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Expert Systems With Applications 238 (2024) 122318
Available online 27 October 2023
0957-4174/© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
A novel spherical decision-making model for measuring the separateness of
preferences for drivers’ behavior factors associated with road
trafc accidents
Sarbast Moslem
a
,
1
,
*
, Danish Farooq
b
,
2
, Domokos Eszterg´
ar-Kiss
c
,
3
, Ghulam Yaseen
d
,
Tapan Senapati
e
,
4
, Muhammet Deveci
f
,
g
,
h
,
5
a
School of Architecture Planning and Environmental Policy, University College Dublin, Ireland
b
Department of Civil Engineering, COMSATS University Islamabad, Wah Cantt 47040, Pakistan
c
Department of Transport Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics,
M˝
uegyetem rkp. 3. Budapest, Hungary
d
Department of Civil Engineering, Muslim Youth University Islamabad, 44000, Pakistan
e
School of Mathematics and Statistics, Southwest University, Beibei 400715, Chongqing, China
f
Department of Industrial Engineering, Turkish Naval Academy, National Defence University, 34940 Tuzla, Istanbul, Turkey
g
The Bartlett School of Sustainable Construction, University College London, 1-19 Torrington Place, London WC1E 7HB, UK
h
Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
ARTICLE INFO
Keywords:
AHP
Spherical fuzzy sets
Drivers’ behavior
Road trafc accidents
Kendall model
ABSTRACT
Enhancing road safety through a more effective understanding of drivers’ behavior is a viable approach to
curbing trafc collisions. When evaluating driving behavior, the selection of methodologies is diverse, often
facing scrutiny. This study aims to detect, compare and quantify critical drivers’ behavior factors concerning
road safety in Budapest, Hungary. Employing the Analytic Hierarchy Process (AHP) within a spherical fuzzy
framework, based on Spherical Fuzzy Sets (SFS), we assess driver preferences. Kendall’s test gauges’ agreement
levels among hierarchical driver groups. At Level 1, our Spherical Fuzzy AHP (SFAHP) identies ’Lapses’ as
crucial, followed by ’Errors’ for experienced and young drivers. However, foreign drivers prioritize ’Errors’ and
’Violations.’ At Level 2, “Aggressive violations” prevails across all groups, contrasting with “Ordinary violations.”
At Level 3, “Driving with alcohol use” reigns supreme. Kendall’s concordance demonstrates low similarity at
Level 1, while strong agreement surfaces for Levels 2 and 3. Our insights can empower transportation authorities
to bolster road safety strategies by addressing these pivotal behavior factors.
1. Introduction
In 2018, a global status report about road safety was released by the
World Health Organization (WHO), which shows trafc accidents on
different roads and indicates trafc accidents as a major issue on global
health and development (WHO, 2018). Already in 2010, the European
Union started developing road safety instructions with an aim to achieve
50 % reduction in road fatalities by 2020 compared to the number of
fatalities in 2010. The numbers were later redened to minimize the
number of road accidents by 2030 (Carson et al., 2020). However, for
example Hungary (Janstrup, 2017) recorded a signicant increase
regarding the ratio of fatalities compared to the accident data of recent
years, which highlights the issue of prevention and the need for un-
derstanding the underlying causes.
* Corresponding author.
E-mail addresses: sarbast.moslem@ucd.ie (S. Moslem), danish.farooq@ciitwah.edu.pk (D. Farooq), esztergar@mail.bme.hu (D. Eszterg´
ar-Kiss), ghulam.yaseen@
ce.uol.edu.pk (G. Yaseen), muhammetdeveci@gmail.com (M. Deveci).
1
ORCID: 0000-0003-4587-7482.
2
ORCID: 0000-0003-3403-7744.
3
ORCID: 0000-0002-7424-4214.
4
ORCID: 0000-0003-0399-7486.
5
ORCID: 0000-0002-3712-976X.
Contents lists available at ScienceDirect
Expert Systems With Applications
journal homepage: www.elsevier.com/locate/eswa
https://doi.org/10.1016/j.eswa.2023.122318
Received 25 August 2023; Received in revised form 30 September 2023; Accepted 22 October 2023
Expert Systems With Applications 238 (2024) 122318
2
Recent and past studies show that about 95 % of all trafc collisions
are due to driver-related risky behaviors (Bazzaz et al., 2014). Risky
driving is a human error related to driver that conspicuously contributes
to the happenings of trafc injuries (Atombo et al., 2017; Sabate, 2014).
The Road Safety Action Program (RSAP) report is a situation analysis
which nds that human factors involve high in road accidents; therefore,
reducing them helps to achieve the most signicant target in road safety
(OECD/ITF, 2016). Most drivers’ behavioral (DB) factors are found to be
conscious violations of the trafc rules as well as errors deriving from
the lack of driving experience (Stanton & Salmon, 2009). While other
drivers might cause accidents because of inattention, or failure to
perform, which is normally related to the drivers’ age (Wierwille et al.,
2002). To ensure road safety, the research objectives of a recent study in
Egypt worked to measure the impact of the road users’ behavior in
hazardous trafc conditions, and to record the correlation among the
drivers’ demographic features, the details of trafc regulations, the
different violations, as well as accidents with the risk perception on
different roads (Sayed et al., 2022).
Several studies conducted to investigate the concerns of road acci-
dents as well as various factors inuencing such incidents (Zhang et al.,
2019; Adanu et al., 2017). As proposed by Saaty (1977), specifying
priorities and nding major criteria are possible by using the analytic
hierarchy process (AHP). However, the AHP could not handle with the
uncertainty and ambiguity issue of the decision makers during the
evaluation process. To overcome this issue, the scholars adopted AHP in
different fuzzy environments to evaluate the complex problems
(Mathew et al., 2020; Redjem et al., 2021; S´
anchez-Lozano et al., 2022;
Nejkovic et al., 2022; Bulut &
¨
Ozcan, 2023; Nezhad et al., 2023) and
some applications has already been conducted to assess transportation
problems (Kashav et al., 2022; Ebrahimi & Bridgelall, 2021; Duleba
et al., 2021; Gündo˘
gdu et al., 2021; Farooq & Moslem, 2022; Moslem
et al., 2023; Younis Al-Zibaree & Konur, 2023). Similarly, different types
of spherical fuzzy sets (SFS) dened through an interval are used by the
decision-makers’ on the membership objective of a fuzzy set. Imple-
menting the SFS of the data, a greater preference realm can be provided
for the decision-makers during the evaluation process (Sharaf, 2020). A
recent study assessed the public transportation issues through an
interval-based spherical fuzzy AHP (SFAHP) method (Duleba et al.,
2021).
