Analysing Self-Reported Risky Behaviours of Motorcyclists in Bali using Structural Equation Modelling.

Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015

Analysing Self-Reported Risky Behaviours of Motorcyclists in Bali using
Structural Equation Modelling
Dewa Made Priyantha WEDAGAMA
Department of Civil Engineering, the University of Udayana, Bali, 80361 Indonesia
E-mail: priyantha.wedagama@gmail.com
Abstract: This study investigates cause and effect relationships among risk perception,
positive affect and risky behaviours of motorcyclists in Denpasar, Bali using Structural
Equation Modelling. Questionnaires were distributed to collect age, gender, self-reported
risky riding behaviour, perceived risk and positive affect of motorcyclists. This study found
that positive affect influences more than risk perception on risky riding behaviours.
Accordingly, this shows that motorcyclists on the road are relied more on intuitive than
rational processes. Motorcyclists have also been found to consistently perceive and behave on
such a risky behaviour that may distract them on the road, for instance using a mobile phone
while riding. In addition, riding fast on curves is a behaviour factor which strongly indicates
both rational and intuitive processes of motorcyclists’ decision making while on the road.
Further studies should be more focused on the influence of positive affect of motorcyclists’
ages and gender on risky riding behaviours.
Keywords: Positive Affect, Risk Perception, Risky Behaviour, Motorcyclist.
1. INTRODUCTION

Human factors have long been recognised to significantly influence road accidents (Matthews
and Moran, 1986; Sabey and Taylor, 1980). Previous studies found that road user behaviours
contributed to 95% of road accidents (Sabey and Taylor, 1980; Ulleberg and Rundmo, 2003
in Hongsranagon, et.al, 2011). In fact, these studies were conducted in developed countries.
Many accident studies conducted in developing countries however, indicated the road user
behaviours significantly influencing road accidents (Indriastuti and Sulistio, 2010; Ramli,
et.al, 2014). In addition, in a country where motorcycles are dominant, motorcyclists and their
behaviours become highly significant to influence road accidents and fatalities. For instance,
motorcyclists contributed more than 50% and 80% to road fatalities in Malaysia (Ambak, et.al,
2011) and in Thailand (Swaddiwudhipong, et. al, 1994 in Hongsranagon, et.al, 2011)
respectively. Meanwhile, there were 12,133 road accidents consisting 2,948 fatal and 5,887
serious casualties during the period 2009-2013 in Bali. Of those accidents and casualties,
more than 70% involved motorcyclists (Bali Regional Police, 2014).
A few accident studies conducted in Bali also show that motorcyclists significantly
influenced road accidents and fatalities. Wedagama and and Dissanayake (2010a) found that
motorcyclists at faults and failed to yield on arterial roads affected motorcycle casualties by
more than 33% respectively. In addition, motorist failed to yield affecting about 30% on
motorcycle fatal accidents in Tabanan regency (Wedagama, 2009a). In relation to age, young
motorists (17-26 years old) contributed by 38% to motorcycle fatal and seriously injured on
two arterial roads (Wedagama, 2009b). It is also found that age was about 50% affecting all

vehicle fatalities in Bali (Wedagama and Dissanayake, 2010b). These studies therefore,
indicated that age and behaviour of motorcyclists are considered significant to influence road

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Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015

accidents and casualties in Bali. An understanding of traffic risk behaviours and perceptions
therefore, is useful to predict road users' actions on the road (Hongsranagon, et.al, 2011).
There have been a few studies however, in relation to road user behaviours conducted in
Indonesia. A study by Joewono, et.al (2015) argued that understanding factors which cause
violation of traffic rules is essential to promote road safety in urban roads. The study
conducted in three cities in Indonesia namely Bandung, Yogyakarta and Surabaya. Structural
equation modeling was employed to investigate factors which contribute to traffic violations
by motorcyclists and their causal relationships. The study found that the shortage of
engineering and maintenance conditions of the road infrastructure significantly contributed to
disobedience to traffic rules by motorcyclists in Indonesia.
Meanwhile, motorists theoretically considered dual processes of decision making while
driving or riding on the road. These processes involve a rational and analytic process in one
hand and experimental and intuitive process on the other hand. For instance, risk perception

