Injury Severity Analysis of Accidents Involving Young Motorcycle Riders in Malaysia

Proceedings of the Eastern Asia Society for Transportation Studies, Vol.8, 2011

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Injury Severity Analysis of Accidents Involving Young Motorcycle Riders

in Malaysia

Mohd Hafzi MD ISA Research Officer Vehicle Safety and Biomechanics Centre Malaysian Institute of Road Safety Research 43000 Kajang, Selangor Malaysia Fax: +60387368685 E-mail: [email protected]

Zulhaidi MOHD JAWI Research Officer Vehicle Safety and Biomechanics Centre Malaysian Institute of Road Safety Research 43000 Kajang, Selangor Malaysia Fax: +60387368685 E-mail: [email protected]

Rohayu SARANI Research Officer Road Engineering and Environment Centre Malaysian Institute of Road Safety Research 43000 Kajang, Selangor Malaysia Fax: +60387342160 E-mail: [email protected]

Shaw Voon WONG Director Vehicle Safety and Biomechanics Centre Malaysian Institute of Road Safety Research 43000 Kajang, Selangor Malaysia Fax: +60387368685 E-mail: [email protected]

Abstract: Motorcyclists are the highest contributors to road fatality statistics in Malaysia,

particularly young riders. This warrants a study to be carried out to understand the overall

accident characteristics and investigate the associated risk factors. Secondary data from police

records (POL 27) were utilized using three years of accident records (2006 – 2008). Results

shown that the following factors led to a higher probability of being killed or severely injured

(KSI) for young motorcycle riders; being a male motorcyclist, riding with a learner

probationary license or without a license, riding with an improper helmet or without wearing

any, being involved in a crash that happens during the wee hours, being involved in a crash

that happens during dawn or dusk or in the dark with and without street lighting, being

involved in a crash that happens at built-up or rural areas and being involved in a head-on

collision.

Key Words: young motorcycle riders, logistic regression, injury severity

1. INTRODUCTION

Road transportation mishaps are responsible for the death of more than a million of the

world’s population each year and also leave other 50 million surviving victims with injuries

and incapacities (WHO, 2009). It is also learned that 90% of the said fatalities occur in low-

income and middle-income countries. Figure 1 describes the trend of reported deaths by three

road user groups – (1) Vulnerable Road Users (VRUs), (2) motorized four-wheeled vehicle

occupants and (3) others – according to WHO region and income level classifications which

are Low (LIC), Middle (MIC) and High Income Country (HIC).

In overall, reported fatalities involving VRUs is more pronounced in low and middle income

countries. One of the factors associated with this finding is that the Southeast Asia region has

a considerable number of motorcycle users. For instance, registered motorcycles constitute

more than 50% of the total registered vehicles (2007) in Malaysia, Indonesia, Vietnam,

Thailand and Philippines, with the highest in Vietnam at 95% (WHO, 2009). This fact can be


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linked to high accident rate of motorcycles and consequently a significant number of related

casualties (e.g. Wedagama et al., 2009; Iamtrakul et al., 2003; Sigua, 2009). Another notable

finding is that, the situation pertaining VRU fatalities in the Southeast Asia region is way

greater in the MIC group as compared to its LIC counterpart due to differences in socio-

economic situation as compared to the situation in middle income countries (more vehicles,

traffic etc.).

Figure 1 Reported deaths by type of road user (%), by WHO region and income group

(Source: WHO, 2009)

In Malaysia, the population of motorcycle represents about half of the total registered vehicles

in the country (RMP, 2009). Its economical features especially in terms of ownership costs as

well as its ease-of-use are apparently the main attractions a motorcycle has to offer. In the

motorized vehicle segment in Malaysia, a motorcycle is in average priced five-folds lower

than the cheapest car in the market (for a new unit). It is certainly not a fuel guzzler, entitled

for nominal road tax, excluded from being tolled in most highways, easy to maneuver and

enables its user to change lanes and save time during traffic congestions, and many other

advantages that make motorcycles a very convenient and attractive mode of transportation.

In contrast, motorcycle casualties have long been a significant concern in the country, whereby

the motorcycle users represent more than half of the total traffic fatalities each year e.g. 4067

out of 6745 fatalities in 2009 alone (RMP, 2009). Furthermore, from the breakdown of

fatalities by type of road user as compiled by the Malaysian Institute of Road Safety Research

(MIROS), motorcycle users recorded an increase of approximately 4% in fatalities in 2009 as

compared to 2008. It also recorded almost 19% increment as compared to the counts in 2002

(Figure 2).