The requirement of ranking various factors is rather popular among
the decision-makers in different areas of studies, such as engineering,
management sciences, medicine, and nance. (Gündo˘
gdu and Kahra-
man, 2020; Vahidinia & Hasani, 2023; Rezazadeh et al., 2023). Also, a
question arises how different groups are presented according to the
ranking techniques. Kendall and Smith (1939) introduced the Kendall’s
coefcient of concordance to measure the ranking correlation between
different groups (Couso et al., 2018). The literature contains an imper-
ative pointer to assess the comprehensive relationship between rank-
ings, i.e., the so-called Kendall’s coefcient of concordance, as
demonstrated in previous studies (Muhammet Gul, 2018; Walker,
2007).
The key emphasis of current research is exploring as well as quan-
tifying the signicant factors which affect DB specically related to safe
movement on the roads in Budapest by applying the SFAHP technique.
The SFAHP efciently works in the development of a selection frame-
work (DB model) and determination of the most signicant criteria
affecting road safety using experts’ judgments. SFS are incorporated to
provide a reection on the three-dimensional sets. The novelty is indi-
cated as the parameters of the independent membership are assigned.
Accordingly, it evaluates robustly the signicance of DB related to road
safety. In addition, current study measures the degree of concordance
between specied groups of drivers by applying Kendall’s concordance
approach.
Current study is structured as dened in this paragraph. Section 1
includes background studies from the literature covering road accidents,
decision making and analytical tools to investigate DB criteria. In
Section 2, the methodology of current research is described covering risk
perception, linguistic scales and critical behavior ranking along with
measurement of degree of concordance. The results and key ndings
with the categorical analysis and factorial prioritization are described in
Section 3. Ultimately, the last 2 sections demonstrate the conclusion,
limitations, as well as ndings of the study to pursue future works.
2. Methodology
2.1. Questionnaire on the characteristics of the drivers’ behavior
The SFAHP linguistic spectrum is intended to estimate DB factors
related to road trafc safety. As a source, a survey is conducted in
Budapest (PhD based research at Budapest University of Technology and
Economics), Hungary which includes three specied driver groups. To
collect data, we approached to the drivers and conducted interviews on
designed linguistic scales. A linguistic variable is a simple way to iden-
tify complex or poorly dened situations by modifying them into nu-
merical expressions (Xu, 2012). The rst group of drivers (i.e., Group A)
involves drivers from abroad but with Hungarian driving license and
substantial experience in driving. According to Zhou et al. (2020), major
geographical differences occur indicating the distinctive characteristics
associated with the features and antiquity of the areas; these distinctions
should act as a signicant basis in planning safety campaigns and trafc
rules. In Hungary, those citizens from foreign countries can acquire a
driving license who had been staying in the country for six months
before issuing the license. Furthermore, the second group (i.e., Group B)
consists of drivers who have a high level of driving experience (at least
10 years). Based on the research of Karlaftis and Golias (2002), as the
experience in driving increases, the knowledge of trafc rules increases,
and the adequate driving skills are associated with less trafc violations
and collisions. Ultimately, the third group (i.e., Group C) includes young
drivers with a low level of driving experience. In trafc collisions, young
drivers are involved to a greater extent, as found in studies (Chu et al.,
2017; Truelove et al., 2019; Constantinou et al., 2011; ¨
Ozkan & Lajunen,
2006). The study includes 35 participants for each group selected
randomly. These respondents give linguistic decision data as the SFAHP
approach requires. The survey has two sections. The rst section is
designed to gather the participants’ demographics; its results are shown
in Table 1. Moreover, the second section intends to estimate as well as
prioritize DB data regarding road safety, as mentioned in Sections 4 and
5.
2.2. The signicant drivers’ behavior criteria
Evans (2004) stated that DB is the most signicant factor in dening
the overall trafc safety. The study considers well recognized signicant
DB criteria (Farooq et al., 2019) designed on the SFAHP linguistic
structure to compare and investigate the participants’ responses for the
different driver groups. These behavior criteria affect the trafc safety
Table 1
The participants’ demographic characteristics.
Variables Group “A” Group “B” Group “C”
Age
Mean
Std. Dev.
32.2461
5.641
38.2742
3.6721
21.6352
2.7373
Gender *
Mean
Std. Dev.
1.0
0.0
0.8834
0.3532
0.7852
0.3171
Driving experience
Mean
Std. Dev.
3.5232
2.7213
17.3264
2.7145
1.8521
1.0413
Drivers’ occupation **
Mean
Std. Dev.
0.9122
0.5421
1.0
0.0
0.3613
0.6482
* Gender (1 male, 0 female), ** Drivers’ occupation (1 job, 0 student).
S. Moslem et al.
Expert Systems With Applications 238 (2024) 122318
3
Table 2
The DB factors relevance to road accidents.
DB factors Why it is relevance to road safety
Level
1
Violations (F1) Road trafc violations (RTVs) are undeniably a critical factor contributing to road safety risks, posing a signicant threat to the
safety of all road users. These violations encompass a wide range of behaviors, from speeding and reckless driving to running red
lights and driving under the inuence of alcohol or drugs. (Stradling et al., 2000).
Lapses (F2) A comprehensive study conducted in Qatar revealed that lapses in driver behavior emerged as prominent predictors of accident
involvement. This nding underscores the crucial role that lapses play in contributing to road accidents within the region. These
lapses can encompass various aspects of driver behavior, such as inattentiveness, momentary distractions, or a lack of focus on
the road. Understanding the signicance of lapses as predictors is pivotal for devising effective road safety strategies and
interventions (¨
Ozkan & Lajunen, 2006).
