Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia

IATSSR-00164; No of Pages 14 IATSS Research xxx (2017) xxx–xxx

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IATSS Research

Research article

Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia Muhammad Marizwan Abdul Manan a,⁎, András Várhelyi b, Ali Kemal Çelik c,d, Hizal Hanis Hashim a a Road Safety Engineering & Environment Research Center (REER), Malaysian Institute of Road Safety Research (MIROS), Lot 125-135, Jalan TKS 1,Taman Kajang Sentral, 43000 Kajang, Selangor Darul Ehsan, Malaysia b Traffic and Roads Unit (Taffik och väg), Department of Technology and Society (Teknik och Samhälle), Faculty of Engineering (Lunds Tekniska Högskola), Lund University, P.O. Box 118, John Ericssons väg 1, 22100 Lund, Sweden c Department of Econometrics, Faculty of Economics and Administrative Sciences, Atatürk University, Turkey d Atatürk Üniversitesi Kampüsü, 25030 Yakutiye, Erzurum, Turkey

a r t i c l e

i n f o

Article history: Received 23 August 2016 Received in revised form 3 November 2017 Accepted 9 November 2017 Available online xxxx Keywords: Motorcycle single-vehicle fatal crash Motorcycle multi vehicle crash Multinomial logit and mixed logit Road characteristics Environmental factors

a b s t r a c t This study aims to determine risk factors contributing to traffic crashes in 9,176 fatal cases involving motorcycle in Malaysia between 2010 and 2012. For this purpose, both multinomial and mixed models of motorcycle fatal crash outcome based on the number of vehicle involved are estimated. The corresponding model predicts the probability of three fatal crash outcomes: motorcycle single-vehicle fatal crash, motorcycle fatal crash involving another vehicle and motorcycle fatal crash involving two or more vehicles. Several road characteristic and environmental factors are considered including type of road in the hierarchy, location, road geometry, posted speed limit, road marking type, lighting, time of day and weather conditions during the fatal crash. The estimation results suggest that curve road sections, no road marking, smooth, rut and corrugation of road surface and wee hours, i.e. between 00.00 am to 6 am, increase the probability of motorcycle single-vehicle fatal crashes. As for the motorcycle fatal crashes involving multiple vehicles, factors such as expressway, primary and secondary roads, speed limit more than 70 km/h, roads with non-permissible marking, i.e. double lane line and daylight condition are found to cause an increase the probability of their occurrence. The estimation results also suggest that time of day (between 7 pm to 12 pm) has an increasing impact on the probability of motorcycle single-vehicle fatal crashes and motorcycle fatal crashes involving two or more vehicles. Whilst the multinomial logit model was found as more parsimonious, the mixed logit model is likely to capture the unobserved heterogeneity in fatal motorcycle crashes based on the number of vehicles involved due to the underreporting data with two random effect parameters including 70 km/h speed limit and double lane line road marking. © 2017 International Association of Traffic and Safety Sciences. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction 1.1. Background Motorcyclists are considered more vulnerable and they may suffer more severe injuries than other groups of road users due the lack of protection at the scene of a crash [1–3]. Motorcyclists, as defined by the World Health Organization (WHO), are road users of powered (i.e. motorized) two- or three-wheeled vehicles [4]. Mopeds and scooters are PTWs of ‘step-through’ design, usually with automatic transmission, generally restricted to low speed zones in urban areas, while motorcycles

⁎ Corresponding author. E-mail addresses: [email protected] (M.M. Abdul Manan), [email protected] (A. Várhelyi), [email protected] (A.K. Çelik), [email protected] (H.H. Hashim). Peer review under responsibility of International Association of Traffic and Safety Sciences.

must generally be straddled by the rider and have manual transmissions [5]. However, in Asian countries such as Taiwan, India, Malaysia and Vietnam, by far the most common PTWs are those with engines up to 150 cm3; PTWs with engines above 150 cm3 are considered large or high capacity motorcycles [6,7]. Despite the number of wheels or engine capacity assigned to these type of vehicles, they are commonly identified as ‘motorcycles’, and their riders have been categorized as vulnerable road users [4,5]. Thus, for the generalization purposes of this study, the word ‘motorcycle’ or ‘MC’ is used for these PTWs. According to the World Health Organization (WHO), almost to a quarter (24.1%) of the world's road traffic deaths occur among motorcyclists or powered-two-wheelers (PTW) [4]. Of these motorcycle fatalities, the South-East Asia region (i.e. mostly low- to middle-income countries) has the highest rate with 49.9%, compared to only up to 10.9% motorcyclist fatalities in high-income countries in the European region. Sixty five percent of the world's motorcycles are used in Asia, whereas Europe and North America account for only 16% [5]. The four countries with the

https://doi.org/10.1016/j.iatssr.2017.11.001 0386-1112/© 2017 International Association of Traffic and Safety Sciences. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

Please cite this article as: M.M. Abdul Manan, et al., Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia, IATSS Research (2017), https://doi.org/10.1016/j.iatssr.2017.11.001