Being classified as Vulnerable Road Users (VRU) together with pedestrians and cyclists,

motorcyclists often suffer more severe injuries than occupants of bigger vehicles due to

minimal protection to their exposed body. The death profile suggests that the injury outcome

for motorcyclist is rather high, and an in-depth study of 186 fatally injured motorcyclists by

Pang et al. in 2000 had revealed that 71.5% of them were pronounced dead at the scene and a


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further 25.3% died within three hours after the accident.

Road User

% change over

2002 % 2008 % 2009 % Previous

year 2002

Pedestrian 650 11.03% 598 9.16% 589 8.73% -1.51 -9.83

Motorcycle 3429 58.21% 3898 59.72% 4067 60.30% 4.34 18.61

Bicycle 261 4.43% 203 3.11% 224 3.32% 10.34 -14.18

Car 1023 17.37% 1335 20.45% 1405 20.83% 5.24 37.34

Van 156 2.65% 96 1.47% 91 1.35% -5.21 -41.67

Bus 45 0.76% 48 0.75% 31 0.46% -35.42 -31.11

Lorry 197 3.34% 195 2.99% 213 3.16% 9.23 8.12

4Wheel 74 1.26% 106 1.62% 78 1.16% -26.42 5.41

Other 56 0.95% 48 0.74% 47 0.70% -2.08 -16.07

Total 5891 100% 6527 100% 6745 100% 3.34 14.50

Figure 2: Fatalities by mode of transport (source: MIROS)

The number of casualties involving young motorcycle riders in the country is a significant

concern. In a survey by Nor Afiah et al. (2005), the prevalence of injuries among adolescents

(19 years) was 18.8% and majority of them sustained bruises and ligament sprain.

Furthermore, according to police reports, 60% of the 6300 deaths from traffic injuries in 2007

involved motorcycle riders and of these motorcyclist deaths, 50% were young riders aged

between 16 to 30 years old. The proportion of fatalities among motorcycle riders aged

between 16 to 30 years from all rider fatalities increased from 40% in 2001 to 60% in 2007

(Figure 3).

Figure 3 Motorcycle rider fatality trend by age distribution (source: MIROS)

The number of fatalities involving this age group had cost the country approximately RM 1.9

billion after taking into account the value of statistical life (VOSL) of RM 1.2 million per

fatality, as reported by Nor Ghani and Mohd Faudzi (2003). This is a great lost in the

productivity within healthy years among those in the young age group and needs further

attention. Thus, this warrants a study to be carried out to understand the overall accident

characteristics and to determine the associated risk factors for young motorcyclists aged

between 16 and 30 years. The findings can be used in future intervention programs and may

influence current legislation particularly on rider training and licensing procedure.


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2. LITERATURE REVIEW

2.1 Previous Studies

It has been highlighted in numerous literatures whereby young motorcyclists face higher risk

of being involved in traffic accidents. In a recent review study on the contribution of age and

experience to young driver crashes (Mccartt et al., 2009), the authors found that young drivers

had higher crash rates than older drivers even after taking account the effects of length of

licensure. Furthermore, it has been well documented that young motorcycle riders are

statistically over-represented in road crash injuries and mortalities in most motorized

countries.

In the US, it has been reported that 346 motorcycle riders aged between 15 to 20 years old

were killed and 8000 were injured in 2008 (NHTSA, 2008). In Australia, it was estimated that

the fatality statistics for riders aged between 17 to 25 years old is 47.0 fatalities per 100

million kilometers ridden in comparison with riders aged between 26 to 39 years (14.5

fatalities per 100 million kilometers ridden) and riders aged 40 years and over (7.7 fatalities

per 100 million kilometers ridden), as reported by the Australian Transport Bureau (ATSB,

2002).

This problem is not only prevalent in the western countries but also in Asian countries. The

number of casualties involving motorcycle riders aged between 16 to 25 years constitutes

about 31% of the overall motorcycle fatalities in Taiwan in 1992 (Lin et al., 2003). In Khon

Kaen, Thailand, riders aged between 16 to 25 years had the tendency to encounter accidents

than other age group of riders (Iamrtrakul et al., 2003). Furthermore, it has been shown that

motorcycle casualty is significantly higher among the younger group in comparison with other

road accident victims in Singapore (Leong et al., 2009). The study also shows that young

motorcycle riders have a higher injury risk than riders who are older.

