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PREDICTION MODEL OF MOTORCYCLE ACCIDENT IN ECONOMIC AND
DRIVING BEHAVIOUR FACTORS
ArticleinEastern-European Journal of Enterprise Technologies · August 2022
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PREDICTION MODEL
OF MOTORCYCLE
ACCIDENT IN
ECONOMIC AND
DRIVING BEHAVIOUR
FACTORS
Friska Putri
Corresponding author
Bachelor of Engineering*
Е-mail: friskaferonica9@gmail.com
Muhammad Arifin
Doctor of Civil Engineering*
Ludfi Djakfar
Professor of Civil Engineering*
*Department of Civil Engineering
Brawijaya University
Veteran str., Ketawanggede,
Malang, East Java, Indonesia, 65145
The number of motorized vehicles, especially motor-
cycles, is also offset by increased traffic accidents. As
is known, road accidents essentially depend on four
interrelated factors: human behavior, vehicle efficien-
cy, environmental conditions, and the characteristics of
the infrastructure. However, most accidents are attribu-
table to the first three factors, almost always to impro-
per user behavior. This study aims to determine motor-
cyclists’ socio-economic characteristics and conduct on
the intensity of accidents. The research location is on the
Pandaan-Purwosari National Road, Pasuruan Regency,
Section 094-098 (Surabaya-Malang). Three hundred
forty respondents are motorcyclists who have experi-
enced accidents in this segment. The research method
is interviews and questionnaires — data analysis using
Structure Equation Modeling (SEM), with software
SmartPLS (Partial Least Square).
The result of accident modeling Y = 0.299X1+0.154X2+
+0.077X3+0.554X4. The first biggest influence on the
chance of an accident is the characteristics of driving
behavior (X4) exceeding speed (X4.10). The more often
the rider exceeds the rate, the higher the chance of an ac-
cident. The second most significant influence of socio-eco-
nomic characteristics (X1) is the age indicator (X1.2),
the more mobility in the productive age, the higher the
risk of accidents
Keywords: traffic accidents, motorcyclist, behavior,
demographics characteristics, structural equation mo del-
ing (SEM), SmartPLS (Partial Least Square)
UDC 614
DOI: 10.15587/1729-4061.2022.263651
How to Cite: Putri, F., Arifin, M., Djakfar, L. (2022). Prediction model of motorcycle accident in economic and driv-
ing behaviour factors. Easter n-European Journal of Enterp rise Technologies, 4 (3 (118)), 27–33. doi: https://doi.o rg/
10.15587/1729-4061.2022.263651
Received date 17.06.20 22
Accepted date 19.08.2022
Publi shed date 31.08. 2022
1. Introduction
Traffic accidents are caused by the malfunction of a sys-
tem, namely, vehicles, road infrastructure, road users, and
their interactions [1], and the leading cause of death world-
wide and are expected to be the fifth by 2020 [2]. This is
a problem because 91 % of fatalities occur on the road [3]. Ac-
cording to the Indonesian National Police, the highest number
of accidents in Indonesia occurred in 2019, with 116,441 ca-
sualties. Previous studies have found that the human factor is
the leading cause of accidents [4, 5]. Research has shown that
driving behavior has a positive and significant relationship
with accident involvement. Analysis by [5] revealed that driv-
ing behavior increases the risk of 50 % of having an accident.
The most important part of accident involvement [6, 7].
However, previous studies that have been conducted
have used less-renewable analytical methods and have not
been able to describe the causes of accidents and their rela-
tionship to human factors in detail.
Therefore, research devoted to predicting the develop-
ment of motorcycle accidents has scientific relevance and
needs to be done to minimize the incidence of accidents.
2. Literature review and problem statement
Research conducted by [8, 9] showed that the age factor
also determined the element causing the accident, but this
study did not explain how the relationship between the age
factor and the impact on the occurrence of accidents. How-
ever, the study used a random sample, where respondents
were drawn from those who had or had never had an accident.
SEM (Structural Equation Modeling) is a multivariate
data analysis technique that combines regression, factor, and
path analysis. To estimate the causal relationship of latent
variables at the same time [10, 11].