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highest numbers of motorcycles per 1000 of population are Malaysia, Thailand, Cambodia and Japan [5,8]. In developing and low- to middleincome countries, such as those in the Asian region, motorcycles are used and exposed frequently as they are relatively affordable to purchase and run [4,5]. Hence, the high number of motorcycles on Asian roads is reflected in their high proportion of fatality accidents. The large variation in motorcycle fatal crashes among different regions of the world may reflect the usage and purpose and the exposure of motorcycles. For instance, in Europe and North America (i.e. the US and Canada), the share of motorcyclist fatalities is relatively low, because motorcycles are few in number and they are mostly preferred for occasional touring and leisure purposes [5]. Numerically, 36% of the motorcyclist fatalities were found to be associated with high engine capacity motorcycles (i.e. custom-built bikes, touring and super-sport bikes) in a developed country, Sweden, whereas mopeds and scooters were fortunately involved in around 4% [9]. Past research [10] indicated that in Germany motorcycle fatal crashes involving high engine motorcycles mostly occur during recreational rides, on weekends or in the summertime. Motorcycles are commonly used for commuting in the large cities of some developed countries of Europe such as Barcelona and Paris [5,11,12], and Athens [13,14] causing the majority of motorcyclists' fatal crashes occur in urban environment [11,12,14] rather than in rural areas in Europe. In contrast, fatal motorcycle crashes in the Asian region are more or less probably distributed along rural areas. Particularly, a very recent work by Abdul Manan [15], presents that 61% of the total motorcyclist fatalities occur in rural areas of Malaysia. The share of such fatalities was found as 31% (highest among location type) in Taiwan [16], 64% in Bangladesh [17] and N40% in rural areas in Vietnam [6,18]. In terms of usage and purpose, on many urban roads in Asia, the motorcycle usage are towards daily commuting purposes and most frequently-used motorcycle type is below 150 cm3 [4,5,17,19]. A brief introduction of Malaysia, Malaysia has a total landmass of 329,847 km2 separated by the South China Sea into two similarly sized regions, Peninsular Malaysia and East Malaysia (Malaysian Borneo). Its road network covers 124,656 km by which composed of 1.3% tolled expressways, 13.6% primary roads, 43.9% secondary roads, 34.8% local roads and 6.4% minor roads [20]. In terms of road traffic volume, the average share of registered motorcycles was 47.0%, while that of passenger cars were 44.8%, out of the total number of registered vehicles in Malaysia [15]. Of the various road types in Malaysia, (expressways, primary and secondary road), secondary roads are mostly used by motorcycles in both urban and rural environments, i.e. motorcycle comprised up to 25%–26% of the road traffic volume, compared to other type of road. Expressway roads have the least amount of motorcycle traffic with 2.2% of the traffic composition. However, primary roads in urban areas have the highest density of motorcycles in terms of the number of motorcycles per 10 km per day [15]. In developed countries, motorcycle single-vehicle fatal crashes (i.e. a type of road traffic crash in which only one vehicle and no other road user is involved) are more likely to be occurred [21]. In fact, motorcyclist fatalities in single-vehicle crashes account for approximately between 45% and 50% percent of all motorcyclist fatalities in the US [22–24]. In Scandinavian countries such as Denmark and Sweden, the most common type of fatal crash involving motorcyclists is also the single-vehicle crashes, i.e. up to 44% out of the total motorcyclist fatalities [25–27], whereas, the proportion of fatal motorcycle single-vehicles crashes has been increasing with an approximately 42% in Australia [28]. On the other hand, despite the increasing trend of riding motorcycle in the UK [29], fatal motorcycle single-vehicle crashes accounted for around 25% of motorcyclists killed or seriously injured [30], and those involved are more likely to be killed than those involved in crashes involving other vehicles [29]. Similarly, multivehicle motorcycle fatal crashes have also been overwhelmingly increasing compared to fatal single-vehicle crash involving motorcycle in countries such as the US [22,24], Australia [28], Greece and Spain [14].