The high accident risk among young motorcycle riders could be attributed to a number of

factors. Early studies suggest that inexperience may account for the high accident rate among

young motorcycle riders (Mccartt et al., 2009). This lack of riding experience is not the only

explanation for road accidents. According to Pileggi et al. (2006), the major reason for

accidents involving adolescents is their risky and unsafe driving behaviors rather than driving

experience or technical failures. Steg and Brussel (2009) identified that young moped riders

were more likely to speed and had a stronger tendency to travel over the speed limit. Speeding

increases both the risk of being involved in a crash and suffering more severe injuries in the

event of a crash.

Studies on the contribution of risk factors on injury severity among young motorcycle riders

using accident data have been scarcely explored. In a previous study in Sweden (Zambon and

Hasselberg, 2006), accident records of motorcycle riders aged between 16 to 30 years who

sustained at least injuries were extracted from the Swedish National Road Administration

Accident Database. Univariate analysis shows that alcohol consumption, riding in rural areas

and travelling at the speed of over 50 km/h, are all factors positively associated with the

likelihood of being severely injured. Alcohol consumption was the strongest determinant after

taking into consideration of other factors through stepwise logistic regression.


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In another study by Lin et al. (2003), self-administered questionnaires were used to collect

information on crash characteristics. The subjects for the survey were junior students from

two residential areas in Taiwan (Taipei and Hualien) who have been involved in motorcycle

crashes. The results of the study indicate that crashes occurring at rural areas, in the dark,

involving high riding speed, collisions with a moving car or a parked car or a stationary object

increase the severity of injury. This study used logistic regression technique to determine the

risk factors associated with injury severity.

The following subsection summarizes recent literatures and explains in detail the application

of logistic regression analysis used in this paper.

2.2 Logistic Regression

Identifying the underlying cause of road accidents is important. However, determining the

factors associated with the cause of accident is equally imperative. There are many causes of

accidents, and three influential factors are human, vehicle and the environment. However, in

discussing the three, there are specific sub-factors that come under each of these factors. For

example, under the factor of human, several aspects come into question such as which age

group has the highest number of casualties, who are at higher risk, and how their driving

experience affects their capability in vehicle handling and many other unknown factors. The

same goes for the factors of the vehicles and the environment. The need to find out which of

these factors pose the highest risk leads to the use of logistic regression.

Especially in road safety, logistic regression is a handy tool that is essential; to researchers.

There are various variables involved in a traffic collision which illustrates the nature of the

collision and its impact. From the severity of the accident (fatal, serious, slight and damage-

only accident), condition of the victims (killed, seriously injured, slightly injured), location of

the collision (straight road, curved road) or the environmental condition of the location

(weather, lighting condition), the variables recorded are mostly categorical. Due to the nature

of data, normal regression model is almost impossible. Therefore, logistic regression suits

accident data since the dependent variable usually is a categorical data.

Extensive literature search performed showed that logistic regression plays an important role

in analyzing road crashes data; as illustrated in Al-Ghamdi (2002). In his paper, Ali S. Al-

Ghamdi utilised accident data to assess the effects of risk factors to accident severity using

logistic regression. Wong and Sze (2007) make use of logistic regression in evaluating injury

risk of pedestrian casualties using comprehensive historical data in Hong Kong. The objective

is to determine the association between the probability of fatality and severe injury and all its

contributing factors. Among factors being considered is the influence of pedestrian behaviors,

traffic congestion and junction type. Wedagama et al. (2009) also used logistic regression to

determine the influence of accident factors on road fatalities in Bali, Indonesia.

The theoretical background of logistic regression model is explained as follows:

ppjj xxxxxf βββββ ++++++= ........)( 22110 (1)

Whereby xj is the value of the independent variable j, with β1j as the corresponding coefficient,

for j = 1,2,….,p, and p is the number of independent variables.

With this latent variable, the conditional probability of a positive outcome is determined by


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)(

)(

1)(

xf

xf

e

ex

+=Π (2)

The maximum likelihood method is employed to measure the associations by constructing the

likelihood function as follows:

iiii yxyxL −−Π= 1))(1()()( ππβ (3)

Whereby yi denotes the observed outcome of i, with the value of either 0 or 1 only, and i = 1,2

3,. . . , n whereby n is the number of observations. By maximizing the log likelihood

expression as

))}(1ln()1())(ln({)ln()( iiii xyxyLLl ππββ −−+∑== (4)

The best estimate of β could be obtained accordingly. The influence of each level for each

independent variable; on the dependent variable/ outcome could be revealed by the odd ratio

(OR) value whereby .βeOR =

The odds ratio shows the strength of association between a predictor and the response of

interest by comparing the odds of success for one group to another group. An odds ratio that is

greater than 1 indicates that the concerned attribute leads to a higher risk and vice versa.