and positive affect or the psychological affective preferences (i.e., what people like or dislike)
represent rational and intuitive processes respectively. These models have not been used
widely in road safety modelling (Rhodes and Pivik, 2011). A previous study conducted by
Budiartha R.M (2014) however, has recently investigated factors influencing car drivers and
motorcyclists’ risky behaviours in Bali using such models. The risky behaviour models were
measured using two variables consisting risk perception and positive affect following
previous studies by Jonah (1986) and Rhodes and Pivik (2011). In analysing the relationship
among the three variables, Budiartha R.M (2014) used direct effect and mediation analyses
and regression models.
These previous studies indicated that changes are required for road user behaviour to
reduce road accidents. Certainly, human factors including motivations, risk perception or
culture should be carefully considered. As a matter of fact, technological developments of
motor vehicles and infrastructures, including road signs and pavement markings, have almost
reached a satisfactory standard in Bali. A more full comprehension on road user behaviour
therefore, should offer a better improvement in road safety.
This study aims to investigate the influence of risk perception and positive affect on
risky behaviours of motorcyclists in Denpasar, Bali. This study involves a survey of
motorcyclists to collect various data such as personal characteristics, self-reported risky riding
behaviours and decision making data consisting positive affect and risk perception. The
developed model for this study however, is different to those of the relevant past studies

(Jonah, 1986; Rhodes and Pivik, 2011; Budiartha, R.M, 2014). This study focuses on cause
and effect relationships among risk perception, positive affect and risky behaviour of
motorcyclists. The contributing factors are identified using Factor Analysis (FA) and
subsequently the assessment and prediction models are developed using Structural Equation
Modelling (SEM).

2. LITERATURE REVIEW
2.1. Risky Behaviour, Risk Perception and Positive Affect
Risky behaviour on the road may exist via rational process such as risk evaluation by
motorists including the assessment of both advantages and disadvantages of an action. In the
meantime, road user education is considered insufficient to encourage safer driving (Rhodes
and Pivik, 2011). More than rational models therefore, are required to comprehend risky
driving/riding behaviours.
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Meanwhile, risky driving/riding behaviour is defined as an unbearable activity in public
with possible negative results if negligence increases including speeding, impaired driving
and violation involvement. According to Rhodes and Pivik, (2011), motorists on the road

consider dual process of decision making consisting rational and intuitive processes. These
rational and intuitive processes can be represented with risk perceptions and positive affect
respectively. In theory, accident risk perception is described as overall perceived risks of an
accident involvement while driving/riding and specific driver behaviours or driving situations.
Thus, risk perception may refer to the perceived likelihood of an event such as an accident
while driving or the chance that the incident occurred in negative situations, for instance a
fatal accident. Meanwhile, positive affect is defined as a person who accounted for loving of
risky driving/riding behaviour such as the enjoyment of speeding (Jonah, 1986; Rhodes and
Pivik, 2011).
The purpose of this study therefore, is to examine on how positive affect and risk
perception contribute to motorcyclists’ risky behaviours. In so doing, hypothesised models are
initially required to test the relationship among the measures as follows:
H1 : risk perception is the awareness of motorcyclists on these 12 behaviour measures.
H2 : positive affect is the enjoyment of motorcyclists on these 12 behaviour measures.
H3 : risk perception (RP) and positive affect (PA) is expected to influence risky
behaviours (RB) of motorcyclists.
H4 : risky behaviour is the occurrence of motorcyclists involved in such 12 behaviour
measures.
In this study, risky behaviour, positive affect and perceived risk of the motorcyclists are
measured with twelve items as shown in Table 1.These measures refer to the study results

originally conducted in the UK and the US. Some measures were partly obtained by
categorising and contextualising risk taking behaviour of car drivers in the UK through
studying their motivations and attitudes towards risk ((Musselwhite, 2006). In the meantime,
the other parts were resulted from emerging themes from preliminary focus group data
conducted in the US (Rhodes and Pivik, 2011).
These measures however, are generally regarded as relevant to represent the existing
motorcyclists’ behaviours in Bali, despite some riding characteristics in Bali have been
observed differently to those in the UK and the US, for instance motorcyclists are commonly
speeding and manoeuvring among stationary or moving vehicles to get ahead of the congested
traffic (Wedagama and Dissanayake, 2010b). Further studies therefore, are required to
identify more on these riding characteristics in Bali.
Table 1. Twelve behaviour measures
No

Behaviour Measures

Risky
Risk
Positive
Behaviour Perception Affect

1. Changing lanes to overtake other vehicles
Y1
X1
Z1
2. Speeding up and braking
Y2
X2
Z2
3. Exceeding speed limit even when feeling unsafe
Y3
X3
Z3
4. Riding fast on curves
Y4
X4
Z4
5. Riding while sleepy
Y5
X5
Z5