Future studies on motorcycle fatal crashes involving single- and multi-vehicles in Malaysia appear to be beneficial and they may provide valuable evidence for solving traffic safety issues of both motorcyclists and all road users since 47% of registered vehicles are motorcycles and 59% of the victims of reported crash fatalities are motorcyclists [4,15]. In 2013, Malaysia ranked number five in the world among countries with a high percentage of motorcyclist fatalities, in fact N 50% of the total road fatalities are associated with motorcycles [4]. Besides, Malaysian motorcycles represent approximately between 20% and 35% of motorized vehicle fleet, which are over-represented in road traffic crashes (as similar to Singapore [31] and Indonesia [17]); accounting for about 47% of total road traffic crashes and 59% of road fatalities [20]. On average, more than seventy motorcyclists are killed in road traffic crashes every week, and more than ten motorcycle riders are killed every day [4,20]. Single- and multi-vehicle fatal crashes involving motorcycles in developing countries are much different compared to developed countries. For example, among the total fatalities in motorcycle singlevehicle crashes in Taiwan, only 17% are resulted in fatalities [16], whilst multi-vehicle crashes involving motorcycle are twice more likely to result in fatality compared to motorcycle single-vehicle crashes [16,18]. Subject to an in depth study from a proper crash database, motorcyclists in Asian countries such as Thailand, Laos, Vietnam, Indonesia and Malaysia are more likely to be fatal due to crashes resulted from multi-vehicle crashes [32–35], which may be due to the highly mixed traffic condition with different operational capabilities and more hazardous road environment in nature (e.g. heavy rains, dense forest along the roads, development next to road, etc.). Table 1 provides a comparison of selected middle- and high-income countries in terms of geographical regions and summarizes the number of reported road fatalities and the percentage of fatalities for riders on motorized 2- or 3-wheelers (TOTW). As Table 1 shows rider fatality percentage in Malaysia is one of the highest in Asia after Thailand and Laos and higher than all selected high-income countries. As shown in Table 2, out of all the motorcycle single-vehicle crash cases in Malaysia from 2010 to 2012, only 5% to 6% resulted in a fatality, while, out of the 54,000 to 60,000 reported cases of crashes involving a motorcycle with another vehicle; only 2% to 3% of motorcyclists were fatally injured. On the other hand, multi-vehicle crashes involving at least one motorcycle, 13.9% to 18.6% resulted in a fatality. This evidence shows that a motorcycle crash involving two of more other vehicles in Malaysia is more likely to end in a fatal outcome compared to a motorcycle single-vehicle crash and a crash involving a motorcycle with another vehicle. This scenario is contradictory to the crash outcome of motorcyclists in developed countries such as in the US [22,37], the UK [38] and in Europe [9,14,26], where crashes mostly resulting in loss of control (single-vehicle) being more likely to produce a fatality and those resulting from collisions with other vehicles (multi-vehicle) being less likely to result in fatality. Thus, we can generalize that in Malaysia, the fatal crashes involving motorcycles are different from those in the developed countries in terms of the number and percentage of single vehicle respectively multiple vehicle crashes. These facts raise the necessity to analyze motorcycle crashes considering the number of vehicles involved, by classifying them into three categories, i.e. Motorcycle single-vehicle crash, Motorcycle crash involving another vehicle, and Motorcycle crash involving two or more other vehicles. Motorcycle crashes involving another vehicle can be either a crash with a motorcycle or another type of vehicle, e.g. a passenger car, a truck, etc. Where else, motorcycle crashes involving two or more other vehicles can be either with several motorcycles or a combination of various types of vehicle. These crash configurations figure commonly in the police reports but never been properly looked into the probability of their occurrence. 1.2. Earlier research findings This sub-section reviews earlier research findings to identify the critical factors associated with motorcycle crashes to understand the

Please cite this article as: M.M. Abdul Manan, et al., Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia, IATSS Research (2017), https://doi.org/10.1016/j.iatssr.2017.11.001

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Table 1 Rider fatality profiles of selected countries. Source: WHO [36] Violence and injury prevention country profiles (in 2010). Country

Income group

Region

Population (million)

Total registered vehicles (million)

Total motorized TOTW vehicles (million)

Reported road fatalities

Riders motorized TOTW fatality (%)

Australia Greece Canada The UK The USa Spain Sweden China Iran Laos Malaysia South Korea Thailand Turkey

High High High High High High High Middle Middle Middle Middle Middle Middle Middle

Australia Europe North America Europe North America Europe Europe Asia Asia Asia Asia Asia Asia Europe

22.3 11.4 34 62 310 46 9.4 1300 74 6.2 28.5 48 69.1 73

16.1 7.9 21.4 35.1 259 31.1 5.2 207 21 1.0 20.2 19.7 28.5 15.1

0.7 1.4 0.6 1.2 8 2.7 0.4 – 8.2 0.8 9.5 1.8 17.3 2.4

1363 1451 2227 1905 33,808 2478 266 65,225 23,249 790 6872 5505 13,766 4045

16% 31% 9% 22% 13% 20% 17% 35% 23% 74% 59% 20% 74% 8%

a

The data of the US refer to 2009 crash reports.

phenomenon of motorcyclists' fatalities from a broader perspective in terms of the number of vehicle involved, i.e. single-vehicle or multi-vehicle. The emphasis is on the interaction of motorcyclists with the road infrastructure and environment. The type of area that the road network encompasses could be one of the influential characteristics of infrastructure that affects the probability of motorcycle crashes. In European countries, most crashes involving motorcycles occur in urban areas [39]. In Australia, approximately 70% of motorcycle injuries occur on local area roads [40]. In the US, the urban and suburban motorcycle crashes have been found to be 80% of all the motorcycle crashes observed [37]. On the other hand, the prevalence of death on rural roads and at intersections in Taipei is relatively higher for motorcycle drivers compared to automobile drivers [41]. In Malaysia, 59% of the motorcycle crashes occur in rural areas [42]. A serious consideration in motorcycle safety is the influence of road geometry, road markings and roadside installations, such as barriers, posts and so on. According to Elliott et al. [38], parallel longitudinal grooves in the road surface (for instance, to avoid aquaplaning), as well as inefficient marking, can also induce instability for motorcycle riders. Moreover, in wet conditions, road markings, manholes and cattle grids can become more slippery than the rest of the road surface [43]. The risk associated with road geometry, e.g. curves or straight road sections, has been underlined by some studies. Hurt et al. [37] highlighted the high frequency of right of way violations and single-vehicle crashes on bends. A high portion of motorcycle crashes that involve going out of control on a curve was also identified in Preusser et al. [44] and Clarke et al. [45]. Schneider et al. [46] conclude that the radius and length of the horizontal curve, along with the shoulder width, annual average daily traffic, and the location of the road segment, in relation to the curve, significantly influence the frequency of single-motorcycle crashes. On the other hand, studies from Malaysia have shown that the majority of motorcycle fatal crashes occur along straight road sections [42,47]. Road surface conditions such as slippery surfaces, repaired patches on the road, unevenness, road markings, longitudinal parallel grooves, cobbles, drain covers and gratings may present a hazard to motorcyclists [38].