Based on the analysis of odds ratio, one can then determine which factor has the highest risk

and hence, countermeasure can be proposed to either lower the risk or if possible eliminate it.

3. METHODOLOGY

3.1 Data Source

The source of the road accident data was obtained from the MIROS Road Accident Database

System (M-ROADS) which contains accident data collected by the Royal Malaysian Police

(RMP) Traffic Department. The form used by the police for the data collection is a road

accident form known as POL27. POL27 contains a total of 91 variables which comprise

information on the crash characteristics, the casualties and the vehicles involved.

3.2 Data Selection

Only accident cases which resulted in at least minor injuries to the rider were included.

Damage-only cases were not included due to a high number of such cases going unreported. In

the police accident reporting system, injury severity is divided into three categories; death,

serious injury and minor injury. The type of casualty is defined by the RMP as follows:

i. Death – any person who dies within 30 days of an accident

ii. Serious injury – any person who has injured as a result of an accident as referred to

section 320 of the Penal Code which includes any of the following:

a. Loss of sexual ability;

b. Permanent privation of the sight of either eye;

c. Permanent privation of the hearing of either ear;

d. Privation of any member of joint;

e. Destruction or permanent impairment of the powers of any member of joint;

f. Permanent disfiguration of the head or face;


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g. Fracture or dislocation of a bone;

h. Any hurt which endangers life, or which causes the sufferer to be, during the space

of twenty days, in severe bodily pain, or unable to follow his ordinary pursuits.

iii. Minor injury – any injury that does not fall under death or serious injury.

As there is no valid international definition of ‘young motorcycle rider’, the age from 16 to 30

years, which represented about 60% of all casualties, is chosen for this paper. After applying

the exclusion criteria to the initial data set including the removal of missing and unreliable

data, 7676 accident cases were used for the study.

3.3 Data Description

The dependent variable (injury severity) which was initially defined by the police as death,

serious injury and minor injury is redefined into two categories; KSI (killed or serious injury)

and minor injury. The term KSI is used instead of DSI (death or serious injury) in order to

align this study with previous studies. The independent variables were classified into four;

rider, environment, site and crash configuration. Table 1 below summarizes the dependent and

independent variables considered in this study.

Table 1: Variables selected for the study

Variable Group Variable type Classifications and coding

Dependent variable Injury severity 0: Minor injury

1: KSI (killed or serious injury)

Rider Gender 0: Female

1: Male

Race 0: Malay

1: Non-Malay

License status 0: Full license

1: Learner probationary license

2: No license

Helmet use 0: Improper use/ No helmet

1: Proper helmet use

Environment Time of day 0: Normal hours

1: Peak traffic hours

2: Night

3: Wee hours

Day of week 0: Weekday

1: Weekend

Light condition 0: Daylight

1: Dawn/ dusk

2: Dark with streetlight

3: Dark without streetlight

Weather condition 0: Fine

1: Bad

Site Location type 0: City/ Urban

1: Built-up area

2: Rural

Road geometry 0: Straight

1: Curve

2: Roundabout

3: Junction


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Crash configuration Collision type 0: Head-on collision

1: Rear collision

2: Lateral collision

3: Side swipe

4: Hitting objects/ pedestrian/ animal

5: Others

3.4 Data Analysis

Logistic regression analysis was performed in order to deal with the binary nature of the

dependent variables i.e. injury severity. Referring to logistic regression coefficient mentioned

in the literature review earlier (section 2.2), if β is the logistic regression coefficient, then βe is

the odds ratio corresponding to a one unit change in the exponential beta of said variable. In

this study, each level of independent variables has their odds ratio whereby the first level

(coded as 0) is the base of the odds (Table 1). Odds ratio greater than 1 indicates the risk of

getting killed or seriously injured is higher as compared to the base level group and vice versa.

