al., 1999), and Underwood et al. (2003, 2005). The mo-
vie clips each lasted between 20 and 95 s, with an average of 50 s,
and included at least one potentially hazardous situation (with a
maximum of three hazardous events per clip). The definition of a
hazard was presented as ‘anything that would cause you to con-
sider taking action (e.g. braking or steering) to prevent a possible
danger’. Participants were not informed about the number of haz-
ardous events in each clip, and as the clip lengths varied, they
were not able to predict the possible hazards without paying
attention to the videos. Twenty videos contained at least one
abrupt hazard, and 20 contained at least one gradual onset hazard,
and some of the clips contained both types of hazards. Examples of
still frames taken from these videos are shown in Fig. 1. A further
five videos, containing both abrupt and gradual onset hazards,
were used for practice before the experiment started. The hazards
all involved other road users – another vehicle that pulled out into
the path of the camera-car, or a pedestrian that stepped onto a
marked road crossing, or a cyclist that prompted a change of direc-
tion by pulling into the car’s path. The hazards were classified
according to how long the other road user was in view before an
action became necessary. For example, children that run into the
roadway in front of the camera car could be an abrupt hazard if
they appear suddenly from behind a parked bus, or they could
be a gradual onset hazard if they have been stood at a pedestrian
crossing for a while as the camera-car approaches. The start time
for both hazards was the first appearance on the screen of the haz-
ardous object, for example another car or a child. Road users (such
as a child pedestrian) who were visible for less than 3 s before
becoming hazardous and requiring action were classified as having
an abrupt onset (mean time on screen before action would be nec-
essary was 1.88 s, SD = 0.73), and those that were visible for at
least 5 s were classified as having a gradual onset (mean of 8.0 s,
SD = 3.34).

Fig. 1. Examples of frames taken from gradual and abrupt onset hazard videos.
In the gradual onset movie here (frame a) the nearest parked car on the left
has previously been seen to have a passenger entering, and it is now
signalling that it intends to enter the roadway. It proceeds to do so, ahead of
the camera car. In this case the interval between the passenger being seen to
get into the car, and the car entering the roadway, is approximately 7 s. In the
abrupt onset movie (frame b) the cyclist on the left appears from the side the
building, and enters the roadway ahead of the camera car, without pausing.
The interval between the cyclist being first visible on the screen, and the
cyclist entering the roadway, is approximately 2 s. In both cases the camera
car must brake.

G. Underwood et al. / Safety Science 56 (2013) 29–35 31

The hazard videos were presented from an Apple iMAC G5 with
a 17-inch monitor and were played through a purpose-written pro-
gram. The program showed the videos and recorded button press
responses. The participant was seated approximately 60 cm from
the computer screen. The task required participants to respond
to any potential hazards by tapping the spacebar on the computer
keyboard, as soon as they were detected. The time of each button
press was recorded relative to the start of the onset of each hazard.
No feedback was given and each movie clip continued for its full
course, regardless of how many button press responses were made.
The 5 practice clips were shown, participants’ questions answered,
and then the 40 test clips shown in a new random order for
each participant. All of the hazard movies were shown to all
participants.

3. Results

A series of 3 ! 2 ANOVAs were conducted, with road users and
type of hazard as the independent variables. The principal depen-
dent measures were the response latency to the first appearance
of each hazard and the percentage of hazards detected, for each
group of road users. These data are shown in Figs. 2 and 3. A hazard
was declared as being correctly detected if the response was made
while the hazard was visible on the screen – while a pedestrian
was still in sight or when the brake lights of a leading car were still
illuminated. This hazard window varied in duration for each
hazard.

The three measures of response time, percentage of hazards de-
tected, and hazard detection difference ratio were submitted to
separate analyses of variance, each with one between-groups fac-
tor (road users) and one within-group factor (type of hazard). For
response times, there were differences between road users
(F(2,90) = 4.49, MSE = 4.44, p < .05), and between hazard types (F(1,90) = 487.15, MSE = 191.86, p < .001). There was also an inter- action between these factors (F(2,90) = 4.23, MSE = 1.67, p < .05). Pairwise t-tests indicated that motorcycle riders made faster re- sponses to hazard onsets than both the inexperienced drivers (p < .05) and the experienced drivers (p < .01). Abrupt onset haz- ards gained faster responses than gradual onset hazards, and the interaction was first inspected with an analysis of simple main ef- fects. There was no difference between the three groups of road users in their responses to abrupt onset hazards (F < 1), but there was a substantial difference in the responses to gradual onset haz-

ards (F(2,180) = 8.50, MSE = 5.88, p < .001). With gradual onset hazards only, pairwise tests found faster responses for rider-driv- ers relative to both inexperienced drivers (p < .01) and experienced drivers (p < .01). The difference between inexperienced and experi- enced drivers was not statistically reliable.

The ANOVA applied to the percentage of hazards correctly de-
tected again found a difference between abrupt and gradual onset
hazards (F(1,90) = 6.21, MSE = 0.012, p < .05), with more gradual onset hazards being detected, but no effect of road user (F(2,90) = 2.59, MSE = 0.008), and no interaction (F(2,90) = 1.72, MSE = 0.003).

We also recorded the number of button presses (‘‘hazard’’
detections) made when there was no designated hazard present
(Fig. 4). This is a measure of the sensitivity of the road user to
the dangers associated with driving or riding, with someone who
is highly aware of potential dangers from other road users ex-
pected to generate high numbers of button presses out of the des-
ignated hazard window. A one factor ANOVA was used to analyse
these rates, finding a marginal effect of road user (F(2,90) = 2.77,
MSE = 10.17, p = .067). This effect is a product of the motorcycle ri-
der-drivers making more button press responses than the other
two groups. Using this measure of false alarms and the successful
hazard detection rates we derived a difference ratio to indicate the
sensitivity of the participants to the presence of actual hazards. The
ratio was designed to emphasise correct detections relative to the
number of false alarms, and took the form:

“False Alarms # Hits$=” False Alarms % Hits$ & D

Fig. 2. Response times (means and standard errors) to the first appearance of a
hazard, for three groups of road users and for two types of hazards.

Fig. 3. Percentages (means and standard errors) of abrupt-onset and gradual-onset
hazards detected by three groups of road users.

Fig. 4. False alarm rates (means and standard errors) by three groups of road users,
for all videos. These are button presses (hazard detections) when no designated
hazard was shown.

32 G. Underwood et al. / Safety Science 56 (2013) 29–35

A high value of D for an individual road user would be indicative
of a relatively large number of false alarms for each correct hazard
detection, with a value approaching +1 if there were very few hits
and a large number of false alarms. The highest values from indi-
vidual participants here were D = 0.839 (abrupt hazards) and
D = 0.855 (gradual hazards). A low value of D would be obtained
by an individual making very few false alarms, which would render
a value of D that approached #1. The lowest values observed were
D = #0.769 (abrupt hazards) and D = #0.754 (gradual hazards). A D
value approaching 0 would be obtained by making approximately
the same number of false alarms as correct detections.

These difference ratios are shown in Fig. 5, and are an alterna-
tive to the more traditional measure of sensitivity generated by
the application of signal detection theory, whose assumptions are
not met by the present data. As has been pointed out by Parasur-
aman et al. (2000) and by Wallis and Horswill (2007), correct hits
(a button press during a hazard) and false alarms (button presses at
other times) are readily identifiable only when they form non-
over-lapping distributions whereas driving situations regularly
present degrees of danger. The calculation of a false alarm rate is
straightforward only in that the number of false alarms per movie
can be found (the means ranged from 0.15 to 5.97 per movie), but
assigning these values to either abrupt or gradual onset hazards is
not possible, given that some clips contained both types of hazard.
Incorrect misses can be identified simply enough (no button press
during a designated hazard window), but a correct rejection also
presents a difficulty because it corresponds to the continued ab-
sence of a button press when no hazard is being shown, and is
therefore not a discrete, measurable event. Correct rejections can
be classified on the basis of the opinions of expert drivers, but even
then calculation is not straightforward. For example, if the camera
car is travelling along an empty country road, with good sight of
the road ahead, and no walls or hedges to occlude the possible en-
try of other road users into the car’s path, then it may be reason-
able to declare that there are no potential hazards, and so no
events to reject. On the other hand, if the car is filming along a busy
urban street with cars parked on both sides of the road, and pedes-
trians walking along both footpaths, then it may be reasonable to
declare that there is a huge number of potential hazards that
should be rejected. The number of correct rejections will vary from
scenario to scenario, and from moment to moment in each scenario

depending on the potential for a hazard to develop. For these rea-
sons we opted not to use the measures provided by the fuzzy signal
detection theory suggested by Parasuraman et al. (2000) and by
Wallis and Horswill (2007), and to rely instead on the ratio of hits
to false alarms. The difference ratio D used here makes no assump-
tions about the hazard-like characteristics of segments of the vid-
eos, and records a value between #1 and +1 according to the ratio
of hazard detections to other button presses.

The difference ratios (D) summarised in Fig. 5 were also submit-
ted to a two-factor ANOVA, which found a main effect of road users
(F(2,90) = 3.40, MSE = 1.20, p < .05), with pairwise tests indicating that experienced drivers showed greater selectivity (lower D ratio) than the riders (p < .01). There were no other pairwise differences. There was also a main effect of hazard type (F(1,90) = 8.34, MSE = 0.003, p < .01), with abrupt hazards having higher difference ratios than gradual hazards. There was no interaction (F(2,90) = 2.37, MSE = 0.001) between the two factors.

4. Discussion

We predicted that the road users with more extensive and more
varied experience would have an advantage with hazards that
tested their anticipation of unfolding events when the precursors
of a hazard were visible for a few seconds before evasive action be-
came necessary. There were differences between the road users in
their responses to hazards in the roadway videos shown in the
experiment, but not the differences that were expected.

The motorcycle riders did not respond to the hazard videos in
the same way as either group of car-only drivers. They responded
faster to the onset of anticipation hazards than both inexperienced
drivers and experienced drivers, confirming an advantage for riders
reported previously (Underwood and Chapman, 1998; Horswill
and Helman, 2003; Hosking et al., 2010). This difference was only
apparent with gradual-onset hazards; abrupt onset hazards did
not differentiate between road users, confirming their attention-
capturing status. Riders detected as many of the hazards as the
other road users, but they tended to make more button-press re-
sponses (false alarms) overall, and there was an interesting com-
parison with the difference ratio D. As can be seen in Fig. 5,
riders have higher D scores than the experienced drivers, who
did not differ from the inexperienced drivers. The rider-drivers
made a larger number of responses during the relatively safer se-
quences of the videos (Fig. 4), indicating a greater inclination to de-
clare any given situation as being more hazardous than did the
drivers. Overall these rider-drivers both responded faster and more
often than the other road users, suggesting both greater caution
generally and faster detection of actual hazardous situations.

Motorcycle riders are much more likely than car drivers to be
involved in a fatal road crash, even though they cover less distance
travelled (e.g., National Highway Traffic Safety Administration,
2006; European Transport Safety Council, 2007; Department for
Transport, 2010). They are over-represented in the crash statistics,
and when they do crash the consequences are more serious for
them than for car drivers. As well as needing to be aware of other
road users that are generally of greater mass, they also need to be
more aware of discontinuities in the road surface than do car driv-
ers. Perhaps it is this vulnerability that results in a cautious view-
ing of road scenes, with more hazards being perceived by riders
than by equally experienced drivers. Following the Pelz and Krupat
(1974) analysis we would conclude that the high number of false
alarms relative to correct hazard detections indicates that riders
are more aware of roadway dangers than other road users. Aware-
ness of their own vulnerability requires riders to have enhanced
situation awareness. Shahar et al. (2010, Expt. 1) found a similar
result by asking riders and drivers to rate written vignettes that
described roadway events. Riders rated the scenes as being more

Fig. 5. Difference ratios (means and standard errors) for three groups of road users
and for two types of hazards. This ratio takes account of the mean number of button
presses made outside of the hazard window, and ranges between +1 and #1, with
higher ratios representing larger number of non-hazard button presses relative to
the detection of pre-defined hazards.

G. Underwood et al. / Safety Science 56 (2013) 29–35 33

dangerous than did the drivers. There is an apparent inconsistency
here, because if this analysis is correct, with the roadway as being
perceived as dangerous more by riders than by drivers, then a
question is raised by the observations reported by Horswill and
Helman (2003). As well as reporting faster hazard detection re-
sponses by riders, they also found that riders had more risky atti-
tudes towards high speeds, and had greater tolerance of small gaps
when pulling into a line of traffic and when overtaking using a
video-presented judgement task. In their on-road observations
Horswill and Helman also recorded motorcycles travelling at higher
speeds than cars. Acceptance of high riding speed is experience-
dependent however, and using a motorcycle simulator, Liu et al.
(2009) found more cautious behaviour in more experienced riders.
They approached hazards more slowly than inexperienced riders,
and crashed at hazards less often. Riders appear to perceive the
roadway as being more dangerous and yet their attitudes and
behaviours indicate a greater tolerance of risk.