Sudden changes in road surface friction, which may provoke instability in one-track vehicles, can be caused by patches of diesel and oil on the road, and by spillage of grease from stationary buses in some areas [38]. Moreover, motorcyclists are particularly vulnerable when it comes to bitumen, a material used frequently in modern road repair mainly to fill and patch road fissures [38]. An earlier case control study in Victoria, Haworth et al. [48] found that road surface actively contributed to 15% of crashes while other important factors were the surface grip, surface irregularities and potholes, loose materials, patch repairs and road markings. A study carried out in Singapore showed that a wet pavement surface was also a cause of at-fault motorcycle crashes at non-intersections [49]. However, despite poor road surface conditions being frequently mentioned by motorcyclists in the UK, road surface was found to contribute to only 5% of faults made by riders on built-up and non-built-up roads [50]. Motorcycle visibility is a significant concern. According to Wanvik [51] and Savolainen and Mannering [52], increased motorcyclist injury severity is associated with poor visibility due to horizontal curvature, vertical curvature, darkness. Poor sightline visibility and rider conspicuity are likely to contribute to motorcycle crashes at intersections [43,47]. Riding in darkness without street lighting is related to severe motorcyclist injury crashes [53,54]. In general, injuries resulting from after midnight night riding (00 am–7 am) have been found to be the most severe, especially in stop controlled junctions [54]. Motorcyclists are more vulnerable during nighttime at intersections and on expressways, perhaps because of increased speeds and hence stronger impacts [49]. Riding in fine weather appears to result in more severe injuries than in bad weather [20,33,54,55]. Intuitively, riding a motorcycle is heavily influenced by the weather. However, studies have shown that weather is a less influential factor in crash outcome [39,56]. An earlier research conducted in California [37] showed that weather was less influential in 98% of motorcycle crashes, compared to other prevailing factors related to type of collision, age, gender, etc. Weather made no contribution to crash causation in 92.7% of crash cases in the European countries [39]. An in-depth crash investigation of 1082 motorcycle crashes in

Table 2 Number of motorcycle crashes and percentage of fatal crashes considering the number of vehicles involved in the crash in Malaysia. Motorcycle fatal crash considering the number of vehicles involved

Motorcycle single-vehicle crash Motorcycle crash involving another (one) vehicle Motorcycle crash involving two or more other vehicles All crash case

2010

2011

2012

Crash cases

% fatal of all cases

Crash cases

% fatal of all cases

Crash cases

% fatal of all cases

22,355 60,269 4474 87,098

5.3% 2.6% 18.6% 4.1%

20,689 56,399 4188 81,276

5.5% 2.5% 18.7% 4.1%

22,493 53,512 9172 85,177

5.6% 2.7% 13.9% 4.7%

Source of crash data: Royal Malaysian Police (2010−2012), analysis done by MIROS. Note: Crash cases refers to all types of road traffic crash which resulted in fatal injury, serious injury, slight injury and/or damage to the vehicle.

Please cite this article as: M.M. Abdul Manan, et al., Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia, IATSS Research (2017), https://doi.org/10.1016/j.iatssr.2017.11.001

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Thailand between 1999 and 2000 reported that the weather factor was rarely a contributing factor [56]. In this sense, two main reasons can be revisited. Firstly, the motorcycle is not an all-weather vehicle, and it does not have crash characteristics similar to automobiles concerning the effect of weather [57]. Secondly, in many developed countries, riding is mainly a recreational activity, heavily influenced by adverse weather. Even motorcyclists, who may use the vehicle as a means of transport on a daily basis, change to other modes (e.g. car, public transport) when they expect bad weather conditions [57]. Table 3 summarizes other earlier research examining factors that may influence motorcycle crashes. As shown in Table 3, both Multinomial Logit (MNL) and Mixed Logit (MXL) models were frequently performed in the existing literature to determine significant risk factors that may influence motorcycle crashes. 1.3. Aim Since univariate analyses have some shortcomings such as the potential uncertainty and biasedness, multivariate analyses are frequently performed to consider the effect of all potential risk factors influencing road traffic safety [65,74]. When the relatively high percentage of fatal motorcycle crashes in Malaysian roads are considered, a close attention on only fatal cases may be needed to better understand potential significant risk factors rather than a standard level of injury severity analysis. In addition, underreported data are commonly found as a crucial issue on crash prediction studies. In order to overcome underreporting data, this paper uses only fatality crash cases, since fatality crash records are found to be more accurate and consistent than injury data in police records [75–77]. On the other hand, many past research works [2,11,52, 78] found strong evidence on the association between the number of vehicles involved and an increasing probability of fatal motorcycle crashes. Earlier work [78,79] has also concentrated on motorcycle