Univariate logistic regression analysis was performed to estimate unadjusted odds ratio (OR)

and 95% confidence interval (CI). Nevertheless, the univariate analysis only gives affects for

each individual variable. Thus, in order to take into consideration the effects of other

variables, a multivariate logistic regression analysis was employed. In short, adjusted odds

ratio estimates the magnitude after controlling for other confounding factors (Hosmer &

Lemeshow, 1989). The statistical analyses were carried out using the SPSS software package

(version 17; SPSS Inc., Chicago, IL, USA).

4. RESULTS

A total record of 7676 motorcycle riders who were involved in traffic accidents was reviewed.

Of this, 3012 (39.2%) of them were either killed or seriously injured and 4664 (60.8%) were

slightly injured. The following subsections explain the independent variables and injury

severity analysis in detail.

4.1 Rider

Table 2 describes the profile of the riders according to their gender, race, license status and

also helmet use. Based on the records, males make up a higher percentage as compared to

females (87.8% male, 12.2% female). According to race classification, Malays as the majority

in the country’s population outnumber the other races by 80%. A majority (71.3%) of the

young riders possess full license (formally known as Competence Driving License – CDL),

and only 3.8% are in the learner probationary license group. However, an alarming figure of

1908 or about 25% of the young motorcycle riders do not possess a license. In overall,

approximately 6.6% of motorcyclists were found to be either wearing a helmet (but

improperly buckled or strapped) or not wearing a helmet at all.

Table 2: Rider characteristics for all casualties

Factor n %

Gender

Female 937 12.2

Male 6739 87.8


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Race

Malay 6168 80.4

Non-Malay 1508 19.6

License status

Full license 5475 71.3

Learner probationary license 293 3.8

No license 1908 24.9

Helmet use

Proper helmet use 7168 93.4

Improper use/ No helmet 508 6.6

4.2 Environment

Table 3 explains the environment profiles pertaining to the accident scene where the young

motorcyclists involved in the studied road accident cases. From a total of 7678 cases, this tally

is evenly distributed in three out of four “Time of Day” classifications; normal hours (25.9%),

peak traffic hours (33.4%) and at night (31.1%). The remaining 736 casualties (9.6%)

occurred during wee hours (between midnight and early morning). In terms of the day of the

week, approximately 70% of the cases occurred on a weekday. Furthermore, only 14.6% of

the cases occurred during adverse weather as most of the studied young motorcycle riders

were involved in accidents in clear weather. For light condition, from both ambient and street

lighting sources, 4856 or 63.3% of the studied samples were involved in accidents in daylight.

In the condition with low ambient light intensity (dawn or dusk) or completely dark (at night),

the situations were rather obvious at night with 28.8%. From the figure, 18.2% occurred with

the presence of streetlight and the remaining 10.6% were without streetlight.

Table 3: Environment factors for all casualties

Factor n %

Time of day

Normal hours 1990 25.9

Peak traffic hours 2562 33.4

Night 2388 31.1

Wee hours 736 9.6

Day of week

Weekday 5328 69.4

Weekend 2348 30.6

Weather

Fine 6555 85.4

Bad 1121 14.6

Light condition

Daylight 4856 63.3

Dawn/ dusk 612 8.0

Dark with streetlight 1395 18.2

Dark without streetlight 813 10.6

4.3 Site

The accident site profiles of the accidents involving the studied samples are further explained

in Table 4. For location type factor, it is further categorized into city or urban setting, built-up

area and rural. It is found out that approximately 62% of the cases occurred in rural areas, as

compared to those that happened in developing areas (20.4%) or developed areas (17.9%).


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Moreover, in term of road geometry, more cases involving young motorcyclists happened on

straight roads and at intersections with the proportion of 54.4% and 34.7%, respectively. The

other road geometry category (curved roads and roundabouts) only account for a total of 825

casualties or 10.7% of all accidents.

Table 4: Site factors for all casualties

Factor n %

Location type

City/ Urban 1375 17.9

Built-up area 1565 20.4

Rural 4736 61.7

Road geometry

Straight 4184 54.5

Curved 792 10.3

Roundabout 33 0.4

Junction 2667 34.7

4.4 Crash configuration

From Table 5, it is learned that 3510 or 45.7% of the casualties resulted from lateral collisions.

The cases in three other arrangements were fairly evenly distributed at 13.0%, 15.4% and

12.9%, respectively in head-on, rear and side swipe collisions. The rest of the cases that were

grouped in “others” account for 8.5% of the total cases.