The higher D ratios shown by the motorcycle riders here indi-
cates that they were perceiving the roadway as being more hazard-
ous generally than the car drivers, and this is consistent with the
report from the motorcycle simulator study by Shahar et al.
(2010, Expt. 2), in which riders were judged to be safer as well as
more skilful than a matched group of car drivers. The experiences
of riders may have resulted in these road users being more sensi-
tive to potential danger. A study of decision-making with photo-
graphs of roadway scenes also demonstrates that riders are more
cautious than drivers when judging whether it would be safe to en-
ter a main road at a t-junction (Underwood et al., 2011).

One concern of the experiment was to establish whether differ-
ences between hazard types had introduced so much variance in
previous investigations that inexperienced–experienced driver dif-
ferences had been lost through a Type 2 error, and we will now fo-
cus on differences between the two groups of car drivers. Chapman
and Underwood (1998), Crundall et al. (2003), and Sagberg and
Bjørnskau (2006) have reported failures to find any difference in
the reaction time made in response to the onset of a hazard,
whereas other studies have found an advantage for experienced
drivers (e.g., Quimby and Watts, 1981; McKenna and Crick, 1991;
McKenna et al., 2006; Wallis and Horswill, 2007; Smith et al.,
2009). The possibility investigated here is that only gradual-onset
hazards that require anticipation would reveal individual driver
differences, and that abrupt, attention-capturing hazards would at-
tract similarly fast responses for all road users. The abrupt-onset
hazards here certainly attracted faster responses (mean of 1.79 s)
than the gradual-onset hazards (3.87 s), and for all drivers the var-
iance estimates were smaller (as indicated by the standard error
bars in Fig. 2), but the predicted difference between inexperienced
and experienced drivers still did not emerge with either type of
hazard.

The second measure of performance was the number of hazards
detected (hits), and again there was no difference between inexpe-
rienced and experienced drivers. The derived measure D, a differ-
ence ratio that takes into account the relative numbers of false
alarms and correct detections, was intended to identify partici-
pants who made large numbers of ‘‘hazard’’ detections. This is re-
lated to a pattern reported by Pelz and Krupat (1974), who found
that accident- and violation-involved drivers tended to regard all
periods of the videos as being safer than did non-involved drivers.
On this basis we would expect safer, more experienced drivers to
make more button-press responses than the inexperienced drivers,
but this pattern was not seen here. It was the motorcycle riders
who made high numbers of hazard detections during the safer sec-
tions of the videos. Values of D were similar for both groups of car
drivers, suggesting that they were adopting similar criteria for
reporting a hazard. The pattern of inconsistency in reports of
hazard detection performance continues then, and we cannot re-

ject the possibility that drivers do not differ in the speed of their
responses to hazards of all types. This conclusion lends support
to Groeger’s (2000) suggestion that hazard perception testing has
poor face validity, but it does not explain why some studies have
successfully differentiated between inexperienced and experi-
enced drivers on the basis of their responses to hazard videos.

Why should there be an inconsistency in the appearance of the
inexperienced–experienced driver effect in the detection of haz-
ards while watching roadway videos? It is important to resolve this
problem, given the use of hazard perception elements in national
and state driving tests and given that other bodies have the use
of hazard perception under review. We need to know whether
the detection of hazards does discriminate on the basis of experi-
ence and ability, or whether it is an artifact of some other individ-
ual difference. It should be noted that the inconsistency in the
appearance of the effect is not due to differences in the definition
of inexperience, with perhaps only very young or very inexperi-
enced drivers showing impairment. A failure to find a difference
has been found in previous studies of drivers with less than a year
of experience (Chapman and Underwood, 1998; Sagberg and
Bjørnskau, 2006; Borowsky et al., 2010) but other studies have
found differences when drivers with up to 3 years of experience
were tested (Wallis and Horswill, 2007; Smith et al., 2009; Wetton
et al., 2010). The inexperienced drivers tested here had a maximum
of 2 years of driving but showed no disadvantage for the detection
of either type of hazard, ruling out the experience of inexperienced
drivers as the reason that we failed to find the effect. A second pos-
sible source of the inconsistency in the inexperienced–experienced
driver effect in hazard perception may be associated with differ-
ences in visual scanning. There are now a number of reports of
inexperienced drivers demonstrating reduced scanning of the
roadway and of reduced scanning of videos showing roadway
scenes (Crundall and Underwood, 1998; Falkmer and Gregersen,
2001; Underwood et al., 2002, 2003; Borowsky et al., 2010). So if
hazard perception videos differ in the location of the emergent haz-
ard does this account for inexperienced drivers detecting some
hazards more slowly than experienced drivers. If a hazard occurs
in the central field – the lead car braking, for example – then there
may be a smaller difference between types of drivers compared to
the situation when a peripheral hazard such as a pedestrian steps
from behind a parked car. As inexperienced drivers scan the road-
way less than experienced drivers it is to be expected that they
would be slower at detecting hazards approaching from the side
of the visual field. Experienced drivers scan the roadway more
extensively – while driving and while watching videos – and so
they should detect potentially hazardous road users more quickly
and more often, Thus if successful reports of a driver difference
in the literature have used peripheral hazards and unsuccessful re-
ports have used centrally placed hazards, then this would be one
possible explanation of the inconsistency. This possibility requires
future investigation.

One final variable may be relevant for future investigations. In a
study of young pedestrians Underwood et al. (2007) found no over-
all difference in male and female recognition of risk in road scenes,
although males in the age group under investigation were far more
likely to be involved in road accidents. However, they did find a dif-
ference in the focus of attention, with males focusing on physical
environmental factors while the females centred their attention
on people when assessing risk. This difference occurred across
age groups, suggesting that gender may be another reason for dis-
crepancies between studies of hazardous risk.

The two kinds of hazards used here successfully differentiated
between road users. Abrupt-onset hazards captured attention
promptly, with both drivers and rider-drivers responding as
quickly and as accurately as each other. The gradual-onset hazards
required the anticipation of unfolding events, and were regarded as

34 G. Underwood et al. / Safety Science 56 (2013) 29–35

a test of advanced situation awareness. Motorcycle rider-drivers
had an advantage over both groups of drivers in the detection of
these hazards, and they also perceived more hazards in relatively
safe sections of roadway suggesting that their riding experiences
have resulted in an enhanced awareness of roadway dangers.

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G. Underwood et al. / Safety Science 56 (2013) 29–35 35

Research Article
Implementing Surrogate Safety Measures in Driving Simulator
and Evaluating the Safety Effects of Simulator-Based Training o

Risky Driving Behaviors

Eunhan Ka,1 Do-Gyeong Kim,2 Jooneui Hong,3 and Chungwon Lee

1Institute of Engineering Research, Seoul National University, Seoul 08826, Republic of Korea
2Department of Transportation Engineering, University of Seoul, Seoul 02504, Republic of Korea
3Korea Development Institute, Sejong 30149, Republic of Korea
4Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Republic of Korea

Correspondence should be addressed to Chungwon Lee; chungwon@snu.ac.kr

Received 26 December 2019; Accepted 23 April 2020; Published 19 June 202

Academic Editor: Inhi Kim

Copyright © 2020 Eunhan Ka et al. )is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Human errors cause approximately 90 percent of traffic accidents, and drivers with risky driving behaviors are involved in about
52 percent of severe traffic crashes. Driver education using driving simulators has been used extensively to obtain a quantitative
evaluation of driving behaviors without causing drivers to be at risk for physical injuries. However, since many driver education
programs that use simulators have limits on realistic interactions with surrounding vehicles, they are limited in reducing risky
driving behaviors associated with surrounding vehicles. )is study introduces surrogate safety measures (SSMs) into simulator-
based training in order to evaluate the potential for crashes and to reduce risky driving behaviors in driving situations that include
surrounding vehicles. A preliminary experiment was conducted with 31 drivers to analyze whether the SSMs could identify risky
driving behaviors. )e results showed that 15 SSMs were statistically significant measures to capture risky driving behaviors. )is
study used simulator-based training with 21 novice drivers, 16 elderly drivers, and 21 commercial drivers to determine whether a
simulator-based training program using the SSMs is effective in reducing risky driving behaviors. )e risky driving behaviors by
novice drivers were reduced significantly with the exception of erratic lane-changing. In the case of elderly drivers, speeding was
the only risky driving behavior that was reduced; the others were not reduced because of their difficulty with manipulating the
pedals in the driving simulator and their defensive driving. Risky driving behaviors by commercial drivers were reduced overall.
)e results of this study indicated that the SSMs can be used to enhance drivers’ safety, to evaluate the safety of traffic management
strategies as well as to reduce risky driving behaviors in simulator-based training.

1. Introduction

)e worldwide number of annual fatalities in traffic crashes
reached 1.35 million each year, and this number continues to
increase steadily in the world [1]. Human errors cause about
90 percent of all road accidents [2], and the majority of
human errors involve risky driving. Drivers with risky
driving behaviors such as speeding, following other vehicles
too closely (tailgating), erratic driving, and violation of
traffic laws accounted for about 52% of severe traffic acci-
dents [3]. Moderating risky driving behaviors have been
achieved successfully using a variety of approaches that

combine education, engineering, and enforcement; this
approach to safety is known as the 3E principle [4]. Driver
education has been used extensively to reduce risky driving
behaviors. It has been reported to be an effective way to
reduce traffic accidents by detecting risky driving behaviors
and providing appropriate feedback to reduce these be-
haviors [5]. Risky driving behaviors should be measured and
evaluated quantitatively to give appropriate feedback to
drivers in order to reduce risky driving behaviors.

Current driver education programs have focused on
educating drivers about the skills and attitudes necessary to
become a safe driver. Videos and lectures about traffic

Hindawi
Journal of Advanced Transportation
Volume 2020, Article ID 7525721, 12 pages
https://doi.org/10.1155/2020/7525721

mailto:chungwon@snu.ac.kr

https://orcid.org/0000-0002-2845-500

https://creativecommons.org/licenses/by/4.0/

https://doi.org/10.1155/2020/7525721

regulations and automobile-related knowledge, on-road
training, and simulator-based training generally have been
used in driver education programs. Videos and lectures help
drivers acquire knowledge about driving safely by providing
information about traffic regulations and the appropriate
operation of automobiles. However, these approaches to
teaching drivers have limitations in that they do not help
improve the practical skills that are required in on-road
driving [6]. On-road training with a driving instructor is an
effective method to educate drivers to drive more safely on
the road. However, even professional instruction and on-
road training cannot address all of the potential crashes of
driving because they cannot expose drivers to the various
potential collision situations that can occur on the road.

Driving simulators are used extensively as a tool to
instruct drivers to drive in a common driving environment
as well as in collision situations that would be too dan-
gerous to create in actual on-road driving [7]. )e in-
structor can design various driving scenarios including
myriads of road and traffic environments, movements of
surrounding vehicles, and collision scenarios. )erefore,
driving simulators can be used to give risky drivers re-
peated training with various collision situations. Driving
simulators can be used to measure driving behaviors
quantitatively as well as to acquire the trajectory data for
surrounding vehicles [8]. However, there are issues con-
cerning the validity of virtual simulations of real driving
environments. Current studies have shown that they have
similar patterns, but the driving behaviors in driving
simulators and on-road driving may not be the same [7, 9].
In other words, driving simulators can be useful tools for
educational purposes in driver education programs. In fact,
the driving instructors involved in a previous study thought
that one-hour simulator training was as effective as three
hours of on-road training [10].

Most research on reducing risky driving behaviors based
on driving simulators has been conducted with a focus on
drivers’ eye movements and the movements of the subject
vehicle, i.e., movements such as erratic acceleration and
deceleration, speed variation, and lane deviation [11–14].
Since risky driving behaviors cause severe road crashes, it is
necessary to evaluate the crash potential in the interactions
between the subject vehicle and surrounding vehicles, such
as following leading vehicles (car-following) and changing
lanes (lane-changing). However, few studies have evaluated
the crash potential between the subject vehicle and sur-
rounding vehicles, which would address the interactions
between vehicles [15]. )is study implemented realistic
interactions between the subject vehicle and surrounding
vehicles in a driving simulator by applying traffic flow
models to the movements of surrounding vehicles. In ad-
dition, we examined surrogate safety measures (SSMs),
which are used extensively in the field of road safety as useful
measures for assessing crash potential or severity even on
roads where no actual collisions have occurred. )e

SSMs

can increase our understanding of the situations that cause
collisions. In this study, the SSMs were used to evaluate risky
driving behaviors in order to evaluate vehicles’ crash
potentials.