6. Drink and ride
Y6
X6
Z6
7. Passengers doing things that can distract you while riding
Y7
X7
Z7
8. Run a red light
Y8
X8
Z8
9. Doing thing that can distract you while riding
Y9
X9
Z9
10. Braking hard to stop in time
Y10
X10
Z10

11. Peak hour riding
Y11
X11
Z11
12. Racing with other vehicles
Y12
X12
Z12
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Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015

Meanwhile, Structural Equation Model (SEM) has been widely used to give numerical
estimates which suggesting the strength of the relationships among the model parameters. In
this study therefore, SEM is used to test the conceptual models above and to analyse the
measures variables which are good indicators of the latent variables.
2.2. Structural Equation Model (SEM)
Structural Equation Modelling (SEM) is a statistical tool used to resolve simultaneous
multilevel models that can not be solved by linear regression. In addition, SEM can be
considered as a combination among multiple regression, path and factor analyses. SEM can be

used to construct the model equations with a large number of endogenous and exogenous
variables and latent (unobserved) variables. These variables are classified as linear
combinations of the observed variables. Hypotheses regarding causal relationships among
observed and unobserved variables are tested with each direct effect corresponds to an arrow
in a path diagram. The analysis is based on matrix covariance which providing more accurate
than the linear regression analysis (Hair, et.al, 2010). This modelling technique is able to
examine simultaneous dependence relationships so it helps to address such complicated
human behavioural issues.
An assessment whether the developed model fits the data is also an important stage in
SEM. There have been many criteria however, for assessing the goodness of fit of the
constructed models (Hooper, et.al, 2008; Schermelleh-Engel and Moosbrugger, 2003). The
indices for model assessment do not count on comparison with a baseline model. Instead, a
measure of goodness of fits of the model is compared to no model at all (Jöreskog and
Sörbom, 1993, in Hooper, et.al, 2008). These include the Chi-Squared test, Root Mean Square
Error of Approximation (RMSEA), Goodness-of-Fit Index (GFI), Adjusted Goodness-of-Fit
Index (AGFI), Root Mean Square Residual (RMR) and Standarised Root Mean Square
Residual (SRMR) (Hooper, et.al, 2008).
The Chi-Square value has long been used to measure overall model fit. This value
shows the difference between the sample and fitted covariance matrices. Due to its limitation,
the relative chi square which is the ratio between  2 and df (degree of freedom) has been

used to assess model fit. A rule of thumb for this index is ranged from as high as 5.0 to as low
as 2.0 (Wheaton et al, 1977 and Tabachnick and Fidell, 2007 in Hooper, et.al, 2008). An
SRMR will be low however, as model consisting high number of parameters and using large
sample sizes. Both AGFI and GFI are also likely to increase with sample size. Consequently,
they are not used as an individual index considering the effect of sample size on these indices
(Hooper, et.al, 2008).
Meanwhile, there are also some concerns with regard to the use of Normed Fit Index
(NFI), Non-Normed Fit Index (NNFI) and Comparative Fit Index (CFI). In a correct model
with smaller samples, NFI may not arrive at 1.0 while the value of NNFI sometimes may be
outside of the range between 0 and 1. Table 2 summarises criteria from Schermelleh-Engel
and Moosbrugger (2003) to assess goodness-of-fit for overall Structural Equation Models.

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Table 2. Structural equation model evaluations: some rules of thumb
Fit Measures
Root Mean Square Error of Approximation (RMSEA)
Standarised Root Mean Square Residual (SRMR)
Normed Fit Index (NFI)
Non-Normed Fit Index (NNFI)
Comparative Fit Index (CFI)
Goodness-of-Fit Index (GFI)
Adjusted Goodness-of-Fit Index (AGFI)

Good Fit
0 0.5)
0.711
0.695
0.805
0.745
0.778
0.547
0.683
0.755
0.667
0.543
0.726
0.503
0.730
0.720
0.817
0.547
0.673
0.563
0.601
0.727
0.732
0.697
0.713
0.537
0.570
0.607