Road safety for two-wheeled vehicles or motorcycles has
become a global concern. In 2010 there were more than one
billion motor vehicles worldwide [11].
Motorcycles are an essential mode of transportation in
the Indonesian public transportation system and the most
significant contributor to vehicle production in Indonesia.
Of the total population of vehicles (operating units) from
2010 to 2017, motorcycles were used around 82 % and used
by 120 million Indonesians in 2018 [12].
However, there is an unresolved and challenging problem
in transportation, namely accidents. Accidents are hazardous
for road users because accidents can result in physical and
material losses. Accidents are still a significant problem that
must be prevented for safe transportation. Motorcycles are
in great demand by road users for various reasons, and their
use has continued to increase over the years. One of the rea-
sons for their appeal, in terms of mobility, is their compact
size, allowing them to park in small spaces. Its size also helps
motorcyclists to move in and out of traffic quickly. Motorcy-
cles have low mass and aerodynamic drag compared to cars,
Eastern-European Journal of Enterprise Technologies ISSN 1729-3774 4/3 ( 118 ) 2022
28
involve low CO2 emissions, and are, therefore, a fuel-efficient
mode of choice. They are attractive for leisure travel, and
many return users or new users with disposable income are
(re)discovering this activity [12]. However, there are draw-
backs to this bicycle vehicle; for example, on the road with low
traffic, motorcyclists tend to drive at high speed. Motorcycles
only have two wheels meaning the rider can lose control more
quickly than a car user; control can be lost due to uneven road
surfaces, objects on the road, or not understanding traffic
signs [13]. Therefore, motorcyclists are road users who are
very vulnerable to accidents and can suffer serious injuries.
Research by [14] was found that the most significant
driving behavior in shaping the chance of an accident was
exceeding speed. Exceeding speed while overtaking a vehicle
can lead to accidents, and the level of injury caused can lead
to death. Many factors can cause this [1]. Also, disobeying
traffic signs and exceeding speed while driving are some of
the main factors leading to motor vehicle accidents. The
socio-economic level can be roughly shown according to
education level, employment status, and environmental in-
come [15]. Lower socio-economic status is associated with
a higher risk of road traffic injury [8, 16] and mortality [17].
Previous research also found that not only demographic fac-
tors but also socio-economic statuses such as individual edu-
cation level, gender, age, and occupation affect the number of
deaths due to road traffic accidents [18, 19].
As a result of the increasing number of traffic accidents,
especially by motorcycles, several steps have been taken to
address these problems, such as improving road conditions
and lighting. However, research by [1] states that improve-
ments in road conditions, the environment, and road lighting
conditions can affect the driver’s perception of speed choices
that can affect road safety. Factors such as people, roads, ve-
hicles, and environmental/weather conditions contribute to
road accidents. Drivers’ beliefs in these factors can influence
road behavior. In an attempt to understand behavior, previ-
ous researchers also discussed the issue of driving regulation
violations and aggressive behavior relative to traffic acci-
dents [20]. Several researchers evaluated the impact of these
factors on driver performance and have linked road accidents
with damaged infrastructure and inexperience [21].
Accidents can still happen at any time; therefore, the
parties involved need to be responsible and make maxi-
mum efforts to overcome these problems. In this regard,
research conducted by [22] has investigated the factors that
cause accidents, namely driver behavior and socio-economic
factors. However, this study uses an analysis method that is
not renewable and cannot describe the causes of accidents
and their relationship to human factors in detail. The dis-
cussion of the causes of accidents involves only one or two
elements. Meanwhile, the current research conducted by the
author relates the four factors that cause accidents based on
a combination of factors that do not yet exist. Therefore,
it is necessary to research the causes of accidents caused
by many factors.
This paper aims to determine the factors that cause ac-
cidents, especially in driving behavior and driver socio-eco-
nomic characteristics, to create safe and secure transporta-
tion with SEM (Structural Equation Modeling). Previous
research using the first generation in SEM with software
lisrel [18], the software is a first generation sem software,
which allows more and more complicated assumptions. The
author uses the second generation of SEM, namely Smart
PLS, which is easier to use, does not require much data, and
automatic data normality makes data analysis easier. How-
ever, vehicles have an unresolved and challenging problem,
namely accidents. Accidents are dangerous for road users
because accidents can result in physical and material losses.