)e aim of this study was to determine whether the SSMs
can identify risky driving behaviors in driving simulators
and whether the SSMs are effective in improving drivers’
behaviors when the SSMs are used as evaluation measures in
the simulator-based training.

2. Literature Review

Risky driving behaviors are defined differently by many
organizations and in many studies. Since the motivation of a
driver is difficult to determine, risky driving behaviors can
only be judged and evaluated based on the motions of ve-
hicles [16, 17]. Risky driving behaviors mean taking risks
that endanger the safety of both the driver and other road
users [18]. Generally, risky driving behaviors include
speeding, noncompliance with traffic laws, tailgating,
reckless changing speeds, erratic lane-changing, and threats
to other drivers (yelling and horn honking) [3, 16, 19].

Driving simulators have been used increasingly for
driver education because of the advantages they provide,
including the freedom to present drivers with a wide variety
of scenarios without any threat to their safety or the safety of
other people [20]. Studies on reducing or evaluating risky
driving behaviors using driving simulators have investigated
mainly risky driving behaviors in terms of the drivers’ re-
actions and the movements of vehicles. Studies of drivers’
reactions have used the movements of the eyes, the focus of
gazes, the duration of glances, and the number of fixations as
measures to evaluate drivers’ physical responses to collision
situations [11, 14, 21]. )ese studies have shown that drivers’
perceptions of conflict situations improve after they have
had driver education in which eye-tracking systems to
improve the ability of novice drivers and older drivers to
recognize situations where collisions could occur. Various
studies have used response time, pressure on the accelerator,
and pressure on the brake pedal to measure drivers’ re-
sponses to collision situations and red lights at intersections
[12, 13, 22]. Measures related to drivers’ reactions were used
mainly to evaluate risk perception rather than to reduce
risky driving behaviors.

Most of the studies related to the movements of vehicles
have focused on the movements of the subject vehicle, and
they evaluated primarily the risky behaviors of the drivers of
the subject vehicle, e.g., erratic steering control, speeding,
and tailgating. Erratic steering control involves the driver’s
sudden and unexpected changes in steering the vehicle or
how far the driver allows the vehicle to deviate from the
center of its lane. )e steering angle, steering reversal rate,
lane deviation, and mean lane position have been used in
assessing erratic steering control [13, 14, 22–24]. Velocity,
mean speed, speed variation, speeding, and acceleration are
speeding-related measures that can be used to determine the
driver’s compliance with the speed limit and the reckless
changing of speed [13, 14, 22, 25]. Evaluations of the gap
distance between vehicles and time-to-collision (TTC) with
a leading vehicle were made mainly in a car-following sit-
uation [15, 22]. However, the gap distance between vehicles
and the TTC between the subject vehicle and the leading
vehicle cannot take into consideration the accelerations and

2 Journal of Advanced Transportation

decelerations of either vehicle. Also, few studies have con-
sidered the potential for crashes between vehicles when they
are changing lanes [26].

Most studies have attempted to evaluate the effects of
simulator-based training on risky driving behaviors. )ese
studies focused principally on risky driving behaviors related
to the movements of the subject vehicle. Since most risky
driving behaviors require consideration of the subject ve-
hicle’s interactions with surrounding vehicles, it is essential
to evaluate the crash potential with one or more of the
surrounding vehicles. However, research considering the
interactions between vehicles has rarely been conducted
because the movements of surrounding vehicles would not
be implemented realistically. )is study attempted to in-
troduce the SSMs into a simulator-based training program
to evaluate the crash potential between vehicles and to re-
duce the risky driving behaviors associated with the sur-
rounding vehicles.

3. Methodology

3.1. Framework. )is study consisted of three parts: a survey
of SSMs and scenario design, a preliminary experiment, and
a simulator-based training program (see Figure 1). )e SSMs
can be classified into measures that consider only the subject
vehicle and measures that consider both the subject vehicle
and surrounding vehicles. Before the SSMs were used as
measures of driving behaviors, it was necessary to test
whether the SSMs could detect risky driving behaviors and
conservative driving behaviors in a driving simulator. )is
study conducted a preliminary experiment for the sensitivity
analysis of SSMs. )e purpose of the sensitivity analysis of
SSMs was to ensure that SSMs could detect extreme driving
behaviors, i.e., normal, conservative, and risky driving. In a
preliminary experiment, each driver was required to engage
in one of the three types of driving behaviors (normal,
conservative, and risky) in the driving simulator. Finally, this
study used a quantitative evaluation based on the SSMs to
analyze whether drivers reduced their risky driving be-
haviors after engaging in the simulator-based training
program.

3.2. Survey of Surrogate Safety Measures. Since a simulator-
based training requires immediate feedback concerning
which driving behaviors are risky in the various driving
scenarios, it is crucial to be able to calculate the SSMs used in
the simulator-based training within a short time after
driving in the driving simulators. )is study reviewed nu-
merous studies about road safety in order to investigate the
SSMs that can be used in simulator-based training, and 31
SSMs were selected as alternatives. Since 11 of the SSMs were
challenging to calculate instantaneously in driving simula-
tors or unsuitable in evaluating driving behaviors, 20 out of
the 31 SSMs were selected as implementable SSMs. For
example, the Crash Index is a measure concerning the se-
verity of a potential crash, and it is presented in the form of
the kinetic energy of the crash [27]. It is challenging to
translate kinetic energy values into an easily understandable

account of the risk associated with a given participant’s
driving behaviors. )us, this study excluded the Crash Index
from the implementable SSMs and selected implementable
SSMs as measures that can be explained easily to the drivers
in simulator-based training.

)e implementable SSMs were divided into “Relating to
the subject vehicle” and “Relating to surrounding vehicles,”
depending on whether or not the SSMs related to interac-
tions with surrounding vehicles. )e SSMs relating to the
subject vehicle can be calculated without any interactions
with surrounding vehicles (nos. 1 to 9 in Table 1). )e SSMs
relating to surrounding vehicles consider interactions with
surrounding vehicles, such as car-following situations and
lane-changing situations (nos. 10 to 20 in Table 1). )e gap
distance in the car-following situation (no. 10) was used to
confirm the validation of driving errors between the driving
simulator and on-road driving [7]. )e time-to-collision
(TTC, no. 12) was used for studies in which driving be-
haviors in critical situations were compared [15, 22, 35].
However, the gap distance and the TTC between the subject
vehicle and the leading vehicle have the limitation that the
difference between the acceleration of the subject vehicle and
the deceleration of the leading vehicle cannot be considered.
)is study used modified TTC (no. 13) and deceleration rate
to avoid crash (no. 14) to evaluate the crash potential by
considering the difference between the acceleration of the
subject vehicle and the deceleration of the leading vehicle in
situations where the subject vehicle is following the leading
vehicle. )e study in which the driving behaviors were
analyzed in the lane-changing situation used the gap dis-
tance (no. 16 and no. 17) with the surrounding vehicles in a
target lane [26]. )is study adopted the SSMs (nos.13, 14, 19,
and 20) that had not been used to reduce risky driving
behaviors in existing driving education programs to assess
the crash potential between the subject vehicle and sur-
rounding vehicles properly. )e contribution of this study is
to secure the effectiveness of driver education by capturing
interactions between a subject vehicle and surrounding
vehicles based on the simulator-based training using SSMs
and then ultimately induce the prevention and reduction of
road accidents.

)e SSMs consist of measures with a single outcome for
the dataset and measures with continuous outcomes cal-
culated at every time step of the dataset. Accumulated
speeding (AS), speed uniformity (SU), speed variation (SV),
acceleration noise (AN), and lane deviation were measured
with a single outcome and calculated after completing the
driving scenarios. Measures with continuous outcomes
should be transformed into a single representative value in
order to evaluate how risky the drivers’ driving behaviors
were.

A single representative value of SSMs can be obtained
either as the maximum (minimum) value of total outcomes,
such as Max S, i.e., the maximum velocity in a conflict
situation [33] or as the ratio of conflicts defined as exceeding
the threshold value of each measure [27, 36]. )is study
adopted the minimum value as the representative value for
SSMs related to lane-changing situations (see nos.15 to 20 in
Table 1). )e method to define the threshold value for

Journal of Advanced Transportation 3

3.2 Survey of surrogate
safety measures

Subject vehicle
Subject vehicle and
surrounding vehicles

(i)

(ii)

31 drivers (preliminary
experiment)
58 drivers (simulator-
based training)

(i)
(ii)

Specification of
driving simulator
Scenario design

(i)
(ii)

3.3 Driving simulator
and scenario design

3.4 Preliminary
experiment

Normal driving
Conservative driving
Risky driving

(i)
(ii)

(iii)

Driving before
intervention
Intervention (feedback)
Driving after
intervention

(i)

(ii)
(iii)

3.5 Simulator-based
training

3.6 Participants

Figure 1: Framework of the study.

Table 1: Description of implementable surrogate safety measures.

Relation with
surrounding vehicles

No. Surrogate safety measure Unit Description

Relating to the subject
vehicle

1 Accumulated speeding (AS) kph
)e normalized relative area (per unit length) bounded between the
speed profile values higher than the speed limit and the speed limit

line [28]

2 Speed uniformity (SU) kph
)e normalized relative area (per unit length) bounded between the

speed profile and the average speed line [28]
3 Speed variation (SV) kph )e standard deviation of the speed
4 Acceleration (%) m/s2 )e acceleration of the subject vehicle
5 Deceleration (%) m/s2 )e deceleration of the subject vehicle
6 Acceleration noise (AN) m/s2 )e root mean square deviation of the acceleration [29]
7 Lane deviation m )e standard deviation of lane position [30]
8 Yaw rate (%) °/s )e rotational velocity around the z-axis of the subject vehicle [31]
9 Lane change (%) — )e number of lane change manoeuvres completed

Relating to
surrounding vehicles

10 Gap distance (%) (GD) m
)e longitudinal distance along a travelled way between one vehicle’s

leading surface and another vehicle’s trailing surface

[32]

11
Proportion of stopping
distance (%) (PSD)

—
)e ratio of the distance available for manoeuvring to that of the
necessary stopping distance to a projected point of collision [33]

12 Time-to-collision (%) (TTC) sec
)e time interval required for one vehicle to strike another object if
both objects continue on their current paths at their current speed

[32]

13 Modified TTC (%) (MTTC) sec
)e time interval required for one vehicle to strike another object if
both objects continue on their current paths at their current speed

and acceleration [32]

Deceleration rate to avoid

crash (%) (DRAC)
m/s2

)e deceleration required by the following vehicle to come to a timely
stop or attain a matching lead vehicle speed to avoid a rear-end crash

[34]

15 Min_Front_GD m
)e minimum value of gap distance (GD) with leading vehicle of

current

lane in lane-changing situation

16 Min_Lag_GD m
)e minimum value of gap distance (GD) with lag vehicle of target

lane in lane-changing situation

17 Min_Lead_GD m
)e minimum value of gap distance (GD) with leading vehicle of

target lane in lane-changing situation

18 Min_Front_TTC sec
)e minimum value of time-to-collision (TTC) with leading vehicle

of current lane in lane-changing situation

19 Min_Lag_TTC sec
)e minimum value of time-to-collision (TTC) with lag vehicle of

target lane in lane-changing situation

20 Min_Lead_TTC sec
)e minimum value of time-to-collision (TTC) with leading vehicle

of target lane in lane-changing situation

4 Journal of Advanced Transportation

counting the number of conflicts in each of the SSMs was
derived from the existing literature [37]. )e 85th percentile
value of total participants’ driving data distribution was used
as the threshold for SSMs for which higher values indicated
more risky driving behaviors (acceleration (%), yaw rate (%),
lane change (%), gap distance (%), and proportion of
stopping distance (%)). For SSMs for which lower values
indicated more risky driving behaviors (deceleration (%),
time-to-collision (%), modified TTC (%), and deceleration
rate to avoid crash (%)), the 15th percentile value of total
participants’ driving data distribution was used as the
threshold.

3.3. Driving Simulator and Scenario Design. )e driving
simulator used in this study was mounted on a six-degree-
of-freedom motion system, with a size of
3500 × 3500 × 3500 mm. )e visual system for the driving
simulator consisted of three 43-inch full HD LED monitors,
providing a 150-degree field of view with a resolution of
5760 ×1080 pixels and a 60 Hz refresh rate. )e virtual
environment with various driving conditions was repre-
sented through the three monitors, with rear-view and side-
view mirrors visible on the center monitor and side mon-
itors, respectively (Figure 2(a)). )e vehicle dynamics were
validated based on the real motion of the Hyundai Sonata.