4. MODEL DEVELOPMENT AND ANALYSIS
A statistical modelling software LISREL (Linear Structural Relationships) version 8.80 is
used to provide simultaneous model estimations and a causal relationship analysis. As risk
perception and positive affect are two measures of impact factors for risky behaviour of
motorcyclists, the correlations may exists between the three measures. Based on the
hypothesised models and factor analysis, the interactions among the three dimensions are
constructed as shown in Figure 4. Risk perception is grouped into two factors consisting Risk
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Perceptions (RPs) 1 and 2. These factors have 6 (X2-X6, X12) and 5 (X7-X11) observed
variables respectively while Positive Affect (PA) and Risky Behaviours (RB) contain 9 (Z1-Z6,
Z9-Z10, Z12) and 6 (Y2-Y4, Y6, Y10, Y12) observed variables respectively. Thus, RP and PA are
expected independently to influence risky riding behaviours.

Figure 4. The developed model
In this study, the combined model validity criteria of Schermelleh-Engel and
Moosbrugger (2003) and Lai (2011) is used to assess the goodness of fit of the developed
model as shown in Table 5. As the result, the NFI, NNFI, CFI, GFI and AGFI values are less
than the cut off point shown in Table 2. However, based on  2 /df, RMSEA, SRMR and PNFI
values, the developed model is statistically acceptable.
Table 5. Goodness of fit of the developed structural equation model

Fit
pdf
2
Measures
value
Developed
1384.45 293
0
Model

 2 /df RMSEA SRMR NFI NNFI CFI GFI AGFI PNFI
4.72

0.1

0.07

0.85

0.87

0.88 0.75

0.71

0.77

Table 6 shows that the path estimates are considerable with a significant difference
 0.001 (excluding the structural path between Y2 and risky behaviours). This indicated that
all hypotheses assumptions (H1-H4) for the developed model have been statistically accepted.
More specifically, the structural equation model explains that risky behaviours of
motorcyclists are influenced by risk perceptions and positive affect (hypothesis 3).

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Table 6. Standardised coefficients list
Path
Estimate
Risky Behaviour  Risk Perception 1
-0.35
Risky Behaviour  Risk Perception 2
0.37
Risky Behaviour  Positive Affect
0.71
Y2 (Speeding up and braking)  Risky Behaviour
0.55
Y3 (Exceeding speed limit even when feeling unsafe)  Risky Behaviour
0.66
Y4 (Riding fast on curves)  Risky Behaviour
0.75
Y6 (Drink and ride)  Risky Behaviour
0.71
Y10 (Braking hard to stop in time)  Risky Behaviour
0.35
Y12 (Racing with other vehicles)  Risky Behaviour
0.69
X2 (Speeding up and braking)  Risk Perception 1
0.65
X3 (Exceeding speed limit even when feeling unsafe)  Risk Perception 1
0.60
X4 (Riding fast on curves)  Risk Perception 1
0.77
X5 (Riding while sleepy)  Risk Perception 1
0.73
X6 (Drink and ride)  Risk Perception 1
0.75
X12 (Racing with other vehicles)  Risk Perception 1
0.64
X7 (Passengers doing things that can distract you while riding)  Risk
0.67
Perception 2
X8 (Run a red light)  Risk Perception 2
0.66
X9 (Doing thing that can distract you while riding)  Risk Perception 2
0.79
X10 (Braking hard to stop in time)  Risk Perception 2
0.72
X11 (Peak hour riding)  Risk Perception 2
0.37
Z1 (Changing lanes to overtake other vehicles) Positive Affect
0.52
Z2 (Speeding up and braking)  Positive Affect
0.67
Z3 (Exceeding speed limit even when feeling unsafe)  Positive Affect
0.64
Z4 (Riding fast on curves)  Positive Affect
0.78
Z5 (Riding while sleepy)  Positive Affect
0.65
Z6 (Drink and ride)  Positive Affect
0.76
Z9 (Doing thing that can distract you while riding)  Positive Affect
0.62
Z10 (Braking hard to stop in time)  Positive Affect
0.62
Z12 (Racing with other vehicles)  Positive Affect
0.74

t-value
-3.51
3.68
7.70
N/A
8.54
9.21
8.96
5.42
8.80
12.57
11.34
15.80
14.60
15.09
12.11
12.73
12.52
15.72
13.86
6.40
9.62
13.08
12.46
16.32
12.69
15.62
11.96
11.85
15.00