Accidents are still a significant problem that must prevent
for safe transportation.
3. The aim and objectives of the study
The study aims to determine the economic characteristics
and driving behavior of the intensity of accidents to deter-
mine if supporting variables are needed.
To achieve this aim, the following objectives are ac-
complished:
– determining the most critical factor among the pa-
rameters that affect the accidents: Socio-economic Cha-
racteristics (X1), Driving Behavior (X2) against Accident
Characteristics (Y);
– produce a prediction model of motorcycle accidents
on the national road Surabaya-Malang.
4. Materials and method
Based on previous research, discussing the causes of
accidents only involves one or two factors. Meanwhile, the
current research conducted by the author relates the four
factors that cause accidents based on a combination of factors
that do not yet exist. Therefore, it is necessary to research the
causes of accidents caused by many factors.
In the previous study, it is still using a simple method,
namely regression. Therefore, this research complements
previous research, namely analyzing driving behavior and so-
cio-economic characteristics of the high number of accidents
on the Surabaya-Malang national road with SEM (Structu-
ral Equation Modeling) SmartPLS software.
The sample in this study also uses purposive sampling,
where respondents are selected only who have experienced
an accident so that the results of the analysis can be detected
accurately regarding the factors causing the accident.
The research was conducted in Pasuruan Regency, Gem-
pol-Purwodadi National Road (094-098). The population
of 340 respondents was taken based on Issac and Michael’s
tables through the total population of motorcycles in 2019
taken from the Pasuruan Regency in Center Statistics. The
questionnaire only addressed motorcycle riders with an acci-
dent or purposive sample. So that the modeling results will be
significant and valid. The research method is interview and
questionnaire techniques—using SEM (Structural Equation
Modeling) SmartPLS software.
SEM is a multivariate statistical analysis method to
analyze several research variables simultaneously [23, 24].
Factors from humans have a strong influence on the severity
of accidents [19]. Therefore, the authors use SEM because it
is relevant to the research objectives. The research variables
to be analyzed can be seen in Table 1 Research Design below.
Table 1 above shows the research design based on each
indicator of socio-economic characteristics (X1): gender, age,
work, education, income, driving license and vehicle regis-
tration. Movement characteristics (X2); the number of trips,
mileage, travel time, travel time, and destination. Movement
before riding (X3); checking vehicle light, checking brakes,
checking tire condition, checking machine, check driving
Control processes
29
license and vehicle registration and containing fuel, driving
factors; running a red light, breaking the signs, wrong track,
leading to the right and left, give a turn light, check vehicle,
chat, smoke and exceeding the speed limit. Accident charac-
teristics; collision type, vehicle cons, injury, accident time,
and crash type. In the research design in Table 1, all questions
are from previous research. This question or variable is sure
to affect the chance of an accident occurring.