A part of the street grid in Seoul was implemented in a
virtual environment in order to enhance the reality of the
driving environment. )e total length of the designed route
in the scenario was 10.1 km, including freeway (2.3 km),
urban roads (6.0 km), and rural roads (1.8 km) (Figure 2(b)).
)e freeway consisted of the main freeway segment with a
posted speed limit of 110 kph and an off-ramp. )e urban
roads included ten signal intersections located every
200–400 m on a four-lane two-way road with a speed limit of
60 kph. )e rural roads were either two- or four-lane, two-
way roads with a speed limit of 80 kph.

)e movements of surrounding vehicles significantly
determine the mental load and ability to drive a vehicle. If
the movements of the surrounding vehicles were not real
enough, there is a possibility that drivers will drive a vehicle
differently than they would in actual driving, meaning that
the results and conclusions obtained from the simulation
would not be applicable in actual driving. Many studies
using driving simulators have been limited in expressing
realistic movements because the movements of the vehicles
were very strictly controlled to assess the drivers’ abilities in
certain crash situations [38]. )erefore, if the movements of
the surrounding vehicle are unrealistic and strictly con-
trolled irrespective of the movements of the subject vehicle,
it would be difficult to expect the reduction of risky driving
behaviors in actual driving on the road through simulator-
based training. In order to implement the realistic inter-
actions with surrounding vehicles, traffic flow models (i.e., a
car-following model, a lane-changing model, and a gap-
acceptance model) were modeled based on video data and
vehicle trajectory data, and then, they were applied to the
movements of the surrounding vehicles. Using the traffic
flow models had the additional benefit of showing different

movements in each trial, thereby increasing the sense of
reality and preventing participants from adapting to the
scenario [38]. )e generalized model of car-following was
estimated with data obtained from random vehicles on the
West-Hanam IC and the West-Icheon IC of the Jungbu
Highway [39]. )e lane-changing model was implemented
based on the vehicle trajectory data measured by nine video
cameras in the upper 400 m section of the Middle East IC of
the Seoul Ring Expressway for discretionary and mandatory
lane-changing. )e parameters of a logit model were esti-
mated with the gap distance and the speed of the subject
vehicle as independent variables. )e logit model was es-
timated for the gap-acceptance model at the intersection.
Data were collected using video cameras at six intersections
in Seoul to estimate the parameters of the gap-acceptance
model, and the data included the time gap, type of vehicle,
and traffic volume. )is study estimated the parameters of
the logit models for an unprotected left turn, an unprotected
right turn, and a roundabout using collected data.

3.4. Preliminary Experiment for Extreme Driving Behaviors
with SSMs. )is study conducted a preliminary experiment
to analyze the sensitivity of SSMs for extreme driving be-
haviors. Before participating in the preliminary experiment,
the participants were shown how to control a driving
simulator and performed one to three minutes of practice
driving to prevent simulator sickness and to adapt to the
virtual environment of the driving simulator. To use the
SSMs that could measure the crash potential of surrounding
vehicles in a driving simulator, the sensitivity analysis of the
SSMs was required to determine whether they could capture
risky driving behaviors. In this study, the experimental
methods that had been used in previous studies were used to
analyze the sensitivity of the SSMs to extreme driving be-
haviors. Past participants were involved in a study of ex-
treme driving behaviors that compared the difference in fuel
consumption depending on driving behaviors and com-
pared the difference in the performance of an urban network
on driving behaviors [40, 41].

Participants in the preliminary experiment were asked to
drive “normally,” “conservatively,” or “riskily.” In normal
driving, the participants drove the way they usually drive. In
the conservative driving condition, the participants were
asked to maintain a greater safe following distance, accel-
erate and decelerate as gently as possible, and keep their
speed under the speed limit. In risky driving, the participants
were required to complete their driving route within

minutes rather than the typical 15 minutes, to follow the
leading vehicle more closely than the recommended safe
distance, and to change lanes and the speed of the vehicle
erratically.

3.5. Simulator-Based Training to Improve Driving Behaviors.
)is study used SSMs that statistically could capture risky
and conservative driving within the simulator-based train-
ing conducted by the Korea Transportation Safety Authority
(KOTSA). )e simulator-based training consisted of three
parts, i.e., driving before the intervention, intervention

Journal of Advanced Transportation 5

(feedback based on results of driving behaviors), and driving
after the intervention.

Before the intervention, the participants drove an in-
troduction drive for 1 to 3 minutes to become accustomed to
the control of the driving simulator and the virtual envi-
ronment. Subsequently, they drove the driving scenario as
they usually would do, allowing the instructor to examine
the extent of their risky driving behaviors.

)e intervention consisted of two parts: feedback with a
video replay of the driver’s driving and a commentary video.
)e instructors provided feedback to the participants con-
cerning how risky they drove in terms of six risky driving
behaviors, i.e., speeding, reckless changing speed, rapid
acceleration and deceleration, erratic steering control, tail-
gating, and erratic lane-changing. )e speeding, reckless
changing speeds, rapid acceleration and deceleration, and
erratic steering control only assessed the movements of the
subject vehicle. In contrast, the tailgating and erratic lane-
changing assessed the crash potential with the surrounding
vehicles in the normal driving environment. In typical
drivers’ education programs, the instructor evaluates the
drivers’ driving behaviors based on the movements of the
subject vehicle and the crash potential with the surrounding
vehicles in specific situations (i.e., speeding, reckless
changing speeds, rapid acceleration and deceleration, and
erratic steering control). In the simulator-based training
using the SSMs of this study, the instructor informed the
drivers the risky driving behaviors, including situations in
which they were following a vehicle and changing lanes in a
common driving environment and were riskier than other
drivers. In other words, the contribution of this study is to
evaluate the movements of the subject vehicle as well as the
interactions between vehicles by identifying the risky driving
behaviors such as tailgating in car-following situations and
erratic lane-changing in lane-changing situations. Also, the
instructor educated the drivers about safe methods for
driving on the road to reduce the crash potential between the
vehicles. In the commentary video, videos of actual crashes

attributable to each of the six risky driving behaviors were
shown to encourage safe driving.

After the feedback session at the end of the intervention,
the drivers were asked to drive again so that their driving
behaviors could be observed in order to determine whether
their driving behaviors had been improved; i.e., whether
their risky driving behaviors were reduced. )e instructor
showed the participants how much their driving had im-
proved in terms of the frequency of six risky driving be-
haviors and to encourage safe driving.

3.6. Participants. For this study, we posted advertisements
and recruited 43 participants for the preliminary study. Out
of the 43 participants, 12 participants dropped out of the
experiment due to simulator sickness, leaving 31 partici-
pants, i.e., 21 males and 10 females. )e average age of the
participants was about 36 years old, and the average driving
experience of participants was almost 13 years.

Existing research on simulator-based training was
mainly conducted with novice, elderly, and commercial
drivers [13, 20, 23, 42, 43] because drivers in these three
groups tend to have higher accident rates, making them the
primary targets for improving risky driving [44–46]. Par-
ticipants in the simulator-based training were recruited
separately from the three driver groups by posted adver-
tisements. Out of 69 participants, 11 participants dropped
out of the experiment due to simulator sickness, leaving 58
participants, i.e., 21 novice drivers, 16 elderly drivers, and 21
commercial drivers. )e average age of the participants was
approximately 46 years old, and the average driving expe-
rience of participants was slightly more than 20 years. )e
demographic statistics of the participants are provided in
Table 2.

4. Results

4.1. Results of the Sensitivity Analysis for Extreme Driving
Behaviors with SSMs. In this study, we conducted a

(a)

Freeway (2.3 km)
Urban (6.0 km)

Rural (1.8 km)
Toll plaza

Finish

Start

(b)

Figure 2: Driving simulator and scenario in this study: (a) the driving simulator set-up; (b) the designed route in the scenario.

6 Journal of Advanced Transportation

sensitivity analysis for extreme driving behaviors to test
whether SSMs could significantly distinguish between
normal, conservative, and risky driving. Twenty SSMs
were analyzed for extreme driving behaviors for 31
drivers. )e SSMs were analyzed for the entire road
section and the freeway section because the lane-changing
in the urban road section was forced due to the direction
of the designed travel route, in contrast to the discre-
tionary lane changes in the freeway section. In this study,
driving behaviors except lane-changing were evaluated
over the entire road section, but lane-changing was
evaluated only for the freeway section. Also, an ANOVA
test and a post-hoc test (Tukey HSD) at the 95% signif-
icance level were performed to evaluate whether the SSMs
could detect significant differences in extreme driving
behaviors across the different conditions.

Differences were not statistically significant for five SSMs,
i.e., lane deviation, DRAC, Min_Front_GD, Min_Lead_GD,
and Min_Lag_TTC (Table 3). Lane deviation and DRAC,
which evaluate driving behavior over the entire road section,
did not exhibit significant differences. Few studies have
compared lane deviations for different degrees of extreme
driving behaviors. However, one previous study found that
there was no significant difference in the mean of lane de-
viation before and after training [25]. In contrast to GD, TTC,
and DRAC, the measure of minimum deceleration to avoid a
collision has been known to be limited to reflect a conflict
situation, and drivers may fail to adjust DRAC to avoid a
conflict situation [47].

When a driver changes lanes on the freeway, the
subject vehicle enters the target lane at a higher speed than

the front vehicle in the current lane and the lead vehicle in
the target lane. Min_Front_GD and Min_Lead_GD be-
come shortened when changing lanes, and the driver
changed lanes in a situation in which the driver main-
tained the distance between the surrounding vehicles
necessary for changing lanes. Regardless of the driver’s
extreme driving behaviors, Min_Front_GD and Min_-
Lead_GD were not significantly different. However,
Min_Front_TTC and Min_Lead_TTC, which reflect the
relative speed differences between vehicles, showed sta-
tistically significant differences because the entry speed in
risky driving is higher than that in conservative driving.
Min_Lag_GD showed a statistically significant difference
because drivers changed lanes at a shorter gap distance in
risky driving and a longer gap distance in conservative
driving than in normal driving. In the scenario of this
study, the lag vehicle in the target lane decelerated when
the subject vehicle entered the target lane because the car-
following model was applied to the movements of sur-
rounding vehicles. )erefore, there was no statistically
significant difference between the extreme driving be-
haviors of Min_Lag_TTC due to the decrease in the speed
of the lag vehicle even though the distance between the
subject vehicle and the lag vehicle in the target lane was
shorter in risky driving.

4.2. Results of Improvement for Risky Driving Behaviors with
SSMs. In this study, we classified 15 SSMs into six types of
risky driving behaviors to improve the driver’s under-
standing of SSMs in an intervention of simulator-based

Table 2: Demographics of the participants in this study.

Type
Preliminary experiment (n � 31) Simulator-based training (n � 58)

N Percent (%) N Percent (%)
Age
20–29 13 41.94 20 34.48
30–39 7 22.58 2 3.44
40–49 6 19.35 4 6.90
50–59 2 6.45 16 27.59
≥60 3 9.68 16 27.59

Gender
Male 21 67.74 43 74.14
Female 10 32.36 15 25.8

Driving years
≤2 years 6 19.35 21 36.21
3–20 years 16 51.61 5 8.62
21–39 years 8 25.81 25 43.10
≥40 years 1 3.23 7 12.07

Crash experience
None 26 83.87 42 72.41
1 2 6.45 4 6.90
2 3 9.68 11 18.97
≥3 0 0.00 1 1.72

Traffic violation
None 24 77.41 37 63.79
1 5 16.13 13 22.41
2 1 3.23 4 6.90
≥3 1 3.23 4 6.90

Journal of Advanced Transportation 7

training. Fifty-eight drivers were provided with feedback on
their driving behaviors based on 15 SSMs in simulator-based
training, and they were taught how to reduce their risky
driving behaviors.

4.2.1. Comparison among Novice, Elderly, Commercial, and
Typical Drivers. Before analyzing the effect of simulator-
based training using SSMs on the improvement of driving
behaviors, it was necessary to identify how risky the drivers
participating in the simulator-based training were. )is is
because drivers who had safe driving behaviors were less likely
to improve their driving behaviors, even though they were
trained. In this study, 31 drivers who participated in the
sensitivity analysis experiment were selected as typical drivers
to compare with the three driver groups, i.e., novice, elderly,
and commercial drivers. Because the 31 drivers were recruited
randomly irrespective of gender, age, and driving experience,
the risky driving behaviors of the three driver groups before
the intervention were analyzed and compared based on the
normal driving of typical drivers. )e larger the values of
Min_Lag_GD, Min_Front_TTC, and Min_Lead_TTC were,
the safer the drivers’ driving behaviors drivers were. )e
smaller the values of the other SSMs were, the safer the driving
behaviors of the drivers were (see Figure 3).