Errors (F3) The outcomes of the meta-analysis shed light on a prevalent pattern of cognitive failures, encompassing both errors and slips,
which exhibited a strong association with heightened crash involvement. This comprehensive analysis amalgamated ndings
from various studies and underscored the signicance of these cognitive failures in the context of road safety. Errors, which
encompass more signicant mistakes in judgment or action, and slips, which involve unintentional, minor lapses in
performance, emerged as intertwined factors that contribute to an elevated risk of road accidents (Farooq et al., 2019).
Level
2
Ordinary violations (F11) The ndings of this study underscored the signicant role of ordinary violations in relation to crash involvement. These ordinary
violations, which may often be perceived as minor infractions of trafc rules and norms, were revealed to carry substantial
implications for road safety. While they might not be as overtly conspicuous as aggressive violations, their cumulative effect on
crash risk is not to be underestimated (Farooq et al., 2019).
Aggressive violations (F12) The study’s ndings brought to light the substantial involvement of aggressive violations in road crash occurrences. These
violations, characterized by their blatant disregard for trafc rules and safety norms, emerged as pivotal factors contributing to
heightened crash risk. The signicance of this discovery cannot be overstated. It underscores the critical importance of
addressing aggressive violations in any comprehensive road safety strategy. Effectively curbing aggressive violations through
awareness campaigns, law enforcement, and targeted interventions can potentially lead to a substantial reduction in road
accidents and enhance overall road safety (Evans, 2004).
Drivers’ inattention (F21) In the comprehensive study conducted by Klauer et al. (2011), a rather alarming revelation came to the forefront regarding the
role of driver inattention in trafc conicts. Their research indicated that approximately 25–30 % of trafc conicts could be
directly associated with drivers’ inattention, a signicant proportion in itself. However, what made their ndings even more
striking was the suggestion that the actual involvement of inattention might be substantially higher than initially estimated. In
fact, their study proposed that the true extent of inattention’s contribution to trafc conicts could potentially reach as high as
70 %. This revelation serves as a stark reminder of the critical need to address issues related to driver inattention on the road. It
highlights the pervasive nature of this problem and its far-reaching implications for road safety.
Pulling away from trafc lights in
wrong gear (F22)
The study conducted in the United Kingdom drew attention to a specic driving behavior categorized as an “aberrant DB. This
behavior was identied as ”Pulling away from trafc lights in the wrong gear.“ This nding sheds light on a rather peculiar but
potentially risky driving habit observed among motorists. F22 implies that some drivers, when starting from a standstill at trafc
lights or intersections, engage their vehicles in an inappropriate gear. This action can result in various negative consequences,
including reduced control over the vehicle, increased wear and tear on the engine and transmission, and, most importantly, a
potential safety hazard for both the driver and other road users (Bener et al., 2008).
Hitting something unseen when
reversing (F23)
In the realm of trafc safety research, the analysis of factors contributing to accidents plays a pivotal role in understanding the
dynamics of road incidents. One noteworthy nding in this regard, as highlighted by Winter and Dodou in their 2010 study, is
the prominence of lapses as a signicant contributing factor to accident involvement. Lapses, in the context of driver behavior,
refer to instances where drivers exhibit momentary lapses of attention or judgment while on the road. These lapses can include
instances of absentmindedness, forgetfulness, or brief distractions that divert a driver’s focus from the task of driving. What
makes lapses particularly concerning is their potential to lead to accidents, despite being momentary and seemingly minor in
nature.
Failure of visual perception (F31) In the realm of road safety research, understanding the role of perception failure is paramount to improving safety measures for
all road users. A signicant study by Rowe and colleagues in 2014 shed light on the prevalence of perception failure as a
common factor in road crashes. Perception failure, as the term suggests, encompasses situations where there is a breakdown in
the ability to perceive and respond to critical information on the road. This failure can occur on the part of not only the vehicle
driver but also other road users, such as pedestrians or cyclists. It is often rooted in a variety of factors, including distractions,
impaired visibility, misinterpretation of signals, or a lack of awareness of one’s surroundings.
Wrong visual scanning (F32) In the realm of safe driving, the importance of wide visual scanning cannot be overstated, as highlighted by Klauer and
colleagues in their pivotal study in 2006. Visual scanning, in essence, refers to the systematic and comprehensive process of
visually surveying one’s surroundings while driving. The study’s ndings shed light on the critical role that wide visual scanning
plays in ensuring road safety. When drivers engage in wide visual scanning, they effectively expand their eld of vision, allowing
them to detect potential hazards, obstacles, and other road users that might otherwise go unnoticed. This enhanced awareness of
the driving environment enables drivers to make more informed and timely decisions, which are essential for avoiding
accidents.
Failure in applying brakes in road
hazards (F33)
The introduction of a hazard-based duration model marked a signicant advancement in understanding and analyzing drivers’
braking behavior, as demonstrated in the work by Stradling and colleagues in 2000. This innovative approach departed from
traditional methods of studying braking patterns and delved into the intricate interplay of vehicle dynamic variables. In essence,
a hazard-based duration model is a statistical tool that seeks to unravel the complexities of how and when drivers apply brakes in
response to potential hazards on the road. It takes into account a multitude of factors, such as vehicle speed, road conditions,
trafc density, and driver characteristics, to discern the precise triggers for braking actions.
Level
3
Failure to use personal intelligence
(F111)
The concept of an intelligent transport system (ITS) heralds a transformative shift in the realm of road safety, as underscored by
Bener and colleagues in their work in 2013. Traditionally, road safety efforts have often focused on mitigating the consequences
of accidents, such as reducing injury severity and enhancing post-crash care. While these measures are undeniably crucial, ITS
introduces a paradigm shift by placing a greater emphasis on preventing accidents from occurring in the rst place.
Failure to maintain the safe gap (F112) Gap-acceptance, as highlighted in the ACEM (2004) study, stands out as a critical determinant of trafc safety at intersections.
Intersections are dynamic spaces where the paths of various vehicles intersect, often at different speeds and with varying
intentions. Ensuring safe and efcient trafc ow at these junctures is paramount for preventing accidents and maintaining
smooth transportation.