crashes based on the number of vehicles involved (MCFNV) to seek the potential risk factors. Thus, the aim of this study is to identify the road characteristics and environmental risk factors that influence fatal motorcycle crashes considering the number of vehicles involved in the crash in Malaysia, by adopting both Multinomial Logit (MNL) and Mixed Logit (MXL) models. 2. Method 2.1. Statistical approach For the purpose of this study, the dependent variable is the motorcycle fatal crash based on MCFNV. The dependent variable of this study has three discrete categorical outcomes, i.e. motorcycle single-vehicle fatal crash (MCF1V), motorcycle fatal crash involving another vehicle (MCF2V) and motorcycle fatal crash involving two or more vehicles (MCF3V). Earlier research confirms that MNL, MXL and nested logit models provide a more flexible functional approach to determine potential risk factors of road traffic crashes [80]. Following the earlier work, two most frequently used discrete response models that can predict two or more outcomes, namely MNL and MXL models, were performed in this study. The MNL model is a simple extension of the binomial logistic regression model. It is used when the dependent variable has more than two nominal or unordered categories, in which dummy coding of independent variables is quite common. A multinomial logistic regression model is a form of regression where the outcome variable (risk factordependent variable) is binary or dichotomous and the independent variables are continuous, categorical, or both. In this study, all of our data consists of categorical variables extracted from the police records. The application of multinomial logistic regression in risk analysis arises

Table 3 Selected earlier research examining factors influencing motorcycle crashes. Author(s)

Country

Method

Significant risk factors

Clabaux et al. [12] Jimenez et al. [58]

Paris, France Bogota, Colombia

Systematic approach Systematic approach

Gabauer and Li [59]

Washington, The US

Teoh and Campbell [60] Haque et al. [49]

The US Singapore

Negative binomial Zero-inflated negative binomial Poisson regression Binary logit

Excess speed, urban settlement Excess speed, urban settlement, risky overtaking maneuvers, Increased interaction Smaller radii, longer and isolated curves

Pai [61] Quddus et al. [62] Blackman and Haworth [63] Chung et al. [64] Rifaat et al. [3]

The UK Singapore Queensland, Australia Seoul, South Korea Calgary, Canada

Eustace et al. [2]

Ohio, The US

Albalate and Fernández-Villadangos [11] Shaheed and Gkritza [23]

Barcelona, Spain Iowa, The US

Shankar and Mannering [65] Jung et al. [66] Wedagama [67] Geedipally et al. [68]

Washington, The US California, The US Bali, Indonesia Texas, The US

Chimba and Sando [69]

Florida, The US

Savolainen and Mannering [52]

Indiana, The US

Pai [61] Pai, et al. [70]

The UK Taiwan

Shaheed et al. [71] Maistros et al. [72] Lan and Li [73]

Iowa, The US Ohio, The US The US

Binary logit Ordered probit Ordered probit Ordered probit Ordered logit Heterogeneous choice Partial constrained generalized Multinomial probit Ordered multinomial logit Latent class MNL MNL MNL MNL MNL MNL Multinomial probit MNL Nested logit MXL MXL Binary logit MXL MXL MNL Multinomial probit

Super sport motorcycle, increased engine displacement Excess speed, night time, intersections and expressways, wet road surface Main road, stop- and yield-controlled junctions Increased engine capacity, headlight Type of powered two-wheelers Excess speed, traffic violation behaviors, broadside crashes, night time Excess speed, loops and lollipops, right angle and left-turn-across-path crashes Excess speed, non-intersection location, major roadways, horizontal curves, graded segments Excess speed, road width Excess speed, dry road surface Excess speed, motorcycle displacement, fixed object interaction Road hierarchy, speeding Right-angle crashes Lighting conditions, urban and rural settlement, horizontal and vertical curves Speed limit, increase in number of lanes Curved areas, lighting conditions Excess speed and speed limit, head-on right-angle crashes, darkness Non-built-up roads, lighting conditions Rural settlement, non-rush hours Moped or heavier motorcycle Roadway surface, clear vision, light conditions, speed limit Speed limit, road curves, rigid roadside objects Excess speed, weekend, intersections and influential areas, innermost lanes, lighting conditions

Please cite this article as: M.M. Abdul Manan, et al., Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia, IATSS Research (2017), https://doi.org/10.1016/j.iatssr.2017.11.001