Table 5: Collision type for all casualties

Factor n %

Collision type

Head-on collision 998 13.0

Rear collision 1179 15.4

Lateral collision 3510 45.7

Side swipe 985 12.8

Hitting objects/pedestrian/animal 355 4.6

Others 649 8.5

4.5 Injury Severity Analysis

Table 6 shows the unadjusted and adjusted ORs (with 95% CI) for each of the eleven

variables considered. For univariate logistic regression, almost all the variables were

significantly associated with the injury severity except for day of week and peak traffic hours.

When controlling for other confounding factors, the adjusted ORs reveal that, males as

compared to females, unlicensed riders together with novice riders (those who hold a learner

probationary license) as compared to those who have a full license are among those who have

a higher risk of being killed or severely injured. Proper helmet use helps immensely since

improper or failure to use helmet doubles the risk of being seriously injured or killed.

On the environment factors, crashes that occur during the wee hours increase the risk of a

young rider being killed or severely injured in comparison with crashes occurring during

normal hours. Light is also a significant factor; whereby analysis showed insufficient light

especially during dusk/ dawn, dark with or without streetlight poses higher risk of KSI

outcome. Compared to city areas, young riders are more prone to face severe injury or be


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killed if a crash happens in a rural or built-up area. Furthermore, the risk of getting killed or

severely injured is much higher if the rider is involved in a head-on collision as compared to

other collision types.

Table 6: Univariate and multivariate logistic regression results with ORs (95% CI)

Factor Unadjusted ORs

(95% CI)

Adjusted ORs

(95% CI)

Gender

Female (reference)

Male 1.422 (1.230, 1.645)* 1.273 (1.093, 1.481)*

Race

Malay (reference)

Non-Malay 0.861 (0.766, 0.968)* 0.885 (0.783, 1.000)

License status

Full license (reference)

Learner probationary license 1.420 (1.121, 1.799)* 1.429 (1.118, 1.826)*

No license 1.470 (1.323, 1.634)* 1.272 (1.137, 1.423)*

Helmet use

Proper helmet use (reference)

Improper use / No helmet 2.989 (2.478, 3.606)* 2.510 (2.062, 3.056)*

Time of day

Normal hours (reference)

Peak traffic hours 0.925 (0.819, 1.045) 0.917 (0.809, 1.040)

Night 1.169 (1.035, 1.321)* 0.914 (0.771, 1.083)

Wee hours 1.561 (1.316, 1.851)* 1.242 (1.009, 1.529)*

Day of week

Weekday (reference)

Weekend 0.991 (0.898, 1.095) 0.948 (0.855, 1.051)

Weather

Fine (reference)

Bad 1.165 (1.024, 1.324)* 1.135 (0.988, 1.304)

Light condition

Daylight (reference)

Dawn/ dusk 1.483 (1.252, 1.756)* 1.244 (1.017, 1.521)*

Dark with streetlight 1.288 (1.141, 1.454)* 1.259 (1.064, 1.489)*

Dark without streetlight 1.466 (1.262, 1.703)* 1.228 (1.009, 1.496)*

Location type

City/ Urban (reference)

Built-up area 1.222 (1.050, 1.424)* 1.172 (1.001, 1.371)*

Rural 1.498 (1.320, 1.700)* 1.283 (1.123, 1.466)*

Road geometry

Straight (reference)

Curved 1.292 (1.109, 1.504)* 1.079 (0.917, 1.269)

Roundabout 0.525 (0.244, 1.133) 0.681 (0.309, 1.500)

Junction 0.690 (0.623, 0.763)* 0.761 (0.680, 0.851)*

Collision type

Head-on collision (reference)

Rear collision 0.483 (0.407, 0.573)* 0.534 (0.447, 0.638)*

Lateral collision 0.408 (0.353, 0.471)* 0.495 (0.426, 0.575)*

Side swipe 0.278 (0.230, 0.335)* 0.310 (0.255, 0.375)*

Hitting object/pedestrian/animal 0.305 (0.235, 0.396)* 0.282 (0.216, 0.370)*

Others 0.830 (0.680, 1.012) 0.800 (0.651, 0.983)*

* Statistically significant at the 0.05 level.


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5. DISCUSSION

Understanding the contributing factors of motorcycle crashes involving young motorcycle

riders is a challenge. The current study was embarked upon to assess what factors play the

most important role in the risk of a young motorcycle rider being severely injured or killed in

a traffic accident. This study applies the logistic regression technique to determine the risk

factors associated with injury outcomes.