Changing the lanes frequently (F113) Risk exposure level, as dened by Pradhan et al. (2014), measures the duration during which a vehicle is exposed to potential
crash risk while executing lane changes. This temporal assessment of lane change hazards is critical for comprehending safety
dynamics, facilitating the development of effective safety interventions, and advancing trafc safety research. Pradhan’s
contribution in this regard enhances our understanding of lane change behaviors and their implications for road safety.
(continued on next page)
S. Moslem et al.
Expert Systems With Applications 238 (2024) 122318
4
signicantly, and these were considered substantial as mentioned in
previous studies as shown in Table 2. For study purposes, the criteria are
structured in a hierarchical arrangement, which consists of three levels,
and organized alphabetically; thus, each criterion can be weighted
systematically. Level 1 consists of signicant DB criteria such as ‘Vio-
lations’ (F1), ‘Lapses’ (F2), and ‘Errors’ (F3). While Level 2 and Level 3
consists of sub-criteria as shown in Fig. 1.
Table 2 presents the main and sub-factors related to road accidents.
2.3. Steps of the suggested method
Fuzzy logic is a mathematical framework that deals with uncertainty
and imprecision by allowing variables to take on partial or “fuzzy”
values between 0 and 1. It is particularly useful in situations where data
or information is not precise and can be vague or ambiguous (Zadeh,
1965). The SFS theory depends on the function, such as the spherical
surfaces, and it relies on the concept through dening the membership
functions, which can be utilized by the decision-makers for different
types of sets. Fga technique to solve practical problems by experience
rather than knowledge. The SFAHP model provides an opportunity for
decision-makers to replicate their uncertainties in the procedure of
decision-making through SFS by applying linguistic scales (Gündo˘
gdu &
Kahraman, 2019).
SFS are specically designed to handle directional or angular data. In
the context of your study on road safety, they allow you to represent
preferences regarding different aspects of driving behavior, such as
“Aggressive Violations,” “Ordinary Violations,” and “Driving with
Alcohol Use,” in a way that considers the circular nature of the scale. By
utilizing SFS within the Analytic Hierarchy Process (AHP), you can
assess and quantify driver preferences more accurately. The SFS-based
AHP helps in understanding which aspects of road safety behavior
drivers prioritize and how their preferences are distributed along the
circular scale.
SFS are constructed using membership functions that wrap around a
circular scale, making them suitable for handling directional data. They
contribute to the analysis by providing a more accurate representation
of driver preferences and enabling assessments of agreement among
driver groups. Clarifying these aspects in the paper would enhance the
understanding of how SFS are applied and their signicance in the
research. The suggested SFAHP framework has eight main steps, which
are presented in the following paragraphs.
Step 1. Forming the hierarchical structure (HS) of the problem
As the rst step, a HS involving minimum three levels are created.
Level 1 aims to select the most adequate choice according to the score
index (SI). The SI is evaluated based on a list of criteria C=
{C1,C2, ......Cn}, as presented at Level 2. At Level 3, some sub-criteria
claried for a criterion C in this HS can be found. Thus, a set of m
alternative X= {x1,x2,......xm}(m⩾2) is stated at Level 4, as well as a set
of s suitable decision-makers is obtained (Kutlu Gündo˘
gdu & Kahraman,
2020).
Step 2. Constructing pairwise comparison matrix (PCM)
Various linguistic procedures or techniques are required to be
incorporated in the decision-making procedure. In the spherical fuzzy
(SF) analysis, the PCMs are founded according to the linguistic labels of
signicance, which is presented in Table 3.
For each u, the values
μ
˜
AS(u),
ν
˜
AS(u), and
π
˜
AS(u)show the degree
of membership, non-membership, and the hesitancy of u to
AS, respec-
tively.
Equations (1) and (2) are applied to acquire the SIs in Table 2.
SI =
100*
μ
˜
As−
π
˜
As2
−
ν
˜
As−
ν
˜
As2
(1)
1
SI =1
100*
μ
˜
As−
π
˜
As2
−
ν
˜
As−
ν
˜
As2
(2)
Step 3. Consistency check
For each PCM, the consistency ratio (CR) is computed. To achieve
this aim, the linguistic labels of the PCM are switched to their corre-
sponding SIs provided in Table 2. Afterward, the consistency check ratio
calculation is applied (Subramaniam et al., 2007).
It is set that 10 % is the threshold of the CR. For example, PCM =
F1
F2
F3
EI EI SMI
EI EI HI
SLI LI EI would be converted to PCM =
F1
F2
F3
1 1 3
1 1 5
1/3 1/5 1 ,
and after the traditional computing process for the CR, the result would
be 0.0251. It is considered as an acceptable value since it is lower than
0.1, and the result refers to the consistency of the PCM.
Table 2 (continued )
DB factors Why it is relevance to road safety
Disobeying speed limits (F121) Speeding, identied as a widespread and severe aberrant driving behavior, poses signicant risks not only to the offenders but
also to public safety at large (Bella & Silvestri, 2015). This behavior, characterized by exceeding established speed limits,
demands urgent attention and countermeasures to mitigate its adverse impacts on road safety and reduce the potential for
accidents, injuries, and fatalities.
Failing to yield pedestrian (F122) Concerning the causal factors of pedestrian fatalities, a signicant portion, 22.2 %, was attributed to improper crossings, while
14.2 % resulted from a failure to yield at pedestrian crossings (Khorasani et al., 2013). These ndings underscore the critical
need for enhanced pedestrian safety measures, including education on proper crossing procedures and increased awareness
among both pedestrians and drivers to reduce such preventable fatalities.
Disobeying trafc lights (F123) A notable contributor to the persistently high number of trafc accidents and resulting injuries is the widespread habit of
disregarding trafc signals, as highlighted in the study by Niezgoda et al. (2012). This risky behavior, such as running red lights,
not only endangers the offending drivers but also poses signicant threats to other road users. To mitigate this issue, a
multifaceted approach involving stricter law enforcement, educational campaigns, and technological interventions may be
imperative to enhance road safety and reduce the toll of accidents and injuries.
No deterrence with punishment
(F124)
According to a comprehensive meta-analysis conducted in 2016 and reported by Park et al. (2018), imposing substantial nes
with increases ranging from 50 % to 100 % is associated with a noteworthy 15 % reduction in instances of trafc violations. This
nding underscores the potential effectiveness of signicant nancial penalties as a deterrent to unlawful driving behaviors,
suggesting that robust law enforcement measures and punitive actions can contribute signicantly to enhancing road safety and
reducing trafc rule violations.