Table 1
Research design
Category Question Scale
Screen Questions
1 Have you ever crossed the National Road 094-098? Yes/No
2 Have you ever had an accident in this section? Yes/No
Socio-economic Characteristics (X1)
X1.1 Gender Nominal
X1.2 Age Ordinal
X1.3 Education Ordinal
X1.4 Work Ordinal
X1.5 Income Ratio
X1.6 Driving License Yes/No
X1.7 Vehicle Registration Yes/No
Movement Characteristics (X2)
X2.1 Number of Trips Ordinal
X2.2 Mileage Ratio
X2.3 Travel Time Nominal
X2.4 Traveling Time Ordinal
X2.5 Travel Destination Ordinal
Characteristics Before Riding (X3)
X3.1 Checking Vehicle lights Ordinal
X3.2 Checking Brakes Ordinal
X3.3 Check tire Condition Ordinal
X3.4 Checking Machine Ordinal
X3.5 Checking Driving License and Vehicle Registration Ordinal
X3.6 Checking Fuel Ordinal
Driving Characteristics (X4)
X4.1 Running a Red Light Ordinal
X4.2 Breaking the Signs Ordinal
X4.3 Being on the Wrong Track Ordinal
X4.4 Leading to the Right Ordinal
X4.5 Leading to the Left Ordinal
X4.6 Give a Turn Light Ordinal
X4.7 Check Vehicle Ordinal
X4.8 Chat Ordinal
X4.9 Smoke Ordinal
X4.10 Exceeding the Speed Limit Ordinal
Accident Characteristics (Y)
Y1 Collision Type Nominal
Y2 Vehicle Cons Ordinal
Y3 Injury Ordinal
Y4 Accident Time Nominal
Y5 Crash Type Ratio
Eastern-European Journal of Enterprise Technologies ISSN 1729-3774 4/3 ( 118 ) 2022
30
The illustration of the flow chart of the data collection
and analysis process is the steps taken by the author in ob-
taining data and conducting data analysis. to determine the
characteristics of motorcyclists and accident characteristics,
as shown in Fig. 1.
5. Results of Prediction Model
of Motorcycle Accident
5. 1. Socio-economic characteristics and ri-
der characteristics
5. 1. 1. Feasibility Test/Model Validity
This test aims to describe how well the indi-
cators in this study can be used as instruments for
measuring latent variables. With a significance of
weight <0.05 (5 %). VIF value <10. As in Table 2,
feasibility test.
Table 2 above shows which indicators influence
accidents with valid values for the feasibility test.
X1.2 represents age, X2.1 represents travel intensity,
X3.5 represents driving license check, X4.10 rep-
resents overspeed, and Y5 represents collision type.
The most powerful and dominant indicator in shap-
ing the characteristic socio-economic variable (X1)
is the age variable (X1.2), with the highest factor
weighting 0.507. Thus, the higher the age, the
higher the risk of being a cause of accidents. The
survey results from respondents show that the age
of 31–35 years (20 %) is a productive age to work
and carry out activities. The indicator that plays the
most role in shaping the Movement Characteristics
variable (X2) is the intensity of the trip (X2.1) with
the highest factor weight of 0.508; thus, the more
frequent trips (draining energy when traveling), the
higher the risk of being a factor causing accidents.
71 % of respondents often do mobility with the
intensity of frequent trips (3–4 times) in one week.
The higher the respondent’s mobility will affect ac-
cidents because high travel mobility makes someone
drive more often. The indicator that plays the most
role in shaping the Behavior Before Driving (X3)
variable is checking the vehicle documents X3.5 with
the highest factor weight of 0.394. Thus, the less of-
ten you contain vehicle documents (negligent), the
higher the risk of being a factor causing accidents.
The most critical indicator in shaping the Behavior
While Driving (X4) variable is exceeding the X4.10
speed limit with the highest weight factor of 0.358.
The vehicle’s speed when driving on the road is directly
proportional to the severity of traffic accidents. According
to WHO, an average speed increase of 1 km/hour causes an
increase in the risk of traffic accident severity.
Table 2
Feasibility test
Latent Variable Observed
Variables
Formative Indicator Factor Weight Test Formative Indicator Indepen-
dence Test (Multicollinearity)
Significance < 0.05 (5 %) = Valid VIF<10 = Eligible
Weight Estimate Significance of Weight Conclusion VIF Conclusion
Character Characteristics (X1) X1.2 0.507 0.000 Valid 3.112 Worthy
Movement Characteristics (X2) X2.1 0.508 0.000 Valid 1.293 Worthy
Behavior Before Riding (X3) X3.5 0.394 0.000 Valid 1.297 Worthy
Behavior While Driving (X4) X4.10 0.358 0.000 Valid 1.309 Worthy
Accident Characteristics (Y)Y5 0.596 0.000 Valid 1.33 Worthy
Fig. 1. Flowchart
Control processes
31
The indicator that plays the most role in shaping the
Accident Characteristics variable (Y) is the type of colli-
sion (Y5) with the highest factor weight of 0.596 thus, the
Accident Characteristics variable (Y) can be seen the most
from the Collision Type indicator (Y5).