In term of license status, the proportion of young illegal riders is still a concern as it

constitutes about 25% of the total number of accidents, as shown in Table 2. Furthermore,

from the multivariate analysis, riding without a valid license increases the odds of being killed

or severely injured in comparison with riding with a full license. The risk is also greater for

riders with a learner probationary license. This highlights the necessity to improve current

licensing system and training program for novice riders.

Our findings also show that without proper helmet use or the failure to use a helmet resulted

in a high risk of being killed or severely injured. It has been widely established that helmets

provide considerable protection and are the major factor in the decrease of severity of injuries

and mortality to motorcycle occupants. In Malaysia, it is mandatory for both riders and

pillion-riders to wear helmets. However, the rate of use is still low, especially in rural areas

(Kulanthayan et al., 2001). Due to the low compliance rate among young motorcyclists in

Greece, school-based helmet promotion programs were developed and tested in high schools,

focusing on second grade students who are eligible to obtain a driving license (Germeni et al.,

2010). This approach highlights the importance of targeting new riders who are in the

transition to driving status (about to obtain a driving license) in promoting helmet use and the

same approach can be explored in Malaysia.

In terms of environmental factors, crashes at rural areas increase the risk of young motorcycle

riders being killed or severely injured. This supports the findings of previous studies (Lin et

al., 2003; Zambon and Hasselberg, 2006). Furthermore, based on the multivariate analysis,

riders who were involved in crashes occurred in the wee hours (between midnight and early

morning) faced a higher risk of being killed or severely injured. This can be partly explained

by the social behavior of young motorcycle riders in Malaysia. The social problem coined by

the locals as ‘Mat Rempit’ refers to young riders who are involved in illegal racing and

performing dangerous stunts, mostly at nights and in the small, which may lead to a greater

risk of being involved in an accident. Besides this, they are also involved in other behaviors

that are frowned upon by the public, e.g. drug use, illicit unprotected sex, fist-fights and many

other negative behaviors (Muhamad Fuad et al., 2009).

Since a substantial proportion of road crashes involving younger motorcyclists cannot be

easily avoided, an increased emphasis on reducing injury severity of a crash is needed. Factors

such as being in rural areas, during night or the wee hours and on roads without streetlight,

indicates the importance of improving road infrastructure e.g. expanding exclusive and non-

exclusive motorcycle lanes into rural areas and providing adequate lighting for roads and

improving enforcement system for reducing injury severity in motorcycle crashes.

The motorcycle lane has been considered the best practice and is highly cost efficient in

reducing motorcycle related accidents (Law and Radin Umar, 2005). However, this lane is

only available on a few roads in the country. Nevertheless, the initiative of gradually


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expanding motorcycle lane to rural areas to separate motorcycle riders from the rest of the

traffic can aid in the reduction of the overall rider injury and mortality statistics in the country.

6. CONCLUSION

The analyses in this study are limited by the variables that are readily available in POL 27.

Thus, there is limited information on more detailed vehicle damage and casualty injury as well

as the cause of injury. To improve the overall analysis, future researches should utilize data

from more in-depth accident investigations. Despite the limitations, these analyses have

provided useful information about motorcycle crashes involving young riders in Malaysia and

the contributing factors that lead to higher injury outcomes. Future interventions should take

into consideration the identified risk factors and focus more on this particular age group as

they represent more than half of the total motorcycle rider casualties in the country.

ACKNOWLEDGEMENT

The authors would like to express their gratitude to those who were involved directly or

indirectly in the course of the study. A special mention to the research assistants and other

research members for their considerable support and commitment.

REFERENCES

Al-Ghamdi, A.S. (2002) Using logistic regression to estimate the influence of accident factors

on accident severity, Accident Analysis and Prevention, Vol. 34, 729-741.

ATSB (2002) Motorcycle rider age and risk of fatal injury. Monograph 12: Motorcycle Safety

2000. Canberra: Australian Transport Safety Bureau.

Germeni, E., Lionis, C., Kalampoki, V., Davou, B., Belechri, M., and Petridou, E. (2010)

Evaluating the impact of a school-based helmet promotion program on eligible adolescent

drivers: different audiences, different needs?, Health Education Research, Vol. 25, No. 5,

865-876.

Hosmer, D.W. & Lemeshow, S., (1989) Applied Logistic Regression, Wiley, New York.

Iamtrakul, P., Tanaboriboon, Y., and Hokao, K., (2003) Analysis of Motorcycle Accidents in

Developing Countries: A Case Study of Khon Kaen, Thailand, Journal of Eastern Asia

Society for Transportation Studies, Vol. 5, 147-162.