Note: N/A = not available

The positive affect (loading factor = 0.71) is found to influence more than all risk
perception measures on risky behaviours. Accordingly, this specifies that motorcyclists in
Denpasar are more relied on intuitive than rational processes while on the road. This is
consistent with a past study findings which concluded that positive affect, which is
characterised by positive emotions in driving pleasures, is a strong predictor for risky
behaviour for teen and male drivers than for adult and female drivers (Rhodes and Pivik
(2011). In addition, a previous study by Budiartha R.M (2014) found that positive affect is
significantly related to teens’ risky driving/riding behaviours in Denpasar. As with both
previous studies, this study therefore, suggested that further studies should focus on the role
of positive affect of motorcyclists on risky behaviours. These further studies are expected to
find some more explanations to reduce risky riding behaviours.
Meanwhile, a negative causal relationship (estimated value = - 0.35) is found between a
group of risk perception measures and risky riding behaviours. Such a group of measures
consist of speeding up and braking, exceeding speed limit even when feeling unsafe, riding
fast on curves, riding while sleepy, drink and ride and racing with other vehicles. In other
words, risky riding behaviours have been inversely related to these 6 risk perception measures.
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Statistically, the strongest predictor of these 6 measures for negative influences on risky
behaviours is riding fast on curves with a loading factor of 0.77.
In contrast, there are positive causal relationships (estimated value = 0.37) between the
other 5 risk perception measures and risky riding behaviours. These measures consist of
passengers doing things that can distract a motorcyclist while riding, run a red light, a
motorcyclists does something that can distract him/herself while riding, braking hard to stop
in time, and peak hours riding. This result may be described as motorcyclists perceive and
behave consistently on such measures while riding on the road. With a loading factor of 0.79,
a motorcyclist doing something that can distract him/herself while riding (e.g. using mobile
phone) is statistically considered as the most risky behaviours. In fact, more than 40% of the
respondents perceived such an action as extremely risky and risky behaviours while
consistently have rarely and never been doing so while riding motorcycles.
In the meantime, the positive affect or riding experiences enjoyed by motorcyclists is
significantly found connected with risky riding behaviours (estimated value = 0.71). This
shows that positive affect influences more than risk perceptions on risky behaviours. These
positive affect measures consist of changing lanes to overtake other vehicles, speeding up and
braking, exceeding speed limit even when feeling unsafe, riding fast on curves, riding while
sleepy, drink and ride, doing thing that can distract motorcyclists while riding, and braking
hard to stop in time. The strongest predictor of enjoyment for risky riding behaviours is riding
fast on curves with a loading factor of 0.78. Interestingly, based on the survey results, about
5% the respondents perceive riding fast on curves as not risky at all. In addition, less than
20% of the respondents enjoy it while more than 30% frequently (always and often) do so.
Being a significant indicator for risky riding behaviour, riding fast on curves can be
described as negatively perceived and positively enjoyed by motorcyclists. In fact,
motorcyclists can be seriously injured if they fail to negotiate curves because of excessive
speed. This indicated that riding fast on curves may considerably be accounted for both
rational and intuitive processes of motorcyclists’ decision making while riding on the road.
In this study, the models are also subsequently developed based on age consisting
younger (under 26 years old) and mature (above and equal 26 years old) motorcyclists and
gender (female and male motorcyclists). The model results however, are not statistically
significant. This is possibly due to the insufficient sample size for each of age division and
gender models. Further studies therefore, are expected to consider more sample size
particularly in modelling motorcyclists’ behaviour based on age division and gender.
5. CONCLUSIONS
This study found that risk perceptions and positive affect measures have the influence on
risky riding behaviours. Risk perception measures have both positive and negative influences
on risky riding behaviours. Further, positive affect influences more than risk perception on
risky behaviours. More specifically, motorcyclists have been found to consistently perceive