Disobeying overtaking the rules
(F125)
In a study conducted by Hassan et al. (2017), it was observed that in the year 2006, incidents of dangerous overtaking were
responsible for a staggering 41 % of all fatalities among drivers involved in trafc accidents. This alarming statistic highlights
the severe consequences associated with reckless overtaking maneuvers, emphasizing the critical need for promoting safe and
responsible driving behaviors to mitigate such tragic outcomes on the road.
Driving with alcohol use (F126) The analysis results, based on expert responses, strongly pointed out that “Driving with alcohol use” stands out as the most
signicant factor contributing to road safety concerns (NHTSA, 2008). This nding underscores the critical importance of
addressing and combatting alcohol-related impaired driving, as it continues to pose a major threat to road safety and the well-
being of all road users. Efforts to reduce incidents of driving under the inuence are paramount in enhancing safety on our roads.
S. Moslem et al.
Expert Systems With Applications 238 (2024) 122318
5
Step 4. Computing local SF weights for each criterion
For this aim, the adopted technique is using the weighted arithmetic
mean where w =1/n.
SWAMw(AS1,AS2,AS3,⋯,ASn ) = w1AS1+⋯.+wnASn
=〈1−
n
i=1
(1−
μ
˜
As
2)wi1/2
,
n
i=1
v˜
Asi
wi,
n
i=1
(1−
μ
˜
As
2)wi−
n
i=1
(1−
μ
˜
As
2−
μ
˜
As
2)wi1/2
〉
(3)
Step 5. Computing the weighted decision matrix as well as
nding global weights
The computation process is continued, but defuzzication is not
conducted. Here, fuzzy global preference weights are assessed by
applying Equation (4).
n
i=1
ASij =
ASi1⊗,⊗
ASin ∀i
i.e.
ASi1⨂
ASi2= 〈
μ
˜
As11
2
μ
˜
As12
2,v˜
As11
2+v˜
As12
2−v˜
As11
2v˜
As12
21/2
,
1−v˜
As12
2
π
˜
As11
2+1−v˜
As12
2
π
˜
As11
2−
π
˜
As11
2
π
˜
As12
21/2
〉
(4)
For the factors, SFAHP ultimate score
Fis obtained by adding SF
arithmetic of preference weight, as shown in Equation (5).
F=
n
j=1
ASij =
ASi1⊗,⋯,⊕
ASin ∀i
i.e.
ASi1
⊕
ASi2⊗ = 〈
(v˜
As11
2+v˜
As12
2−v˜
As11
2v˜
As12
2)1/2,v˜
As11
2v˜
As12
2,
1−v˜
As12
2
π
˜
As11
2+1−v˜
As12
2
π
˜
As11
2−
π
˜
As11
2
π
˜
As12
21/2
〉
(5)
Step 6. Defuzzifying the ultimate scores of the alternatives by
applying the score function provided in Eq. (6)
SI(ws
j) =
100*3
μ
˜
As−
π
˜
As/22
−
ν
˜
As/2−
ν
˜
As2
(6)
Step 7. Ordering the choices regarding the ultimate scores
The best alternative has the largest value.
Step 8. Ranking the factors
The most important factor has the highest value.
2.4. Kendalls correlation model
Kendall’s model is a non-parametric principal test, founded on cor-
respondence coefcients and aims to assess the dependent association
between two specied random variables (Kendall & Smith, 1939).
Kendall’s W technique is a standardization of the indicator of the
Friedman test, which is a statistic method and might be easily adopted
for measures to acquire the degree of similarity between various eval-
uators (Goldenbeld, 2017).
In current study, to highlight the degree of similarity (i.e., concor-
dant degree) among the various groups of drivers at each specied level
in the hierarchy model, Kendall’s coefcient of concordance is applied.
The span of Kendall’s concordance degree (W) is between 0 (i.e., no
similarity) and 1 (i.e., full similarity); moreover, in Table 4, the inter-
pretation of the numbers in the range of 0–1 is presented. The compu-
tation process begins with the aggregation of the rating of factor i
described as follows:
Ri=
n
j=1
rij,(7)
where Ri stands for the aggregated ranking of factor i, rij represents the
ranking provided to factor i by the evaluator group j, and n stands for the
number of the groups ranking m factors.
Afterward, R, i.e., the mean of the Ri values, is calculated.
R=n(m+1)
2.(8)
K=
n
i=1
(Ri−R)2,(9)
where K represents a sum-of-squares deviation of the row sums of
ranking Ri.
Afterward, Kendall’s W value is found as a number from 0 to 1by
using equation (10).
W=12K
n2(m3−m).(10)
As the formula is applied, the gained value estimates the extent of
similarity between the participating rater groups.
SFS contribute to the analysis by enabling the calculation of agree-
ment levels among hierarchical driver groups using methods like Ken-
dall’s test. This allows you to assess how similar or dissimilar
preferences are among different driver groups for various road safety
aspects.
3. Results
The SFAHP procedure is applied to determine the level of signi-
cance for DB criteria related to road accidents based on evaluator
groups’ responses for a specied level. Through pairwise comparisons,
the designated criteria and sub-criteria are compared. Then the
weighted decision matrix is developed, and SF global preference weights
are computed for each driver group in a three-level model. The results of
SF weight (SFW) and crisp weight (CW) values are depicted in the
following tables: (see Tables 5–19).
In current research work, the importance of DB criteria associated
with road safety is calculated by using the SFAHP techniques. For Level
1, the results revealed that Groups B and C indicate the same ranking for
the DB criteria. Accordingly, the results show that the most important
criterion is ‘Lapses’ (F2), followed by ‘Errors’ (F3). A previous research
work founf that “Lapses” (F2) are reported more commonly by Qatari
drivers than by other driver groups from different countries (i.e., Jor-
danians, Indians, and Filipinos) (Bener et al., 2008). Additionally,
research found that a driver who commits lapses is generally a novice
driver, who has not learned the routines for driving and probably needs
more practice (Farooq & Moslem, 2019). However, in case of Group A,
‘Errors’ (F3) is observed as the rst critical criterion, where ‘Violations’
(F1) is the second in the ranking, as presented in Table 20 (Fig. 2).