The type of collision is also influenced by the attitude of
the driver in driving its vehicle, exceeding speed, changing
lanes, and not focusing when driving can be an indication
of a collision.
5. 1. 2. Dominant Test
The dominant test determines which variables and indi-
cators are the main priority in overcoming accidents so that
can consider it for policymakers to take strategic steps to
minimize the incidence of motorcycle accidents-dominant
test results as shown in Table 3 below.
From the dominant test results in Table 3
above, the first priority that needs to be im-
proved to minimize the incidence of accidents
is the X4 driving behavior variable that ex-
ceeds speed (X4.10). Economic characteristic
variable X1, on the age indicator. (X1.2),
which is the second priority in accident pre-
vention activities. The third priority is the
movement variable X2 on the travel intensity
indicator (X2.1). The fourth priority is the
behavior variable before driving X3, on the
hand of checking the driving license and ve-
hicle license.
5. 1. 3. The Goodness of Fit
This test explains that the path coefficient
formed can represent the observed data. The
total R-square coefficient value ranges from 0.0
to 100.0 %, where the higher the total deter-
mination coefficient value, the higher the path
coefficient can represent the observed data. In
detail, the standard measurement results for
the inner model testing criteria are based on
the total determination coefficient, as shown
in Table 4.
The coefficient of determination (R-squa re)
obtained from the socio-economic Characte-
ristics model (X1) and Behavioral Characte-
ristics (X2) on the Accident Characteris tics (Y)
is 74.9 %, and others influence the remain-
ing 25.1 %.
Table 4
Goodness of Fit
PLS Models R-Square Determination
Character Charac-
teristics (X1) →
Accident
Charac-
teristics
(Y)
0.749 74.9 %
Movement Cha-
racteristics (X2) →
Behavior Before
Riding (X3) →
Behavior While
Driving (X4) →
5. 2. Accident Prediction Model
The path coefficients in the structural model as well as
the weighted values of the manifest variables in the measure-
ment model can be described through the path
diagram of the measurement model and the
structural model in Fig. 1 Path Chart below
variables outside the study. Based on the exist-
ing reference, the R-square value is considered
vital in representing the research conducted.
Model Prediction Y = 0.299X1+0.154X2+
+0.077X3+0.554X4.
Fig. 1 above explains the value of each vari-
able and indicator, where the highest path coef-
ficient of 0.554 is found in the driver’s behavior
variable (X4) with the highest factor weight
of 0.358, namely driving behavior that exceeds
speed (X4.10). And the second highest path co-
efficient of 0.299 socio-economic characteristic
variable (X1), with the highest factor weighting
of 0.507, namely the rider’s age (X1.2).
Table 3
Dominant test
Effect Between Latent Variables Path Co-
efficient
Rank-
ing
Dominant
Variable
Dominant
indicator
Socio-Econo-
mic (X1)
Accident Cha-
racteristics (Y)0.299 2 Second
Priority X1.2 (Age)
Movement
(X2)
Accident Cha-
racteristics (Y)0.154 3 Third
Priority
X2.1 (Travel
Intensity)
Behavior
Before Riding
(X3)
Accident Cha-
racteristics (Y)0.077 4 Fourth
Priority
X3.5 (Checking
Vehicle Regis-
trartion)
Behavior
While Driving
(X4)
Accident Cha-
racteristics (Y)0.554 1 First
Priority
X4.1 (Over
Speed)
. . . . . . .
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Fig. 2. Path Chart
Eastern-European Journal of Enterprise Technologies ISSN 1729-3774 4/3 ( 118 ) 2022
32
6. Discussion of The Model Prediction
The results of the analysis of the causes of accidents using
SEM (Structural Equation Modeling), it is found that the
factors of driving behavior that drives vehicles exceeding
speed and economic characteristics variables on the produc-
tive age indicator have significant results or influence on
the occurrence of accidents (Table 3, dominant test). These
results align with research conducted by [4, 19], where hu-
man factors or driving behavior are still the leading cause of
accidents. The dominant indicator in shaping the behavior
variable when going (X4) is exceeding speed (X4.10) (Ta-
ble 3, dominant test), with a factor weight of 0.358. Thus,
the more you exceed the driving speed, can increase the risk
of an accident. The vehicle’s speed when driving on the road
is directly proportional to the severity of the traffic accident.