Novice drivers were found to be riskier than typical
drivers except for Min_Lag_GD and Min_Lead_TTC (see
Figure 3). Novice drivers have poor driving skills because of
low driving experience [48]. Since novice drivers have diffi-
culty in maintaining a safe distance from a leading vehicle
because of their lack of driving experience, they were inclined
to tailgate leading vehicles to a greater extent than typical
drivers. Also, novice drivers had limited in-advance per-
ception of dangerous situations, and therefore, it also was
difficult for them to maintain a constant speed [43].

Elderly drivers were found to be riskier than typical
drivers in the cases of deceleration, AN, and yaw rate. In
contrast, other measures of elderly drivers were similar to or
safer than that of typical drivers (see Figure 3). )ese results
were consistent with the results that elderly drivers do not
perceive the brake pedal pressure in the driving simulator as
accurately as young drivers [43].

Commercial drivers were found to be riskier than
typical drivers except for lane change and four SSMs
related to tailgating (see Figure 3). )is suggests that
commercial drivers tend to be more risky drivers than
typical drivers to save time, but maintaining a safe dis-
tance from a leading vehicle is essential for drivers of
heavy trucks and buses [42].

4.2.2. Comparison of before and after Intervention by Driver
Group. )e SSMs used as evaluation measures were sta-
tistically tested at the 95% significance level with paired t-
tests to determine whether drivers’ behaviors improved
before and after the training in simulator-based training (see
Table 4). In the case of novice drivers, Min_Lag_GD and
Min_Front_TTC in lane-changing situations showed that
the improvement before and after the training was not
statistically significant. With the exception of AS, acceler-
ation, deceleration, and AN, elderly drivers did not show
statistically significant improvements after the training. In
the case of commercial drivers, there were statistically sig-
nificant differences for all measures except for Min_-
Front_TTC in lane-changing situations. )e improvement
in driving behaviors after the training was the greatest in
commercial drivers and the least in elderly drivers.

In the case of novice drivers, the improvements in
speeding, rapid acceleration and deceleration, and tailgating

Table 3: Summary of statistics for surrogate safety measures in sensitivity analysis.

No. SSMs
Entire road section Freeway section

ANOVA Post-hoc ANOVA Post-hoc
F-value p-value p-value F-value p-value p-value

1 AS F(2, 90) � 33.12 0.00

0.00

2 SU F(2, 90) � 35.73 0.00 0.00
3 SV F(2, 90) � 37.69 0.00 0.00
4 Acceleration F(2, 90) � 16.80 0.00 0.00
5 Deceleration F(2, 90) � 39.24 0.00 0.00
6 AN F(2, 90) � 28.41 0.00 0.00
7 Lane deviation F(2, 90) � 0.41 0.66 —
8 Yaw rate F(2, 90) � 30.77 0.00 0.00
9 Lane change F(2, 90) � 7.71 0.00 0.00
10 GD F(2, 90) � 29.05 0.00 0.00
11 PSD F(2, 90) � 29.58 0.00 0.00
12 TTC F(2, 90) � 16.22 0.00 0.00
13 MTTC F(2, 90) � 14.28 0.00 0.00
14 DRAC F(2, 90) � 2.91 0.06 —
15 Min_Front_GD F(2, 90) � 2.95 0.06 —
16 Min_Lag_GD F(2, 90) � 11.74 0.00 0.00
17 Min_Lead_GD F(2, 90) � 2.76 0.07 —
18 Min_Front_TTC F(2, 90) � 6.33 0.00 0.04
19 Min_Lag_TTC F(2, 90) � 0.16 0.85 —
20 Min_Lead_TTC F(2, 90) � 8.87 0.00 0.01

8 Journal of Advanced Transportation

0
2
4

A
S

Typical Novice Elderly
Driver group

Commercial

(a)
Typical Novice Elderly
Driver group
Commercial

10
14

(b)
Typical Novice Elderly
Driver group
Commercial

(c)

Typical Novice Elderly
Driver group
Commercial

0.10

0.15

0.20

0.25

0.30

0.35

A
cc

el
er

at
io

(d)

Typical Novice Elderly
Driver group

Commercial

0.05

0.10
0.15
0.20
0.25

D
ec

el
er
at
io
n

(e)

Typical Novice Elderly
Driver group

Commercial
1.5

2.0

2.5

3.0

3.5

A
N

(f)

Typical Novice Elderly
Driver group
Commercial

100

0.125

0.1

Ya
w

ra
te

(g)

Typical Novice Elderly
Driver group
Commercial

La
ne

c
ha

ng
e

30
35

(h)

Typical Novice Elderly
Driver group
Commercial
0.05
0.00
0.10
0.15
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G
D

(i)
Typical Novice Elderly
Driver group
Commercial

0.0

0.1

0.2

PS
D

(j)

Typical Novice Elderly
Driver group
Commercial
0.1
0.2

T
T

(k)

Typical Novice Elderly
Driver group
Commercial
0.10
0.15
0.20
0.25
0.30
0.05

M
T

T
C

(l)

Typical Novice Elderly
Driver group
Commercial

M
in

_L
ag

_G
D

0
100

200

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(m)

Typical Novice Elderly
Driver group
Commercial
M
in

_F
ro

nt
_T

T
C
0
25
50
75
100

(n)

Typical Novice Elderly
Driver group
Commercial
M
in

_L
ea

d_
T

T
C
0
25
50
75
100

(o)

Figure 3: Comparison of surrogate safety measures by driver group: (a) AS (speeding); (b) SU (reckless changing speeds); (c) SV (reckless
changing speeds); (d) acceleration (rapid acc. and dec.); (e) deceleration (rapid acc. and dec.); (f) AN (rapid acc. and dec.); (g) yaw rate (erratic
steering control); (h) lane change (erratic steering control); (i) GD (tailgating); (j) PSD (tailgating); (k) TTC (tailgating); (l) MTTC (tailgating); (m)
Min_Lag_GD (erratic lane-changing); (n) Min_Front_TTC (erratic lane-changing); (o) Min_Lead_TTC (erratic lane-changing).

Journal of Advanced Transportation 9

were significant, but no significant improvements were
observed in erratic lane-changing (see Table 4). )rough the
simulator-based training, most novice drivers improved
their ability to maintain a safe distance from the leading
vehicle. However, they did not show improvement in
keeping a safe distance from adjacent vehicles in lane-
changing situations. )erefore, although novice drivers
improved following the simulator training in the car-fol-
lowing situation, additional training should be provided for
maintaining a safe distance between vehicles when changing
lanes.

Elderly drivers showed improvement in speeding, rapid
acceleration, and deceleration (see Table 4). Since elderly
drivers tend to be more defensive than other driver groups,
they showed the least improvement in risky driving be-
haviors of the three driver groups. Compared to the
younger drivers, elderly drivers were less aware of the brake
pedal pressure in a driving simulator during deceleration.
)ey showed a tendency to decrease speed rapidly in the
driving simulator before the intervention. After the in-
tervention, the deceleration behavior of elderly drivers was
improved because the instructors requested that they begin
their deceleration earlier to prevent erratic deceleration.
However, there is a limit to the conclusions that the
simulator-based training improved the erratic decelerating
behavior of elderly drivers since their erratic deceleration
behavior may have resulted from their use of the driving
simulator.

After the intervention, the driving behaviors of com-
mercial drivers were improved in all 15 SSMs. )e im-
provements in speeding, rapid acceleration, rapid
deceleration, and tailgating were more significant than the
other risky driving behaviors (see Table 4). Since the
commercial drivers had shown more risky driving behaviors
than other groups of drivers, their driving behaviors were
improved to a greater extent by the simulator-based training
than driving behaviors of drivers for other groups. )ese

results showed that the simulator-based training program is
effective in reducing risky driving behaviors of various driver
groups by providing feedback on how risky their driving
behaviors are.

5. Conclusions

)is study implemented SSMs in a simulator-based
training program to evaluate the crash potential with
surrounding vehicles. )e movements of surrounding
vehicles need to be realistic to consider the interactions
with surrounding vehicles in driver education. Traffic flow
models developed from data collected on real roads were
implemented for the movements of surrounding vehicles
(car-following, lane-changing, and gap-acceptance at
intersection). Twenty SSMs were implemented in the
driving simulator. )e preliminary experiment that was
conducted with 31 participants verified that 15 SSMs
could be used to capture risky driving behaviors. )e 15
selected SSMs were used as the measure for current
simulator-based training in the Republic of Korea to
evaluate the driving behaviors of novice, elderly, and
commercial drivers.

After the intervention of the simulator-based training,
the risky driving behaviors of novice drivers, elderly
drivers, and commercial drivers were reduced in different
ways. In the case of novice drivers, additional on-road
training was required to reduce risky driving behaviors in
lane-changing situations. For elderly drivers, the speeding
and rapid acceleration behaviors were improved. However,
other risky driving behaviors were not statistically reduced
because elderly drivers already drive vehicles safely so that
there was nothing to improve significantly except for
speeding, rapid acceleration, and deceleration. )e training
using the driving simulator reduced the risky driving be-
haviors of commercial drivers. )e reason that simulator-

Table 4: Summary of statistics for surrogate safety measures in before and after intervention.

Types of risky driving
behaviors

SSMs

Novice Elderly Commercial
(n � 21) (n � 16) (n � 21)

μbefore μafter t-stat μbefore μafter t-stat μbefore μafter t-stat
(p-value) (p-value) (p-value)

Speeding AS 3.54 1.48 5.41 (0.00) 0.71 0.29 2.46 (0.03) 2.83 0.42 5.54 (0.00)

Reckless changing speeds
SU 15.03 12.97 4.10 (0.00) 13.51 12.73 1.29 (0.22) 15.50 13.37 5.20 (0.00)
SV 30.62 25.53 6.34 (0.00) 23.00 21.86 1.92 (0.07) 28.70 23.50 9.19 (0.00)

Rapid acceleration and
deceleration

Acceleration 0.24 0.15 6.94 (0.00) 0.21 0.17 2.28 (0.04) 0.24 0.14 8.70 (0.00)
Deceleration 0.17 0.11 6.07 (0.00) 0.14 0.12 2.46 (0.03) 0.17 0.10 7.01 (0.00)

AN 2.97 2.27 6.20 (0.00) 2.80 2.49 3.19 (0.01) 2.95 2.04 8.54 (0.00)

Erratic steering control
Yaw rate 0.14 0.12 2.85 (0.01) 0.13 0.12 1.95 (0.07) 0.14 0.12 3.57 (0.00)

Lane change 39.38 36.43 1.89 (0.07) 35.94 33.69 1.35 (0.20) 37.10 33.90 3.36 (0.00)

Tailgating

GD 0.14 0.07 3.84 (0.00) 0.02 0.02 0.08 (0.94) 0.09 0.04 4.32 (0.00)
PSD 0.19 0.10 4.42 (0.00) 0.04 0.03 0.56 (0.58) 0.12 0.06 5.07 (0.00)
TTC 0.21 0.17 2.51 (0.02) 0.07 0.06 1.04 (0.31) 0.14 0.10 4.39 (0.00)
MTTC 0.24 0.15 4.39 (0.00) 0.17 0.14 1.77 (0.10) 0.18 0.10 4.67 (0.00)

Erratic lane-changing
Min_Lag_GD 27.75 49.99 −0.88 (0.39) 79.01 90.95 −0.29 (0.78) 5.42 84.24 −2.45 (0.02)

Min_Front_TTC 76.71 91.01 −0.30 (0.77) 117.18 76.87 0.89 (0.39) 27.97 127.39 −2.78 (0.05)
Min_Lead_TTC 17.09 41.00 −2.66 (0.02) 58.17 60.47 −0.15 (0.88) 16.38 48.78 −2.78 (0.01)

10 Journal of Advanced Transportation

based training was most effective for reducing the risky
driving behaviors for commercial drivers was that their
behaviors were risky compared to the behaviors of other
groups of drivers.

)e results of this study showed that SSMs could be used
both for road safety and traffic management strategies and
for the evaluation of individual drivers’ driving behaviors in
driver education. However, there were two limitations in this
study that should be addressed in future research. First, the
possibility that adaptation to manipulating a driving sim-
ulator after the intervention has a positive effect on reducing
risky driving behaviors cannot be ruled out. )is study did
not compare drivers trained in simulator-based training
using SSMs with drivers trained in the previous simulator-
based training. In this study, there are improvements in
various driving behaviors by giving the drivers feedback
using SSMs, but it is possible that intervention without SSMs
also could contribute to reducing risky driving behaviors.
)erefore, future research should determine the extent to
which the intervention based on feedback using SSMs
contributes to reducing risky driving behavior compared to
existing simulator-based training. Second, it is unclear
whether simulator-based training using SSMs will result in
the reduction of risky driving behaviors in actual driving.
)is study only analyzed instantaneous training effects in
simulator driving. Future research should examine how
simulator-based training using SSMs reduces risky driving
behaviors in actual driving. )erefore, it is necessary to study
whether the trends of SSMs are the same by comparing
drivers in the existing simulator-based training with those in
the simulator-based training proposed in this study by
comparing actual driving data with data from the driving
simulator.