For Level 2, the priority ranking standards are measured through the
SAFHP approach by the evaluators as depicted in Table 21. The results
highlight the “Aggressive violations” (F12) as the most critical factor by
all specied groups. More trafc violations are committed by those
drivers having high values for the trait aggressiveness than by those
making less scores (Zeshui & Cuiping, 1999). While “Ordinary viola-
tions” (F11) is observed as the last-ranked criterion (Fig. 3).
For Level 3, the SFAHP approach measures the ranking criteria by
the driver groups as shown in Table 22. The results highlight “Driving
with alcohol use” (F126) as the most critical factor for all specied
groups, followed by the second-ranked factor “Disobeying trafc lights”
(F123). The Hungarian driving laws states that there is zero-tolerance
with driving with drinking, (WHO, 2018). Additionally, “Failing to
S. Moslem et al.
Expert Systems With Applications 238 (2024) 122318
6
yield pedestrian” (F122) is found in higher ranking (i.e., 3rd) evaluated
by Groups A and B. In Hungary, drivers from abroad are detected to
show explicit behavior, for example, they fail to give way to the vehicle
on the right, which might be a root of collisions (see Fig. 4).
3.1. Results of the similarity check (W)
In the second model of the research work, the value of W is deter-
mined by measuring the degree of agreement among the driver groups
for each level. The obtained similarity value, denoted as W, quanties
the degree of similarity among the criteria used in this study. In our
analysis, As represented in Table 23, in case of Level 1. W was deter-
mined to be 0.333, signifying a relatively weak level of similarity among
these criteria.
The weak similarity among criteria has several implications for our
road safety analysis. First, it underscores the multifaceted nature of DB
and road safety. The fact that the criteria exhibit weak similarity
suggests that various aspects of DB and their impact on road safety are
not directly interchangeable or correlated. This weak similarity among
criteria implies that road safety policies and interventions should adopt
a comprehensive approach. Rather than assuming that improvements in
one criterion will necessarily impact another, it is crucial to tailor in-
terventions to address the specic factors that are relevant to a partic-
ular context or group of drivers.
The weak similarity among criteria highlights the intricate nature of
DB and road safety. It underscores the need for a multifaceted and
context-specic approach to improving road safety outcomes, which our
study aims to address through the integration of AHP within a SF
framework.
For Level 2, the value of W is determined, as well. According to the
concordance value (W =0.818), high similarity is found among the
criteria, as shown in Table 24. At Level 2, the sub-factors show some
similar prominent features related to DB affecting road safety. The
nding of high similarity carries several noteworthy implications for our
research and its relevance to road safety analysis. It underscores the
interconnectedness and interdependence of the selected criteria, indi-
cating that changes or variations in one criterion are likely to have a
substantial inuence on the others. This observed high similarity sug-
gests that, at Level 2 of our analysis, the hierarchical driver groups
exhibit a remarkable level of agreement regarding the importance of
these criteria. This consistency in their perceptions can guide road safety
policymakers and stakeholders in prioritizing interventions and strate-
gies that address the common factors inuencing DB.
Furthermore, at Level 3, the value of W is estimated, as well. The
similarity value (W =0.918) at this level presents a high similarity
among the criteria, which is depicted in Table 25. There are some
dominant features of DB affecting road safety that are similar for the sub-
factors at Level 3. The exceptionally high similarity value indicates that
irrespective of their demographics or driving experience, all hierarchical
driver groups place signicant importance on the criteria considered at
Level 3. This unied agreement reects a shared understanding of the
critical factors inuencing driving behavior in the context of road safety.
Fig. 1. The three levels of DB factors (Farooq et al., 2019).
Table 3
The employed linguistic labels for pairwise comparisons (Gündo˘
gdu & Kahra-
man, 2019).
(
μ
,
ν
,
π
) SI
AMI (0.9, 0.1, 0) 9
VHI (0.8, 0,2, 0.1) 7
HI (0., 0., 0.) 5
SMI (0.6, 0. 4, 0.3) 3
EI (0.5, 0.4, 0.4) 1
SLI (0.4, 0.6, 0.3) 1/3
LI (0.3, 0.7, 0.2) 1/5
VLI (0. 2, 0. 8, 0. 1) 1/7
ALI (0.1, 0.9, 0) 1/9
Table 4
Kendall’s degree of agreement (Goldenbeld, 2017).
Coefcient Explanation
1 Perfect similarity
0.9 to 1 Very high similarity
0.7 to 0.9 High similarity
0.4 to 0.7 Medium similarity
0.2 to 0.4 Low similarity
0 to 0.2 Very low similarity
0 No similarity
Table 5
The ultimate SFWs for the main factors at Level 1 for Group A.
SFW CW
F1 0.538 0.442 0.308 0.348
F2 0.368 0.602 0.295 0.229
F3 0.640 0.342 0.290 0.423
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Expert Systems With Applications 238 (2024) 122318
7
The ndings at Level 3 afrm the practicality of our research approach,
which employs the Analytic Hierarchy Process (AHP) within a SF
framework based on SFSs. This approach effectively captures the
consensus among driver groups and facilitates the identication of
common ground for road safety improvements. So, the substantial
similarity value (W =0.918) observed at Level 3 signies a profound
consensus among hierarchical driver groups regarding the critical
criteria that shape driver behavior and road safety outcomes. This
consensus not only informs strategic interventions but also validates the
robustness of our research approach in capturing shared perceptions and
priorities among diverse driver groups.
4. Discussion
Recently, the researchers are very actively working for improving a
sustainable transportation system. Thus, authorities must concentrate
on evolving efcient trafc systems that support an increase in safety to
reduce cost and continually improve the DB. Therefore, the identica-
tion of the signicant DB criteria is among the most essential decision-
making issues for sustainable transportation system. In current
Table 6
The ultimate SFWs for sub-factors related to ‘Lapses’ at Level 2 for Group A.