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when an analyst analyses relationships between a non-metric dependent variable and metric or dichotomous independent variables [81, 82]. Following the consensus on earlier research [71,74,83–85], an unordered probabilistic model for fatal motorcycle crashes can be defined as V in ¼ βi X in þ εin

ð1Þ

where Vin refers to a function to determine the number of vehicles involved category i for fatal crash n, Xin refers to a vector of measurable characteristics determining the number of vehicles involved category for crash n, βi refers to a vector of estimable coefficients, and εin refers to an unobservable error term or in other words the random component which represents the unobservable impacts on the outcome variables [71,86]. McFadden [86] has shown that the MNL model can be written as: P n ðiÞ ¼

EXP ½βi X in  ∑I EXP ½βi X In 

ð2Þ

where Pn(i) refers to the probability that fatal crash n will be result in the number of vehicles involved category i, and I refers to the set of possible number of vehicles involved outcomes [83]. For the MNL model, a base level is chosen for which the coefficients are set to zero. In this study, the base level was chosen as MCF2V, since its occurrence (reported cases) is the highest among all the included types and it represents the middle or ‘norm’ of fatal motorcycle crashes compared to the extreme cases (i.e. MCF1V and MCF3V). The estimated coefficients of the independent variables do not represent their effects on the dependent variable due to the non-linear feature of the MNL [74]. In that manner, relative risk ratio (RRR) represents the effect of a relevant risk factor. In this study, the RRR of a risk factor is computed relative to the base category. The relative probability of motorcycle single-vehicle fatal crashes (i = 2) to the base category (i = 1) is h i pði ¼ 2Þ ¼ EXP xβð2Þ pði ¼ 1Þ

ð3Þ

where f (βi | φ) refers to the density function of βi and φ refers to a vector of parameters that describes the density function with all other terms defined in Eq. (2) [71,83,93]. Such an estimation provides βi to account for unobserved heterogeneity of the effect of X on the probabilities of the number of vehicles involved outcome by the courtesy of specific density function [71]. The MXL model is estimated using simulated maximum likelihood approach. As Bhat [94] and Train [93] suggest, the parameter estimation will be more consistent in the maximum simulated likelihood if a higher number of Halton draws can be used. 2.2. Pseudo-elasticity In a logit model with three or more categories the coefficient and the odds ratio can yield misleading results about the actual effect of a variable on the probability of the occurrence of MCFNV category. A positive or negative coefficient on a variable in an occurrence cannot be freely interpreted as increasing or decreasing the probability of that occurrence category [95], due to the rate of change in probability is not a simple linear function of the coefficient in that occurrence category, but is also a function of its effect and the effects of all the other coefficients in all other occurrence categories, e.g. single-vehicle crash or multiple crashes. Thus, observing a positive coefficient (or odds ratio) and claiming this indicates the variable increases the probability can therefore may be biased [88]. To avoid this problem and properly explore marginal effects for binary indicator variables, the change in probability is calculated when each variable is altered. Computing their marginal effects is one way to measure the effects of independent variables [88,96]. The marginal effect of an independent variable measures the impact of change in an independent variable (e.g., xi) on the expected change in the dependent variable (e.g., pij) in a regression model, especially when the change in the independent variable is infinitely small or merely marginal [81,97]. In this study, the independent variables are coded as 0 and 1 indicator values. Therefore, the probability relative to any of the observed variables cannot be differentiated to compute a standard elasticity, but with the direct pseudo-elasticity of the probability [74,98], i.e. namely the percentage change in probability when an indicator variable is changed from 0 to 1. Pseudo-elasticity can be determined as

Similarly, the RRR for binary variables is given by h i ð2Þ RRR ¼ EXP xβi

ð4Þ

The RRR of an independent variable refers to the increase (RRR N 1) or decrease (RRR b 1) in risk of a specific motorcycle crash outcome (i.e. MCF1V or MCF3V) relative to the base category (i.e. MCF2V). This interpretation is similar to many recent researches that use specific injury severity level relative to fatal outcome (i.e. Rifaat et al. [87], Kim et al. [88], Celik and Oktay [74]. The analysis of underreported data may lead to a biased estimate when crash prediction models are used. In this case, police data were used and research validated that official police reports of road crash statistics are incomplete, inaccurate and biased [76,77,89–91]. See Abdul Manan and Várhelyi [20] on the underreporting crash data status in Malaysia. Moreover, even if there would be an underreporting fatal crash (i.e. MCF1V), many researchers argued that underreporting in the data will have a minimal impact on the model estimation results in the standard MNL model [65,92]. The MXL model is considered a successful generalization of the MNL model that allows the parameter vector βi to vary across the observations. In other words, the MXL model allows for heterogeneity within the observed data by the elements of parameter vector, while such elements and outcome specific constants can be either fixed or randomly distributed over all parameters with fixed means. Hence, the MXL model can be derived as Z P n ðijφÞ ¼

EXP ½βi X in  f ðβi jφÞdβ ∑I EXP½βi X In 

ð5Þ

5

Epxnkni ¼

P ni ½given xnk ¼ 1−P ni ½given xnk ¼ 0 P ni ½given xnk ¼ 0

ð6Þ

where the kth indicator variable for a fatal motorcyclist n, xnk is shifted. The direct pseudo-elasticity for the MNL can be briefly defined by inserting Eq. (1) into Eq. (4) Epxnkni ¼