Data Availability

)e data used to support the findings of this study are
available from the corresponding author upon request.

Conflicts of Interest

)e authors declare that there are no conflicts of interest
regarding the publication of this paper.

Acknowledgments

)is research was supported by the Institute of Construction
and Environmental Engineering and the Institute of Engi-
neering Research at Seoul National University. )e authors
wish to express their gratitude for their support.

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12 Journal of Advanced Transportation

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articles for individual use.

Research Article
Effect of High-Altitude Environment on Driving Safety: A Study on
Drivers’ Mental Workload, Situation Awareness, and
Driving Behaviour

Xinyan Wang,1 Wu Bo ,1,2 Weihua Yang,1 Suping Cui,1 and Pengzi Chu

1School of Engineering, Tibet University, Lhasa 850000, China
2School of Transportation, Southeast University, Nanjing 211189, China
3%e Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China

Correspondence should be addressed to Wu Bo; thinfog@seu.edu.cn

Received 27 September 2019; Revised 22 June 2020; Accepted 3 July 2020; Published 21 July 202

Academic Editor: Maria Castro

Copyright © 2020 Xinyan Wang et al. *is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*is study aims to analyze the effect of high-altitude environment on drivers’ mental workload (MW), situation awareness (SA),
and driving behaviour (DB), and to explore the relationship among those driving performances. Based on a survey, the data of 35

lowlanders engaging in driving activities at Tibetan Plateau (high-altitude group) and 341 lowlanders engaging in driving activiti

at low altitudes (low-altitude group) were compared and analyzed. *e results suggest that the differences between the two groups
are noteworthy. Mental workload of high-altitude group is significantly higher than that of low-altitude group, and their situation
awareness is lower significantly. *e possibility of risky driving behaviours for high-altitude group, especially aggressive vio-
lations, is higher. For the high-altitude group, the increase of mental workload can lead to an increase on aggressive violations, and
the situation understanding plays a full mediating effect between mental workload and aggressive violations. Measures aiming at
the improvement of situation awareness and the reduction of mental workload can effectively reduce the driving risk from high-
altitude environment for lowlanders.

1. Introduction

Road traffic injury is now the leading cause of death, par-
ticularly for persons aged 5–29 years [1]. Road safety is an
important public health concern around the world, and safe
mobility has been considered as a human right [2]. Scholars
have long been committed to the reduction of traffic
accidents.

Tibetan Plateau is an oxygen-deprived region with an
average altitude of more than 4 000 m above sea level [3],
which is the first step of China’s terrain [4]. Physical activity
at high altitude for lowlanders can induce acute mountain
sickness (AMS) [5, 6], and even diseases, such as hyper-
tension [7]. *e status of traffic safety in the region also
needs to be improved. According to the statistics of the
National Bureau of Statistics of China, for Tibet, there were
363 traffic accidents, 124 death tolls, and 2.43 million yuan’s

property damage caused by traffic accidents in 2018.
However, the region’s permanent population at the end of
the year was only 3.44 million, indicating the road safety
condition was also not optimistic. However, there are many
floating populations from low altitudes taking driving ac-
tivity, a kind of physical work, in Tibet.

From an intuitive perspective, the high-altitude envi-
ronment plays a negative impact on driving activities. But,
there are few studies focused on the suitability of driving
activities for drivers at high altitude. Previous studies had
confirmed that the physical work capacity of low-altitude
residents (i.e., lowlanders) was significantly reduced at high
altitudes [8]. An experiment on the Qinghai-Tibet plateau
showed that the higher the altitude, the more fatigue the
driver [9]. When driving at high altitude, the mental
workload of drivers would heighten with the increase of
altitude, together with the increase of fatigue, reaction time,

Hindawi
Journal of Advanced Transportation
Volume 2020, Article ID 7283025, 10 pages
https://doi.org/10.1155/2020/7283025

mailto:thinfog@seu.edu.cn

https://orcid.org/0000-0001-8604-371

https://creativecommons.org/licenses/by/4.0/

https://doi.org/10.1155/2020/728302

and emotional stress [10]. On the other hand, the conclu-
sions obtained from short-term stress reaction had not
considered the long-term adaptability of human sufficiently,
and the results may not be applicable to the safety features of
drivers at high altitude for a long time. Based on this, in
order to discuss the safety status for floating drivers, the
study explored the performance and influencing factors
based on questionnaire data. Indicators selected included
mental workload, situation awareness, and driving
behaviour.

Mental workload, situation awareness, and driving be-
haviour are important factors influencing driver safety. For
driver, mental workload can be defined as the proportion of
information processing capability used to perform a driving
task [11]. Mental workload that is too high or too low is not
conducive to driving safety [12, 13]. As an assessment index
to analyze drivers’ performance, situation awareness is a very
important precondition to drive safely in a complex and
dynamic environment. It can be described as the ability to
accurately perceive the traffic environment for drivers and to
adapt their interaction with distracting activities [14, 15].
Drivers with higher situation awareness can find more
hazards in a driving task [16]. For driving behaviour, it is
widely analyzed for the possibility of being involved in a
traffic accident and can be regarded as a series of driver’s
behaviours while driving [17–19].

Being able to measure with questionnaires is one other
reason to choose these indicators (i.e., mental workload,
situation awareness, and driving behaviour) in the study. For
example, the driver behaviour questionnaire (DBQ) pro-
posed by Reason [18] has been widely extended and applied
to survey drivers’ self-reported driving behaviours [20, 21].
*e Situation Awareness Global Assessment Technique
(SAGAT) [22] and the Situation Awareness Rating Tech-
nique (SART) [23] have been widely used for the mea-
surement of situation awareness [24–26]. *e Subjective
Work Assessment Technique (SWAT) and the National
Aeronautics and Space Administration-Task Load Index
(NASA-TLX) are popular measuring tools of mental
workload [27–31].

*is study focuses on different performances between
drivers at high altitudes from low altitudes and drivers at low
altitudes. Specifically, the study is organized as follows: some
analyses are implemented in Section 2 (study 1) for the
differences of drivers’ mental workload, situation awareness,
and driving behaviour. In Section 3 (study 2), the rela-
tionships among the three factors are analyzed based on the
structural equation modeling (SEM). *en, Section 4 and
Section 5 are the discussion and the conclusion of the study,
respectively.

2. Study1:DifferencesonDrivingPerformances

Considering the undesired influences of high-altitude en-
vironment on human’s physiological condition [6, 8, 10], the
study aims at determining if there are adverse effects of high-
altitude environment on driving safety to drivers from low
altitudes.

2.1. Methodology

2.1.1. Design. In fact, factors affecting driver’s safety are
varied, and a proper assumption need to be set before
statistical analysis. For this, the study first assumed that there
was no significant difference in their normal driving tasks
between the drivers at high altitudes from low altitudes
(high-altitude group) and drivers at low altitudes (low-al-
titude group). Driving tasks involved of these two groups are
their common driving activities, and the standards of traffic
management, traffic design, and traffic regulations are highly
consistent in the two areas. *erefore, the assumption that
driving activities of these two groups are similar is
reasonable.

For the differences of the two groups on driving per-
formances, a survey on driving performances was carried out
from three angles: mental workload, situation awareness,
and driving behaviour, and the analysis of variance
(ANOVA) was used to the comparison. Meanwhile, there
are three hypotheses need to be tested:

H1: mental workload of high-altitude group is signif-
icantly higher than that of the low-altitude group
H2: situation awareness of high-altitude group is sig-
nificantly lower than that of the low-altitude group
H3: undesired driving behaviour of high-altitude group
is significantly more frequent than that of the low-al-
titude group

2.1.2. Materials and Procedure. *e subjective workload
assessment technique (SWAT) with equal weight was chosen
as the measuring tool, which has a desired sensitivity
[31, 32]. During the preparation of the questionnaire, three
questions of SWAT [27] were modified to suit driving task
(e.g., “how high is your stress usually when driving on Ti-
betan Plateau?”), and a preresearch had been implemented
to reduce the difficulty of understanding. Answers to those
questions were designed to utilize a three-point scale. *e
SWAT contains three dimensions: time load (TL), mental
effort load (EL), and psychological stress load (SL). *e score
of mental workload (MW) in the study was calculated by the
following formula [32]:

MW �
TL + EL + SL

3
. (1)

For situation awareness, the situation awareness global
assessment technique (SAGAT) and the situation awareness
rating technique (SART) are popular measuring tools for
situation awareness [24–26]. SAGAT was commonly used in
the process of an experiment [33], and SART was often used
for post hoc evaluation [34]. Clearly, SART is more suitable
for the study.

During the preparation of the questionnaire, the ques-
tions of 10-D SART [23] were modified to suit the driving
task (e.g., “how high is your alertness usually when driving
on Tibetan Plateau?”), and a preresearch had been com-
pleted to improve readability. Answers to these questions
were designed to utilize a ten-point scale. *e 10-D SART

2 Journal of Advanced Transportation

contains ten questions which could be further grouped into
three overall dimensions named 3D-SART: (a) attention
demand (AD); (b) attention supply (AS); and (c) situation
understanding (SU). Specifically, attention demand is a
combination of the instability of situation, the complexity of
situation, and the variability of situation; attention supply is
a combination of the arousal of situation, the concentration
of attention, the division of attention, and the spare mental
capacity; situation understanding is the combination of the
quantity of information, the quality of information, and the
degree of familiarity. By using a group score, the score of SA
was calculated by the following formula [24]:

SA � SU − (AD − AS), (2)

where SU is the situation understanding, AD is the attention
demand, and AS is the attention supply.

In terms of measuring tool of driving behaviour, the
driver behaviour questionnaire (DBQ) is an instrument
applied widely to examine the self-reported driving be-
haviour [18, 20, 21]. *e DBQ with three-factor structure
(e.g., errors, lapses, and violations) or four-factor structure
(e.g., errors, lapses, ordinary violations, and aggressive vi-
olations) has been broadly implemented in many studies
[35–37]. In the study, a DBQ considering errors, lapses,
ordinary violations, and aggressive violations and including
23 items was carried out to gather data. All the items were
derived or revised from literatures of af Wåhlberg et al. [38],
Bener et al. [39], Liu and Chen [3], Reason et al. [18],
Hezaveh et al. [40], and Maslać et al. [41]. Every question has
five options by using a five-point scale ranging from never
(1) to nearly all the time (5). Meanwhile, Chinese statements
of the referenced items were translated and back translated
to minimize the difficulty of understanding on the premise
of ensuring the original meaning.

A five-month survey was carried out to obtain ques-
tionnaire data, and the survey was conducted in the form of
electronic questionnaire and distributed by social media
such as email, QQ, and WeChat. Participants were invited to
participate in the survey with a certain charge. And, 1295
copies of questionnaire were obtained. 356 participants were
lowlanders from low altitudes, that is, provinces in the third
step of China’s terrain with an average altitude of less than
500 m above sea level [4], and the lowlanders had engaged in
driving task on Tibetan Plateau (high-altitude group). Other
939 participants were also from these low altitudes.

*e platform of electronic questionnaire can automat-
ically identify the city where the participant was located and
judge whether it was a valid object according to holding a
valid driver license or not, the identified city, and the filled
place where the households are registered. Only valid par-
ticipants can complete the questionnaire. *e lowlanders of
939 participants conducted the self-reports of mental
workload, situation awareness, and driving behaviour
according to their experience, and the high-altitude group
reported their experience of driving at high altitude
according to the content of questionnaire. On the other
hand, to reduce the difference between these two groups on
demographic characteristics, the collection of high-altitude

group’s data was finished first. And, to meet the charac-
teristics of the high-altitude group, a total of 939 copies were
collected, of which 341 copies were selected randomly as a
control group (low-altitude group).

*e analytical approach involved in the study contains
reliability analysis, validity analysis, and differential analysis.
Cronbach’s alpha, factor load matrix, the statistic of Kai-
ser–Meyer–Olkin (KMO) test, and Bartlett’s spherical test
were used to further identify reliability or validity [42]. As is
mentioned above, for the comparison on mental workload,
situation awareness and driving behaviour between high-
altitude group and low-altitude group, the analysis of var-
iance (ANOVA), which has been used widely for the dif-
ferential analysis on driver performances was selected
[40, 43].