The average increase in the rate of 1 km/h causes an increase
in the risk of the severity of traffic accidents.
The dominant indicator in forming the characteristic so-
cio-economic variable (X1) is the age variable (X1.2) (Table 3,
dominant test), with a factor weight of 0.507. The survey
results show that the age of 31–35 years (20 %) is a pro-
ductive age to work and carry out activities. The higher the
age also affects the accident. Age is one of the factors that
cannot separate from the emergence of accident risk [18].
According to [24], age is one of the factors that can affect
the emergence of accidents. Young age allows for fatal acci-
dents due to lack of experience, risky behavior, and ignoring
traffic rules. And compared to drivers of productive age,
drivers aged >50 years have a higher risk of fatal injury in an
accident [9, 25] because health factors at that age begin to
decline, so the perception of risk decreases.
The dominant indicator in shaping the behavior variable
when driving (X4) is exceeding speed (X4.10) (Table 3, do-
minant test), with a factor weight of 0.358. Thus, exceeding
the speed while driving can increase the risk of an accident.
The vehicle’s speed when driving on the road is directly pro-
portional to the severity of the traffic accident. The average
speed increase of 1 km/hour causes an increase in the risk
of traffic accident severity. The road environment can affect
driver choice by influencing the driver’s perception of their
current speed and the speed they think is appropriate for the
road. These influence and their effects on speed can affect
crash rates [26].
This research uses SEM (Structural Equation Modeling)
method with SmartPLS software. The advantages of using
SEM (Structural Equation Modeling) are:
– its ability to handle complex relationships between
variables, where variables can be hypothetical or unobserv-
able (latent variables);
– estimate all coefficients in the model simultaneously so
that one can assess the significance and strength of a particu-
lar relationship in the context of the complete model;
– its ability to consider multicollinearity and measure-
ment error is eliminated so that the coefficients are more
valid [18]. Research [24] using Lisrel, which analyzes driving
behavior, also produces the same variable in the cause of
accidents.
However, the research still uses the second-generation
SEM (Structural Equation Modeling) method; this Smart-
Pls software is more accessible and does not require many as-
sumptions. The drawback of this study is that many variables
have not been analyzed concerning the causes of accidents.
The author’s hope for future research is to include more vari-
ables that cause accidents so that they are more valid and can
overcome existing problems. The analysis method also needs
to be developed using the third generation (Structural Equa-
tion Modeling). Can also developed research in other areas
that have almost the same characteristics.
7. Conclusions
1. Socio-economic characteristics (X1) affect the intensi-
ty of accidents, where the age indicator (X1.2) has the most
significant influence on the power of accidents (Y). There is
a need for socialization in the community, especially among
drivers of productive age, so as not to force them to drive
after many activities. Where in this study, the effective age is
in the spotlight because of the most accidents
2. The prediction model obtained is Y = 0.299X1+0.154X2+
+0.077X3+0.554X4. The highest path coefficient of 0.554 is
found in the driver’s behavior variable (X4) with the highest
weight factor of 0.358. From this model, appropriate handl-
ing steps must be taken, so that the problem of motorcycle
accidents can be resolved properly, such as adding billboards
as a warning to reduce vehicle speed, giving shock markers to
keep drivers focused on driving, and giving warnings to take
breaks if they are traveling long distances.
Conflict of interest
The authors declare that they have no conflict of inte-
rest in relation to this research, whether financial, personal,
authorship or otherwise, that could affect the research and its
results presented in this paper.
Acknowledgments
I am very grateful to Dr. Ir. M. Zainul Arifin, MT, and
Prof. Dr. Ludfi Djakfar MSCE., PhD., IPU, for the guidance
given and sound advice during my studies, and I am grateful
to my parents, brothers and sisters, and my friends who have
helped pray and encourage in the making of this article.
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