and behave on such a risky behaviour that may distract them on the road, for instance using a
mobile phone while riding a motorcycle. In addition, riding fast on curves may considerably
be accounted for both rational and intuitive processes of motorcyclists’ decision making while
riding on the road.
Another challenge therefore, is to change motorist attitudes to speed. The investigation
can be conducted initially with the description of speed management policy and local
transport system. In addition, further study should focus more on these positive affect issues
especially in relation to age and gender of motorcyclists. More efforts including policies,
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traffic regulations, education and campaigns are required to control the positive affect of
motorists, in particular motorcyclists, to minimise motorcycle accidents.
REFERENCES
Ambak, K., Ismail, R., Abdullah, R.A. and Borhan, M.N, (2011) Using Structural
Equation Modelling and the Behavioral Sciences Theories in Predicting Helmet Use,
Paper presented at the International Conference on Advanced Science, Engineering
and Information Technology, Putrajaya, Malaysia, January, 14-15.
Bali Regional Police. 2014 Accident Data Report.
Budiartha R.M, N. (2014) Factors Influencing Car Drivers and Motorcyclists’ Risky
Behaviours in Bali, Journal of Society for Transportation and Traffic Studies, 5 (2),
12-22.
Hair, J.F., Black, W.C., Babin, B.J, and Anderson, R.E. (2010) Multivariate Data
Analysis, 7th Edition. Pearson Prentice Hall, New York.
Hongsranagon, P., Khompratya, T., Hongpukdee, S., Havanond, P, and Deelertyuenyong,
N. (2011) Traffic Risk Behavior and Perceptions of Thai Motorcyclists: A Case Study.
Journal of International Association of Traffic and Safety Sciences (IATSS) Research,
35, 30–33.
Hooper, D., Coughlan, J. and Mullen, M. (2008) Structural Equation Modelling:
Guidelines for Determining Model Fit. Electronic Journal of Business Research
Methods, 6(1), 53-60.
Indriastuti, A.K. and Sulistio, H. (2010) Influencing Factors on Motorcycle Accident in
Urban Area of Malang, Indonesia. International Journal of Academic Research, 2 (5),
252–255.
Joewono, T.B., Vandebona, U. and Susilo, Y.O. (2015) Behavioural Causes and
Categories of Traffic Violations by Motorcyclists in Indonesian Urban Roads. Journal
of Transport Safety and Security, 7(2), 174-197.
Jonah, B.A. (1986) Accident Risk and Risk-Taking Behaviour among Young Drivers.
Accident Analysis and Prevention, 18(4), 255-271.
Lai, S.F. (2011) The Accident Risk Measuring Model for Urban Arterials, Paper
Presented at the 3rd International Conference on Road Safety and Simulation,
Indianapolis, USA, September 14-16.
Matthews, M.L. and Moran, A.R. (1986) Age Differences in Male Drivers’ Perception of
Accident Risk: The Role of Perceived Driving Ability. Accident Analysis and
Prevention, 18(4), 299-313.
Musselwhite, C. (2006) Attitudes towards Vehicle Driving Behaviour: Categorising and
Contextualising Risk. Accident Analysis and Prevention, 38, 324-334.
Ramli, R., Oxley, J., Noor, F.M., Abdullah, N.K., Mahmood, M.S., Tajuddin, A.K. and
McClure, R. (2014) Fatal Injuries among Motorcyclists in Klang Valley, Malaysia.
Journal of Forensic and Legal Medicine, 26, 39-45.
Rhodes, N. and Pivik, K. (2011) Age and Gender Differences in Risky Driving: The
Roles of Positive Affect and Risk Perception. Accident Analysis and Prevention,
43(3), 923-931.
Sabey, B.E. and Taylor, H. (1980) The Known Risks We Run: The Highway. Transport
and Road Research Laboratory Supplementary Report 567. The United Kingdom.
Schermelleh-Engel., K. and Moosbrugger, H. (2003). Evaluating The Fit of Structural
Equation Models: Tests of Significance and Descriptive Goodness of-Fit Measures.
Methods of Psychological Research Online, (8)2, 23-74.
2026

Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015

Statistics of Bali Province. (2014) Bali in Figures.
Wedagama, D.M.P. (2009a) Predicting The Influence of Accident Related Factors on
Motorcycle Fatal Accidents Using Logistic Regression (Case Study: Tabanan, Bali).
Jurnal Teknik Sipil - Institut Teknologi Bandung, 16(1), 29-37.
Wedagama, D.M.P. (2009b) The Influence of Young and Male Motorists Accident
Factors on Motorcycle Injuries in Bali. Journal of International Association of Traffic
and Safety Sciences (IATSS) Research, 33(2), 64-75.
Wedagama, D.M.P. and Dissanayake, D. (2010a) Analysing Motorcycle Injuries on
Arterial Roads in Bali using a Multinomial Logit Model. Journal of the Eastern Asia
Society for Transportation Studies, 8,1892 -1904.
Wedagama, D.M.P. and Dissanayake, D. (2010b) The Influence of Accident Related
Factors on Road Fatalities Considering Bali Province in Indonesia as a Case Study.
Journal of the Eastern Asia Society for Transportation Studies, 8, 1905 -1917.

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