Kulanthayan, S., Umar, R.S., Hariza, H.A., and Nasir, M.T. (2001) Modeling of Compliance

of Motorcyclist to Proper Usage of Safety Helmet in Malaysia, Journal of Crash

Prevention and Injury Control, Vol. 2, No. 3, 239-246.

Law, T.H., and Radin Umar, R.S. (2005) Determination of Comfortable Safe Width in an

Exclusive Motorcycle Lane, Journal of Eastern Asia Society for Transportation

Studies, Vol. 6, 3372-3385.

Lin, M.R., Chang, S.H., Huang, W., Hwang, H.F., and Pai L., (2003) Factors Associated with

Severity of Motorcycle Injuries Among Young Adults Riders, Annals of Emergency

Medicine, Vol. 41, No. 6, 783-791.

Leong, Q.M., Tsung Shyen, K.G., Appasamy, V., and Chiu, M.T. (2009) Young Adults and

Riding Position: Factors that Affect Mortality among Inpatient Adult Motorcycle

Casualties: A Major Trauma Center Experience, World J Surg, Vol. 33, No. 4, 870-873.


14

Mccart, A. T., Mayheww, D. R., Braitman, K. A., Ferguson, S. A., and Simpson, H. M.

(2009) Effects of Age and Experience on Young Driver Crashes: Review of Recent

Literature, Traffic Injury Prevention, Vol. 10, 209-219.

Menard, S. (2009) Logistic Regression: From Introductory to Advanced Concepts and

Applications. USA: Sage Publications, Inc.

Muhamad Fuad Abdul Karim, Rokiah Ismail, and Mohamad Fauzi Sukimi (2009) Sub-budaya

Mat Rempit dan Perubahan Sosiobudaya, Malaysian Journal of Society and Space, Vol.

3, 26-43 (In Malay).

NHTSA (2008) Traffic Safety Facts 2008. National Highway Traffic Safety Administration,

Washington D.C.

Nor Ghani, M. N., and Mohd Faudzi, M. Y. (2003) Value of Life of Malaysian Motorists:

Estimates from a Nationwide Survey, Journal of Eastern Asia Society for

Transportation Studies, Vol. 5, No. 8, 232-243.

Nur Afiah, M. Z., Hejar, A.R., Kulanthayan, S., and Law, T.H. (2005) Prevalence of

Motorcycle Injuries Among School Students in Selangor, Malaysia, Proceedings of the

Eastern Asia Society for Transportation Studies, Vol. 5, 1857-1866.

Pang, T.Y., Radin Umar, R.S., Azhar, A., Ahmad, M.M., Mohd Nasir, M.T. and Harwant, S.,

(2000) Accident characteristics of injured motorcyclists in Malaysia. Medical Journal of

Malaysia, Vol. 55, 45 – 50.

Pileggi, C., Bianco, A., Nobile, C.G.A., and Angelillo, I.F. (2006) Risky Behaviors among

Motorcycling Adolescents in Italy, The Journal of Pediatrics, Vol. 148, 527–32.

RMP (2009) Statistical Report of Road Accidents Malaysia 2009. Royal Malaysia Police,

Traffic Branch, Bukit Aman, Kuala Lumpur.

Sigua, R.G. (2009) Motorcycle Involvement in Road Crashes in Metro Manila, Journal of

Eastern Asia Society for Transportation Studies, Vol. 7.

Sze N.N and Wong S. C (2007) Diagnostic analysis of the Logistic Model for Pedestrian

Injury Severity in Traffic Crashes, Accident Analysis and Prevention, Vol. 39, 1267-

1278.

Wedagama, D.M.P., and Dissanayake, D. (2009) The Influence of Accident Related Factors

on Road Fatalities Considering Bali Province in Indonesia as a Case Study, Journal of

Eastern Asia Society for Transportation Studies, Vol. 8.

WHO (2009) Global Status Report on Road Safety: Time for Action. World Health

Organization, Geneva.

Zambon, F., and Hasselberg, M. (2006) Factors Affecting the Severity of Injuries Among

Young Motorcyclists – A Swedish Nationwide Cohort Study, Journal Crash Prevention

and Injury Control, Vol. 7, No. 2, 143-149.

Injury Severity Analysis of Accidents Involving Young Motorcycle Riders in Malaysia

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