2.2. Results. *e summary of those 697 participants is given
in Table 1. *e results of analysis of variance (ANOVA)
indicate that there is no significant difference between the
two groups on traits of gender, age, years of driving expe-
rience, and driving distance. *at is to say, the control group
is effective. *en, the analysis in the study is based on these
data.

*e reliability analysis showed that Cronbach’s alpha of
the subjective workload assessment technique (SWAT) was
0.641. Typically, Cronbach’s alpha greater than 0.7 is ideal
[42], and the value is lower than that. *e result may be the
cause that the set option of SWAT is a three-point scale, and
SWAT only contains three dimensions. Based on this, the
result had been accepted in the study and SWAT could be
utilized as a tool for drivers to measure mental workload.

Further, the results of analysis of variance indicate that
the p value for time workload (TL) was 0.141 (F (1, 695) �
3.549, p>0.05), the p values for mental effort load (EL)
equaled 0.000 (F (1, 695) � 44.149, p<0.01), psychological stress load (SL) equaled 0.000 (F (1, 695) � 24.587, p<0.01), and mental workload (MW) 0.000 (F (1, 695) � 35.207, p<0.01). *erefore, there are strong evidences of difference between drivers of the high-altitude group and drivers of the low-altitude group on EL, SL, and MW, and those indicators of the high-altitude group are higher than those of the low- altitude group (Figure 1). *e hypothesis of H1 is valid and acceptable.

Table 2 shows the reliability of the situation awareness
rating technique (SART) in different dimensions and the
statistics of 10 items. *e results show that Cronbach’s alpha
of SARTand its three dimensions are all greater than 0.7, and
the reliability is ideal.

*e difference test results showed that the p values of
attention demand (AD), attention supply (AS), and situation
understanding (SU) equaled 0.004 (F (1, 695) � 8.235,
p<0.01), 0.000 (F (1, 695) � 13.104, p<0.01), and 0.000 (F (1, 695) � 64.697, p<0.01), respectively. And, the p value of SA was 0.000 (F (1, 695) � 15.880, p<0.01). *us, there are strong evidences of difference between drivers of the high- altitude group and drivers of the low-altitude group on attention demand, attention supply, situation understand- ing, and situation awareness, with lower on attention supply,

Journal of Advanced Transportation 3

situation understanding, and situation awareness but higher
on attention demand of the high-altitude group (Figure 2).
And, the hypothesis of H2 is also acceptable.

Results of reliability analysis in Table 3 show that the
values of Cronbach’s alpha are greater than 0.7, which in-
dicate the internal consistency of the driver behaviour
questionnaire (DBQ) is ideal. For the validity, because each
item comes from researches related to driving behaviour in
the past, the content validity is ideal. In terms of structural
validity verified by factor analysis, four components were
retained with eigenvalues greater than 1, and the rotating
component matrix is shown in Table 4. Due to the existence

of cross-loading of ov_2 (increase speed to pass a yellow
light) and ov_5 (disregard the speed limit of the roads), with
0.654 and 0.654 to ordinary violations but 0.401 and 0.411 to
aggressive violations, these two items were removed. As
shown in Table 3, before and after ov_2 and ov_5 was de-
leted, the values of Cronbach’s alpha and KMO statistics and
results of Bartlett’s spherical test are in the ideal range. *e
cumulative proportion of variance contribution of these four
factors increase from 59.150 to 60.300 and from 48.261 to
50.876 to ordinary violations.

Results of analysis of variance showed that the p values
of ordinary violations (OV), errors (ER), aggressive

Table 1: Sample information.

Categorical variable (F (1, 695), p value) Category High-altitude group (N � 356) Low-altitude group (N � 341)

Gender (1.469, 0.226)
Female 74 8

Male 282 257

Age (0.060, 0.807)
Up to 30 years 240 235
Above 30 years 116 106

Years of driving experience (0.800, 0.372)
Up to 5 years 272 281
Above 5 years 84 60

Driving distance (3.504, 0.062)
Up to 50,000 km 257 255
Above 50,000 km 99 86

Years of driving experience on Tibetan Plateau
Up to 1 years 201 —
Above 1 years 155 —

Driving distance on Tibetan Plateau
Up to 10,000 km 206 —
Above 10,000 km 150 —

0
1

TL EL SL

Sc
or

High altitude
Low altitude

Figure 1: Scores of mental workload.

Table 2: Results of internal consistency and statistics.

Dimensions (Cronbach’s alpha) Items (notation)
High-altitude group Low-altitude group

Mean (std. D) Mean (std. D)

SA (0.856)

AD (0.869)
Instability of situation (s11) 6.247 (2.102) 5.589 (1.981)
Complexity of situation (s12) 6.169 (2.225) 5.868 (1.947)
Variability of situation (s13) 6.225 (2.206) 5.997 (1.903)

AS (0.806)

Arousal of situation (s21) 6.534 (2.071) 7.114 (1.807)
Division of attention (s22) 6.301 (2.026) 7.399 (1.797)
Spare mental capacity (s23) 6.284 (1.978) 6.557 (1.698)

Concentration of attention (s24) 6.927 (1.768) 6.328 (1.843

SU (0.771)
Information quantity (s31) 5.596 (2.404) 6.689 (1.658)
Information quality (s32) 6.239 (1.839) 6.645 (1.742)

Familiarity (s33) 6.208 (1.996) 6.786 (1.806)

4 Journal of Advanced Transportation

violations (AV), and lapses (LA) equaled 0.076 (F (1, 695) �
3.148, p>0.05), 0.432 (F (1, 695) � 0.619, p>0.05), 0.000 (F
(1, 695) � 23.147, p<0.01), and 0.198 (F (1, 695) � 1.662,

p>0.05), respectively. And, the p value of the total score of
driving behaviours (DB) was 0.030 (F (1, 695) � 4.741,
p<0.05). Hence, there are strong evidences of difference

AD AS SU SA

6
4
2
0
Sc
or

es
High altitude
Low altitude

Figure 2: Scores of situation awareness.

Table 3: Results of internal consistency and validity of factors.

Category Cronbach’s alpha KMO statistic Bartlett’s spherical test Cumulative (%)
Ordinary violations (OV) 0.731 (0.811)a 0.744 (0.839)a 0.000 (0.000)a 50.876 (48.261)a

Errors (ER) 0.864 0.862 0.000 64.717
Aggressive violations (AV) 0.821 0.824 0.000 59.127
Lapses (LA) 0.845 0.856 0.000 61.694
Driving behaviours (DB) 0.917 (0.921)a 0.927 (0.927)a 0.000 (0.000)a 60.300 (59.150)a
aResult before ov_2 and ov_5 was deleted.

Table 4: Factor loading and statistics.

Category Brief items
High-
altitude

Low-altitude
Factor loading

Mean (SD) Mean (SD)
Ordinary violations (OV)
ov_1 Ignore the red light and pass through an intersection 1.447 (0.794) 1.420 (0.643) 0.701
ov_2a Increase speed to pass a yellow light 2.101 (1.119) 1.971 (0.781) 0.654 (0.401)
ov_3 Drive the wrong lane in the opposite direction 1.320 (0.699) 1.325 (0.533) 0.736
ov_4 Take more passengers than allowed 1.253 (0.674) 1.299 (0.561) 0.648
ov_5a Disregard the speed limit of the roads 1.843 (1.068) 1.736 (0.787) 0.654 (0.411)
ov_6 Forget to wear seat belt 1.694 (1.074) 1.472 (0.731) 0.534
ov_7 Use the cellular phone while driving 1.975 (1.041) 1.823 (0.863) 0.479
Errors (ER)
er_1 Fail to notice when a traffic-signal turns green 2.039 (0.972) 2.009 (0.705) 0.675
er_2 Misjudge an overtaking gap 1.879 (0.981) 1.942 (0.753) 0.787
er_3 Hit a cyclist nearly when turning right 1.767 (0.958) 1.806 (0.755) 0.658
er_4 Brake inappropriately to stop 1.826 (0.972) 1.959 (0.795) 0.789
er_5 Insufficient attention to vehicle or pedestrian ahead 1.803 (0.962) 1.832 (0.720) 0.620
Aggressive violations (AV)
av_1 Drive too close to impel the car in front to go faster 1.927 (1.018) 1.710 (0.733) 0.557
av_2 Feel angered by another driver’s behaviour 2.360 (1.106) 1.925 (0.883) 0.744
av_3 Become impatient with a slow driver and pass on the right 2.421 (1.156) 2.238 (0.922) 0.670
av_4 Race away from traffic lights to beat the driver next to you 1.801 (0.980) 1.545 (0.702)

0.58

av_5 Be annoyed and sound the horn 1.896 (1.017) 1.725 (0.787) 0.592
Lapses (LA)
la_1 Intend to A, but driving on route to B 2.410 (0.982) 2.493 (0.789) 0.696
la_2 Turn on the wrong device of the vehicle 1.935 (1.006) 1.919 (0.770) 0.710
la_3 Forget where the car parked 1.924 (1.014) 2.006 (0.892) 0.732
la_4 Feel unsure about the lane when approaching an intersection 2.017 (1.045) 1.954 (0.868) 0.724
la_5 Forget to open lights timely when the night has come 2.110 (1.049) 1.870 (0.805) 0.688
aVariable was dropped from the measurement due to cross-loading, with 0.401 and 0.411 to aggressive violations, respectively.

Journal of Advanced Transportation 5

between the high-altitude group and the low-altitude group
on driving behaviours, with more undesired risky driving
behaviour for the high-altitude group. Meanwhile, accord-
ing to the results, the difference is mainly caused by the
behaviour of aggressive violations with smaller p values
similarly and simultaneously (Figure 3). *e results partly
support the hypothesis of H3.

3. Study 2: Factors Affecting Drivers’
Aggressive Violations

Considering the significant difference on aggressive viola-
tions between the two groups, the causes of the phenomenon
are worth exploring. Do the level of mental workload and
situation or situation awareness affect the frequency of
aggressive violations for high-altitude group? And, is there a
progressive relationship between the three dimensions of
situation awareness? *e analysis may lead to some impli-
cations for the management of aggressive violations for the
group.

3.1. Methodology

3.1.1. Design. Aiming at the relationships between the
factors of mental workload, situation awareness, and ag-
gressive violations for the high-altitude group, the sample of
high-altitude group was applied to statistical analysis based
on the method of structural equation modeling (SEM). For
the verification, the following six hypotheses need to be
further tested (Figure 4):

H41: attention demand has a significant positive impact
on attention supply
H42: attention supply has a significant positive impact
on situation understanding
H43: attention demand has a significant positive impact
on mental workload
H44: mental workload has a significant negative impact
on situation understanding
H45: situation understanding has a significant negative
impact on aggressive violations
H46: mental workload has a significant positive impact
on aggressive violations

3.1.2. Statistical Analysis. In order to verify the roadmap or
model above, a structural equation model was established to
develop a path analysis using maximum likelihood for the
multidimensional relationships between drivers’ aggressive
violations, mental workload, attention demand, attention
supply, and situation understanding. While the structural
equation model was developed, the goodness-of-fit of the
model was assessed according to CMIN/DF, absolute index
(including GFI, AGFI, and RMSEA), incremental index
(including NFI and CFI), and parsimony index (including
PGFI and PNFI), following the recommendations of several
literatures [17, 44, 45]. *e recommended threshold of
CMIN/DF was less than 0.3, of GFI, AGFI, NFI, and CFI

more than 0.9, of RMSEA less than 0.08 or 0.05, and of PGFI
and PNFI more than 0.5 [17, 44, 46]. In the study, to acquire
a better goodness-of-fit, the original model was modified
according to the modification indices (MIs) [17, 47].

As for sampling of SEM, several recommendations
suggested that sample size should be at least 10–15 times the
number of observed variables [45, 48]. In this study, data
from high-altitude group were used, and the sample size was
19.778 times the number of observed variables (356 samples/
18 observed variables). *e analysis tool involved in the
study was AMOS 21.0 version.

3.2.Results. *e original model followed the conception of
Figure 4 and had been revised to improve the goodness-
of-fit by correlating the error terms of e12 and e33, e22
and e33, and ea2 and ea4 because of larger modification
indices (MIs). Regression weights between latent variables
and observed variables and covariances and correlations
between error terms and variances of the modified model
(Figure 5) were all significant. In terms of goodness-of-fit,
the results exported by AMOS were that chi-squared
equaled 247.147, degree of freedom 126, CMIN/DF 1.961,
GFI 0.904, AGFI 0.870, RMSEA 0.052, NFI 0.901, CFI
0.948, PGFI 0.745, and PNFI 0.742. *us, only AGFI is
lower than the recommended thresholds, and the model
fits the data well.