! P β 0 xn e i −1  100 eβik P eΔðβi0 xn Þ

ð7Þ

where 1 is the set of possible outcomes, Δ(βixn) is the value with xnk set to 1 and βixn is the value with xnk to 0 [88]. Following Kim et al. [88], due to the fact that the direct pseudo-elasticity is the percentage change in probability for each observation n, we summarized it by taking the average value for all observations. The likelihood ratio test can be applied to test if the occurrence of MCFNV model is significantly different among potential risk factors. The test statistic is given by    X 2 ¼ −2 LLðβT Þ−∑G LL βg

ð8Þ

where LL(βT) is the model's log likelihood at the convergence of the model estimated on all risk factors being tested, LL(βg) is the log likelihood at the convergence of the model estimated on the subset data of MFCNV g and G is the set of all MCFNV groups. This likelihood ratio test statistic is ×2 distributed with degrees of freedom equal to the summation of coefficients estimated in the subset data models less the number of coefficients estimated in the total data model. The null hypothesis for

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Table 4 Descriptive statistics of independent variables. Road environmental elements or characteristics

Motorcycle fatal crashes based on the number of vehicles involved, (MCFNV) MCF1V

MCF2V

MCF3V

Total

1. Road Hierarchy Expressways 324 (40.8%) 363 (45.6%) 108 (13.6%) 795 (8.7%) Primary roads 1110 (29.8%) 2204 (59.2%) 411 (11.0%) 3725 (40.6%) Secondary roads 681 (31.3%) 1261 (58.1%) 230 (10.6%) 2172 (23.7%) Collector roads 930 (41.6%) 1149 (51.3%) 159 (7.10%) 2238 (24.4%) 121 (49.2%) 114 (46.3%) 11 (4.50%) 246 (2.7%) Local roadsa 2. Area type Vicinity of residential area 593 (36.4%) 894 (54.8%) 143 (8.8%) 1630 (17.8%) Vicinity of office 210 (33.4%) 341 (54.2%) 78 (12.4%) 629 (6.9%) Vicinity of shopping area 73 (30.5%) 135 (56.5%) 31 (13.0%) 239 (2.6%) Vicinity industrial/construction 109 (35.3%) 168 (54.4%) 32 (10.3%) 309 (3.4%) Vicinity of school 61 (31.3%) 117 (60.0%) 17 (8.70%) 195 (2.1%) a No development 2059 (34.3%) 3436 (55.7%) 618 (10.0%) 6174 (67.3%) 3. Location type Citya 256 (42.5%) 284 (47.1%) 63 (10.4%) 603 (6.6%) Town 492 (36.9%) 694 (52.0%) 149 (11.2%) 1335 (14.5%) Small town 435 (30.7%) 835 (59.0%) 146 (10.3%) 1416 (15.4%) Rural 1983 (34.1%) 3278 (56.3%) 561 (9.6%) 5822 (63.4%) 4. Road geometry category Straight 2265 (37.3%) 3167 (52.1%) 648 (10.6%) 6080 (66.3%) Curve 620 (45.5%) 615 (45.2%) 127 (9.3%) 1362 (14.8%) Roundabout 28 (70.0%) 12 (30.0%) 0 (0.0%) 40 (0.4%) Crossjunction 57 (11.8%) 381 (78.7%) 46 (9.5%) 484 (5.3%) T-Junction 194 (16.2%) 908 (75.7%) 97 (8.1%) 1199 (13.1%) a 2 (18.2%) 8 (72.7%) 1 (9.1%) 11 (0.1%) Interchange 5. Traffic system category 1 way traffic (single carriageway) 666 (44.0%) 724 (47.9%) 123 (8.1%) 1513 (16.5%) 2 way traffic (undivided) 2151 (32.1%) 3797 (57.7%) 674 (10.2%) 6586 (71.8%) 2 way traffic (divided)a 385 (35.7%) 570 (52.9%) 12 (11.3%) 1077 (11.7%) 6. Road shoulder type Paved 1564 (34.0%) 2572 (55.9%) 468 (10.2%) 4604 (50.2%) Unpaveda 1602 (35.0%) 2519 (55.1%) 451 (9.9%) 4572 (49.8%) 7. Speed limit category 60 km/h and belowa 1487 (36.8%) 2226 (55.1%) 324 (8.0%) 4037 (44.0%) 70 km/h 816 (32.8%) 1394 (56.1%) 277 (11.1%) 2487 (27.1%) 80 km/h 325 (35.1%) 511 (55.1%) 91 (9.80%) 927 (10.1%) 90 km/h 366 (28.5%) 761 (59.2%) 158 (12.3%) 1285 (14.0%) 110 km/h 172 (39.1%) 199 (45.2%) 69 (15.7%) 440 (4.80%) MCF1V: Motorcycle single-vehicle fatal crash, MCF2V: Motorcycle fatal crash involving another vehicle, MCF3V: Motorcycle fatal crash involving two or more vehicles. Location type: City is classified as an area that has a population N 100,000, Town is classified as 100,000 b population b 50,000, Small Town is classified as 50,000 b population b 5000, Rural is classified as population b 5000. Road environmental elements or characteristics, time of day, lighting, weather condition