SFW CW
F21 0.379 0.592 0.304 0.235
F22 0.486 0.479 0.327 0.309
F23 0.681 0.308 0.266 0.456
Table 7
The ultimate SFWs for the sub-factors related to ‘Errors’ at Level 2 for Group A.
SFW CW
F31 0.665 0.314 0.282 0.431
F32 0.344 0.633 0.281 0.207
F33 0.569 0.418 0.298 0.362
Table 8
The ultimate SFWs for the sub-factors related to “Ordinary violations” at Level 3
for Group A.
SFW CW
F111 0.534 0.435 0.316 0.347
F112 0.640 0.342 0.290 0.427
F113 0.362 0.606 0.296 0.226
Table 9
The ultimate SFWs for the sub-factors related to “Aggressive violations” at Level
3 for Group A.
SFW CW
F121 0.386 0.604 0.285 0.117
F122 0.532 0.457 0.305 0.167
F123 0.536 0.454 0.305 0.168
F124 0.367 0.619 0.291 0.110
F125 0.497 0.508 0.283 0.156
F126 0.831 0.173 0.128 0.281
Table 10
The ultimate SFWs for the main factors at Level 1 for Group B.
SFW CW
F1 0.388 0.576 0.308 0.245
F2 0.645 0.338 0.281 0.435
F3 0.495 0.471 0.321 0.321
Table 11
The ultimate SFWs for the sub-factors related to ‘Lapses’ at Level 2 for Group B.
SFW CW
F21 0.509 0.469 0.309 0.314
F22 0.372 0.603 0.298 0.221
F23 0.717 0.274 0.232 0.465
Table 12
The ultimate SFWs for the sub-factors related to ‘Errors’ at Level 2 for Group B.
SFW CW
F31 0.540 0.440 0.308 0.343
F32 0.366 0.604 0.293 0.223
F33 0.663 0.324 0.271 0.434
Table 13
The ultimate SFWs for the sub-factors related to the “Ordinary violations” at
Level 3 for Group B.
SFW CW
F111 0.385 0.582 0.307 0.239
F112 0.680 0.311 0.261 0.456
F113 0.481 0.486 0.321 0.306
Table 14
The ultimate SFWs for the sub-factors related to the “Aggressive violations” at
Level 3 for Group B.
SFW CW
F121 0.306 0.691 0.237 0.090
F122 0.567 0.430 0.284 0.177
F123 0.591 0.408 0.278 0.185
F124 0.395 0.596 0.282 0.118
F125 0.507 0.500 0.272 0.157
F126 0.824 0.181 0.139 0.273
Table 15
The ultimate SFWs for the main factors at Level 1 for Group C.
SFW CW
F1 0.415 0.543 0.329 0.260
F2 0.680 0.304 0.250 0.461
F3 0.443 0.510 0.334 0.279
Table 16
The ultimate SFWs for the sub-factors related to the ‘Lapses’ at Level 2 for Group
C.
SFW CW
F21 0.341 0.640 0.281 0.198
F22 0.551 0.439 0.293 0.337
F23 0.731 0.264 0.229 0.465
Table 17
The ultimate integrated SFWs for the sub-factors related to the ‘Errors’ at Level 2
for Group C.
SFW CW
F31 0.672 0.315 0.264 0.440
F32 0.372 0.598 0.295 0.226
F33 0.528 0.451 0.305 0.334
Table 18
The ultimate SFWs for the sub-factors related to the “Ordinary violations” at
Level 3 for Group C.
SFW CW
F111 0.558 0.421 0.311 0.366
F112 0.378 0.589 0.299 0.237
F113 0.602 0.370 0.312 0.397
S. Moslem et al.
Expert Systems With Applications 238 (2024) 122318
8
research, by using the SFAHP method, an MCDM-based solution is
proposed to emphasize the most serious drivers’ behavior factors asso-
ciated with road safety. In this research, the adopted approach enables
evaluators to replicate their uncertainties in the decision procedure
independently by using a linguistic assessment scale.
A real-world problem based on trafc issues in Budapest is investi-
gated to study the presentation of the projected SFAHP model. The
reliability of the model is veried by using comparative analysis and
consistency, as well. According to the results of current study, the model
is appropriate to analyse the linguistic data, primarily if considering
non-expert as well as diverse respondents’ basis in a complex decision-
making process. It is further revealed that the methodology applied in
this study is robust. The SFAHP method provides a comprehensive
domain of membership function descriptions to the decision-makers,
which is a main benet of the method. However, in its present format,
the model cannot make primary distinction between the xed group
measures in the decision. Practically, when making the absolute deci-
sion, a particular stakeholder (e.g. administration) provides an upper
weight more than the others. It is not covered in current prospect as this
need does not appear in this case study. On the other hand, in the SFAHP
methodology, it is feasible to combine priori stakeholder measures,
solely an additional calculation phase should be performed. For further
research, SFAHP is suggested to be matched with other extensions of the
MCDM approach, for example, the intuitionistic fuzzy AHP and the
Pythagorean fuzzy AHP. Furthermore, it is proposed that SF preference
associations and inter-valued SFS should be applied in SFAHP. Addi-
tionally, the proposed model can be utilized to assess and prioritize
other signicant factors related to road safety such as pedestrian safety,
vehicles and environment.
The work has some limitations regarding the methodology to be
considered when representing the executed ndings. The dataset is
collected in one big city without the inclusion of rural involvement.
Therefore, the sample might not represent the whole driver population.
The work successfully evaluated the driving behavior among 105 eval-
uators in Hungary, however, the evaluator numbers recruited might be
small considering the total driver numbers in Hungary. While the limi-
tations of Kendall’s coefcient method include the following: the entire
distribution is unknown, and the data are not bivariate in normal
distribution.