Meanwhile, results of the path analysis (Table 5)
supported some hypotheses. For the three dimensions of
situation awareness, hypothesis H41 and hypothesis

H42

were valid (p<0.001). *e increase of the attention

0
1
2
3
4
5

OV ER AV LA DB

Sc
or
es
High altitude
Low altitude

Figure 3: Scores of driving behaviour.

Attention
demand

Mental
workload

Attention
supply

Situation
understanding

Aggressive
violations

H41

H42

H43

H44

H46

H45

Figure 4: Hypothesis roadmap.

6 Journal of Advanced Transportation

demand could spur the increase of driver’s attention
supply, and the increase of the attention supply led to an
increase of the level of situation understanding. *e result
showed a progressive relationship among attention de-
mand, attention supply, and situation understanding, and
the intermediary role of the attention supply was also
valid. In addition, the increase of the attention demand
could increase driver’s mental workload (p<0.001), but the increase of the mental workload did not mean an increase in the level of the situation understanding (p>0.05). For the group, the increase of the mental
workload could increase their aggressive violations
(p<0.05), and the increase of the level of the situation understanding could reduce the frequency of aggressive violations (p<0.05). *us, the situation understanding played a full mediating role between the mental workload and the aggressive violations. Moreover, the mental workload also played a mediating role between the at- tention demand and aggressive violations.

4. Discussion

*e results of study 1 and study 2 support some hypotheses,
including that the high-altitude group has more driving
behaviors of aggressive violations, greater mental workload,
and lower situation awareness than of the lower altitude
group. And, aggressive violations are positively correlated
with the mental workload and negatively correlated with the
situation understanding. Some discussions of these results
are as follows.

Due to a higher mental effort load and psychological
stress load, the mental workload of the high-altitude group is
significantly higher than that of the low-altitude group. As
mentioned above, many studies have confirmed that the
physical work capacity of low-altitude residents is signifi-
cantly reduced at high altitudes [8]. Drivers are more prone
to fatigue with the increase of altitude [9]. *e raise of al-
titude not only leads to an increase in mental workload but
also affects the driver’s reaction time and their mood [10]. In

e11 s11

e12 s12

e13 s13

e21 s21

e23 s23

e24 s24

e33 s33

e32 s32

e31 s31

e22 s22

em1SL

em2EL

em3TL

ea2av_2

ea3av_3

ea4av_4

ea5av_5

ea1av_1

es1

es2

es3

0.36

0.53

–

0.54

0.83

0.84

0.73

0.48

0.87

0.76

0.64

0.78

0.82

0.90

0.87

0.63

0.72

0.68
0.06

0.27

0.63

0.71

0.50

0.40

0.50

0.25

0.46

0.51

0.40

0.75

0.69

0.61

0.67

0.82

0.41

0.58
0.58
0.75

0.70

0.54

0.23

0.79

0.41

0.85

–0.25

Figure 5: Estimated results with standardized estimates. Note: AD � attention demand; AS � attention supply; SU � situation under-
standing; MW � mental workload; AV � aggressive violations.

Table 5: Result of the path analysis.

Hypotheses and constructs R. W. Std. R. W. S. E. C. R. p value Result
H41: AS⟵AD 0.322 0.412 0.068 4.756 ∗∗∗ Supported
H42: SU⟵AS 0.620 0.846 0.112 5.54 ∗∗∗ Supported
H43: MW⟵AD 0.123 0.786 0.023 5.429 ∗∗∗ Supported
H44: SU⟵MW 0.214 0.058 0.261 0.819 0.413 Not supported
H45: AV⟵SU − 0.190 − 0.249 0.079 − 2.421 0.015 Supported
H46: AV⟵MW 0.753 0.268 0.307 2.454 0.014 Supported
R. W.: regression weight; Std. R. W.: standardized regression weight; S. E.: standard error; C. R.: critical ratio. ∗∗∗p<0.001

Journal of Advanced Transportation 7

the study, the mental effort load and the psychological stress
load of high-altitude group were higher than of the low-
altitude group with a similar time load, which might be
related to the altitude of position where drivers were located.
*e sample of the high-altitude group came from the Tibetan
Plateau, where the average altitude is more than 4 000 m.
High-altitude environment with low-pressure and oxygen-
deprived climate are more likely to cause fatigue or negative
emotions [10, 49], which is the same for lowlanders to
driving in spite of them have a certain experience in the
environment, and the phenomenon may not change sig-
nificantly over time. Considering the lack of oxygen and its
bad effect on drivers’ emotion, the provision of oxygen and
the playing calm music may help to improve their driving
performances [10, 49, 50].

Driving behaviour has long been discussed as an im-
portant object of researches in traffic safety [17, 18, 40, 41].
Results of the study show that there are differences on
driving behaviour between these two groups. *e undesired
risky driving behaviour of the high-altitude group is more
than that of low-altitude group, which mainly caused by the
behaviors of aggressive violations. Combined with the
connotation of aggressive violations and the effects of high
altitude on human cognition, psychology, and behaviour,
especially the irritability and hostility induced by anoxic
environments [17, 40, 47, 49, 51], there is a considerable
correlation between risky driving behaviours and high-al-
titude environment, especially the behaviors of aggressive
violations. Considering the increase of mental workload and
the impact of mental workload on aggressive violations,
lowlanders may develop an undesirable change of driving
habits because of driving in the environment for a long time.

Combined with the intermediary role of the attention
supply, the progressive relationship among the attention
demand, the attention supply, and the situation un-
derstanding further contribute to explain the hierarchy
of the different level of situation awareness [15, 52, 53].
For other correlations in Figure 5, the direct positive
relationship between the attention demand and the
mental workload means that the increase of the attention
demand in driving activities at high altitudes further
increase drivers’ mental workload. And, the increase of
the mental workload can increase the frequency of ag-
gressive violations, while the increase of the level of the
situation understanding can help to reduce the likeli-
hood. *erefore, it is beneficial to appropriately reduce
drivers’ mental workload at high altitudes. At the same
time, improving driver’s understanding of the traffic
condition and training their situation awareness for
driving at high altitudes are also helpful for reducing the
bad effect of the high-altitude environment on driving
performances.

5. Conclusion

Based on a survey by the subjective workload assessment
technique (SWAT), the situation awareness rating
technique (SART), and the driver behaviour question-
naire (DBQ), the effect of high-altitude environment on

driving performances was analyzed, and the relationships
among mental workload, situation awareness, and ag-
gressive violations were explored. For drivers from low-
altitudes, the high-altitude environment can lead to
greater mental workload, worse situation awareness, and
more risky driving behaviors, especially aggressive vio-
lations. Meanwhile, the mental workload and the situ-
ation understanding can affect the frequency of
aggressive violations. According to the above results,
there are the three suggestions for lowlanders driving at
high altitude:

(1) It is recommended to understand the traffic envi-
ronment before engaging in driving task at high
altitude, including possible dangers and personal
psychological and physical feelings. *e beneficial
effect of the situation understanding on driving
behaviours in the study can support the recom-
mendation. At the same time, the traffic manage-
ment department can consider making some
propaganda on the suggestion.

(2) *e drivers should reduce risky driving behaviours
consciously and judge their driving ability correctly
for the decision whether it is necessary to reduce
driving activities or the work intensity.

(3) It may be an effective mean of releasing oxygen in the
car or playing calm music while driving. *e supply
of oxygen can increase the oxygen content in the car,
and a gentle music can make people feel calm and
perform better in driving task.

Furthermore, the study only discussed the bad effect
of the high-altitude environment by comparing the
differences between the high-altitude group and the low-
altitude group based on self-reported data. Future
studies can focus on the gap for a larger region or the gap
between local drivers and nonlocal drivers, as well as the
refined traffic design and traffic management, and the
evaluation of suitability for lowlanders driving at high
altitudes.

Data Availability

*e data used to support the findings of this study are
available from the corresponding author upon request.

Conflicts of Interest

*e authors declare no conflicts of interest.

Acknowledgments

*is research was supported by the National Natural Science
Foundation of China (Grant nos. 51768063 and 51968063)
and the Cultivation Fund for Scientific Research of Tibet
University (Grant no. ZDTSJH18-02). *e authors also
thank the students Xinlei Wang and Tianjiao Li from Tibet
University for their help in data collection.

8 Journal of Advanced Transportation

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10 Journal of Advanced Transportation

This paper is about Reduction of Hazardous Driving Behavior

Here are the instructions:

One paper citing primary and secondary sources published in peer-reviewed scientific journals. Approved sources include empirical behavior analytic studies and review papers; students may not cite online sources, blogs, magazine articles, etc. Papers will be screened through TurnItIn and/or other plagiarism review services. No more than 30% shared material.

Paper should be in APA 7th edition student paper format, with 12-pt Times New Roman, double-spaced, and 1-inch margins. Papers should include a title page, body page, and reference pages, but no abstract. Papers should be at least 10 but no more than 12 body pages (not including title page and reference pages).

The review paper should report, summarizes, synthesizes, and expands on an area of research from the psychology of learning. It should cover how an area of research has been studied in greater detail than covered in the text or used in particular ways to inform treatment.

It is particularly important that paper content be strictly related to topics, the role of learning and motivation as a behavioral process, not cognitive, mentalistic, or psychosocial processes.

10 pts

Criteria	Pts
Body of Paper This criterion is linked to a learning outcome -Problem/topic/concept is clearly described; -Behavior is explicitly conceptualized in operational terms; -Appropriate application of concepts and principles of conditioning; -Terminology used accurately and consistently;	40 pts
Conclusion This criterion is linked to a learning outcome -Relates back to introduction; -Summarizes purpose of paper; -Summarizes primary arguments; -Presents original insights;	20 pts
Support This criterion is linked to a learning outcome -References cited appropriately; -Minimal use of direct quotes; -Sufficient sources discussed (5+ relevant articles); -Any sources are discussed in the paper, relate directly to topic, and are integrated well;	1 5 pts
Introduction This criterion is linked to a learning outcome -Purpose of paper clearly identified with thesis statement; -Organization of paper clearly described; -Key terms/concepts for paper identified and defined;	5 pts
Organization This criterion is linked to a learning outcome -Progression of ideas is logical; -Follows organization in introduction; -Topic and transition sentences for paragraphs; -Paragraphs are appropriate length, more than 1-2 sentences but no longer than half a page; -Paragraphs centered on a single idea; -Overall cohesion;		10 pts
Mechanics This criterion is linked to a learning outcome -Adheres to APA 7th style; -No errors in spelling, capitalization, punctuation, etc.; -Correct grammar used throughout, tense and agreement correct; -Uses active rather than passive voice; -Academic rather than colloquial tone;

Sources to use:

Ka, E., Kim, D.-G., Hong, J., & Lee, C. (2020). Implementing Surrogate Safety Measures in Driving Simulator and Evaluating the Safety Effects of Simulator-Based Training on Risky Driving Behaviors. Journal of Advanced Transportation, 2020, 1–12.

https://doi.org/10.1155/2020/7525721

Wang, X., Bo, W., Yang, W., Cui, S., & Chu, P. (2020). Effect of High-Altitude Environment on Driving Safety: A Study on Drivers’ Mental Workload, Situation Awareness, and Driving Behaviour. Journal of Advanced Transportation, 2020, 1–10.

https://doi.org/10.1155/2020/7283025

Underwood, G., Ngai, A., & Underwood, J. (2013). Driving experience and situation awareness in hazard detection. Safety Science, 56, 29–35.

https://doi.org/10.1016/j.ssci.2012.05.025

Hill, T., Stephens, A. N., & Sullman, M. J. M. (2021). Mobile phone applications use while driving in Ukraine: Self-reported frequencies and psychosocial factors underpinning this risky behaviour. PLOS ONE, 16(2), e0247006.

https://doi.org/10.1371/journal.pone.0247006

Kazbour, R. R., & Bailey, J. S. (2010). AN ANALYSIS OF A CONTINGENCY PROGRAM ON DESIGNATED DRIVERS AT A COLLEGE BAR. Journal of Applied Behavior Analysis, 43(2), 273–277.

https://doi.org/10.1901/jaba.2010.43-273

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Areas of Expertise

Essay

Thesis

Presentation

Dissertation

Term Paper

Research Paper

Book Review

Assignment

Report

Case Study

Letter

Article

Coursework

Speech

Q & A

Critical Thinking

Areas of Expertise

Essay

Thesis

Presentation

Dissertation

Term Paper

Research Paper

Book Review

Assignment

Report

Case Study

Letter

Article

Coursework

Speech

Q & A

Critical Thinking

Trusted Partner of 9650+ Students for Writing

Preferred Writer

Grammar Check Report

One Page Summary

Plagiarism Report

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Delegate Your Challenging Writing Tasks to Experienced Professionals

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