5. Conclusions
Assessing the underlying causes of road trafc accidents using con-
ventionalAHP methodologies often encounters uncertainty, primarily
due to the complex and uncertain nature of human behavior. In this
current research, a recognised and robust MCDM approach, specically
the SFAHP, is accurately utilized to detect, compare, and prioritize
critical drivers’ behavior criteria, along with their corresponding sub-
criteria, across distinctive driver behaviour components. The SFAHP
methodology establishes the quantication of weights attributed to the
signicant DB criteria and sub-criteria, thereby facilitating the hierar-
chical ranking of these measures for each designated driver group. At
Level 1, the ndings prominently highlight ’Lapses’ as the dominant
criterion, followed by ’Errors’ for Groups B and C. This reveals the need
for extended training and better awareness among these specic driver
groups to correct the identied issues. At Level 2, the prominent ranking
of “Aggressive violations” as a critical factor across all designated groups
is a signicant observation. Addressing the noticeable sub-factors
“Aggressive violations” (F12) could be effectively comprehended
through the strict enforcement of trafc regulations and the imple-
mentation of advance safety measures. Furthermore, at Level 3, “Driving
with alcohol use” (F126) is ranked highest among all specied driver
groups. This resemblance aligns conventionally with Hungary’s present
driving regulations, which enforce a zero-tolerance policy toward
alcohol use while driving vehicles. Moreover, the application of Ken-
dall’s concordance analysis method validates these hierarchical nd-
ings, revealing a moderate agreement at Level 1 and notably stronger
agreements at Levels 2 and 3.
Considering uncertain linguistic parameters, the engagement of
domain experts reveals certain challenges during the evaluation process.
However, the utilization of measurements within a SF environment
effectively mitigates these issues, thereby maintaining the depth and
accuracy of expert opinions. Considering prospective research paths, it is
recommended that the SFAHP method be subjected to further evaluation
in conjunction with other extensions of the MCDM paradigm. This could
potentially enhance the method’s robustness and extend its applicability
in addressing multifaceted decision-making challenges. Moreover, the
model can be used to estimate the risky factors related to road safety of
Automated Vehicles (Shang et al., 2023; Yang et al., 2023).
Table 19
The ultimate SFWs for the sub-factors related to the “Aggressive violations” at
Level 3 for Group C.
SFW CW
F121 0.374 0.612 0.291 0.111
F122 0.361 0.629 0.286 0.107
F123 0.688 0.330 0.211 0.222
F124 0.448 0.539 0.302 0.135
F125 0.463 0.529 0.300 0.141
F126 0.856 0.143 0.112 0.285
Table 20
The ultimate factors weight in Level 1.
Group “A” Group “B” Group “C”
Factor Weights Ranking Weights Ranking Weights Ranking
F1 0.348 2 0.245 3 0.260 3
F2 0.229 3 0.435 1 0.461 1
F3 0.423 1 0.321 2 0.279 2
Fig. 2. Factors’ ranking based on the three groups preferences in Level 1.
Table 21
The ultimate priority of factors in Level 2.
Group “A” Group “B” Group “C”
Factor Weights Ranking Weights Ranking Weights Ranking
F11 0.043 8 0.021 8 0.039 8
F12 0.305 1 0.224 1 0.221 1
F21 0.054 7 0.136 4 0.091 6
F22 0.071 6 0.096 6 0.155 3
F23 0.104 4 0.202 2 0.214 2
F31 0.182 2 0.110 5 0.123 4
F32 0.088 5 0.072 7 0.063 7
F33 0.153 3 0.139 3 0.093 5
S. Moslem et al.
Expert Systems With Applications 238 (2024) 122318
9
Funding
Project no. TKP2021-NVA-02 has been implemented with the sup-
port provided by the Ministry of Culture and Innovation of Hungary
from the National Research, Development and Innovation Fund,
nanced under the TKP2021-NVA funding scheme.
Fig. 3. Factors’ ranking based on the three groups preferences in Level 2.
Table 22
The ultimate priority of factors in Level 3.
Group “A” Group “B” Group “C”
Factor Weights Ranking Weights Ranking Weights Ranking
F111 0.015 8 0.005 9 0.014 8
F112 0.018 7 0.009 7 0.009 9
F113 0.010 9 0.006 8 0.015 7
F121 0.036 5 0.020 6 0.024 5
F122 0.051 3 0.040 3 0.024 6
F123 0.051 2 0.042 2 0.049 2
F124 0.034 6 0.027 5 0.030 4
F125 0.048 4 0.035 4 0.031 3
F126 0.086 1 0.061 1 0.063 1
Fig. 4. Factors’ ranking based on the three groups preferences in Level 3.
Table 23
W value among all evaluator groups for Level 1.
Factor Ranking of
Group “A”
Ranking of
Group “B”
Ranking of
Group “C”
Ri (Ri−R)2
F1 2 3 3 8 4
F2 3 1 1 5 1
F3 1 2 2 5 1
n =3 m =3 S =6 R =6 W =0.333
Table 24
W value among all evaluator groups for Level 2.
Factor Ranking of
Group “A”
Ranking of
Group “B”
Ranking of
Group “C”
Ri (Ri−R)2
F11 8 8 8 24 110.25
F12 1 1 1 3 110.25
F21 7 4 6 17 12.25
F22 6 6 3 15 2.25
F23 4 2 2 8 30.25
F31 2 5 4 11 6.25
F32 5 7 7 19 30.25
F33 3 3 5 11 6.25
n =8 m =3 S =308 R =13.5 W =0.818
S. Moslem et al.
Expert Systems With Applications 238 (2024) 122318
10
CRediT authorship contribution statement
Sarbast Moslem: Conceptualization, Methodology, Formal analysis,
Writing – original draft. Danish Farooq: Conceptualization, Investiga-
tion, Data curation, Writing – original draft. Domokos Eszterg´
ar-Kiss:
Validation, Writing – review & editing, Supervision, Funding acquisi-
tion. Ghulam Yaseen: Resources, Data curation, Writing – review &
editing. Tapan Senapati: Writing – review & editing, Visualization,
Supervision. Muhammet Deveci: Visualization, Supervision, Writing –
review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
Data will be made available on request.
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W value among all evaluator groups for Level 3.
Factor Ranking of
Group “A”
Ranking of
Group “B”
Ranking of
Group “C”
Ri (Ri−R)2
F111 8 9 8 25 100
F112 7 7 9 23 64
F113 9 8 7 24 81
F121 5 6 5 16 1
F122 3 3 6 12 9
F123 2 2 2 6 81
F124 6 5 4 15 0
F125 4 4 3 11 16
F126 1 1 1 3 144
n =9 m =3 S =496 R =15 W =0.918
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