8. Road marking category Double lane line (Overtaking not permissible) Single lane line (Overtaking permissible) One Way lane line markinga ⁎Divider line marking No Marking 9. Road surface quality Smooth Potholea ⁎⁎Rut ⁎⁎⁎Corrugation 10. Road surface condition Dry Flooded Wet Oily Sandya 11. Hour category 00 am–6 am 6 am–9 am 9 am–12 pm 12 pm–2 pm 2 pm–5pma 5 pm–7 pm 7 pm–12 am 12. Lighting condition Daylight Early morning/eveninga Night time with light

Motorcycle fatal crashes based on the number of vehicles involved, (MCFNV) MCF1V

MCF2V

MCF3V

Total

405 (26.5%) 1867 (33.3%) 246 (45.6%) 303 (36.1%) 345 (53.1%)

934 (61.1%) 3181 (56.6%) 254 (47.0%) 442 (52.5%) 280 (43.1%)

190 (12.4%) 568 (10.1%) 40 (7.4%) 96 (11.4%) 25 (3.80%)

1529 (16.7%) 5616 (61.2%) 540 (5.90%) 841 (9.20%) 650 (7.10%)

2981 (33.9%) 9 (22.5%) 66 (55.5%) 110 (49.3%)

4920 (55.9%) 28 (70.0%) 46 (38.7%) 97 (43.5%)

893 (10.2%) 3 (7.5%) 7 (5.8%) 16 (7.20%)

8794 (95.8%) 40 (0.40%) 119 (1.30%) 223 (2.40%)

2790 (33.9%) 4 (25.0%) 308 (38.2%) 4 (50.0%) 60 (48.4%)

4598 (55.9%) 10 (62.5%) 428 (53.0%) 3 (37.5%) 52 (41.9%)

833 (10.2%) 2 (12.5%) 71 (8.8%) 1 (12.5%) 12 (9.7%)

8221 (88.6%) 16 (0.20%) 807 (8.80%) 8 (0.10%) 124 (1.4%)

698 (49.1%) 443 (35.3%) 331 (31.7%) 201 (27.8%) 347 (28.1%) 280 (27.8%) 866 (34.7%)

595 (41.8%) 690 (54.9%) 622 (59.6%) 456 (63.2%) 767 (62.0%) 616 (61.4%) 1345 (54.0%)

129 (9.10%) 123 (9.80%) 91 (8.70%) 65 (9.00%) 122 (9.90%) 108 (10.8%) 281 (11.3%)

1422 (15.5%) 1256 (13.7%) 1044 (11.4%) 722 (7.90%) 1236 (13.5%) 1004 (10.9%) 2492 (27.2%)

1498 (30.1%) 326 (40.9%) 681 (37.7%)

2989 (60.0%) 401 (50.3%) 944 (52.2%)

491 (9.9%) 70 (8.80%) 183 (10.1%)

4978 (54.3%) 797 (8.70%) 1808 (19.7%)

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Table 4 (continued) Road environmental elements or characteristics, time of day, lighting, weather condition

Motorcycle fatal crashes based on the number of vehicles involved, (MCFNV) MCF1V

MCF2V

MCF3V

Total

Night time without light 661 (41.5%) 757 (47.5%) 175 (11.0%) 1593 (17.4%) 13. Weather condition 2907 (34.4%) 4693 (55.5%) 849 (10.1%) 8449 (92.1%) Cleara Windy 2 (33.3%) 3 (50.0%) 1 (16.7%) 6 (0.10%) Foggy 35 (33.7%) 56 (53.8%) 13 (12.5%) 104 (1.10%) Rain 222 (36.0%) 339 (54.9%) 56 (9.10%) 617 (6.70%) MCF1V: Motorcycle single-vehicle fatal crash, MCF2V: Motorcycle fatal crash involving another vehicle, MCF3V: Motorcycle fatal crash involving two or more vehicles. Divider marking refers to road markings that act as a non-physical road median, e.g. hatching, or road line marking around a physical median. A rut is a depression or groove worn into a road or path by the travel of wheels. Corrugation is a form of plastic movement typified by ripples (corrugation) or an abrupt wave (shoving) across the pavement surface. It is usually caused by traffic action (starting and stopping) and combines with excessive moisture in the subgrade. a

Indicates reference category.

Eq. (7) is that the β's in the subset models are the same. If the null hypothesis can be rejected with high confidence, the estimation of separate models for the data subsets is warranted [95,99]. Since the estimated models are conditional on crash occurrence they do not have a crash-risk interpretation. Rather, the models show which explanatory factors are associated with increasing probability of particular type of crash given that a crash occurred. This approach avoids the need to know or measure exposure [88], a significant difficulty in motorcycle traffic crash studies. It yields a disaggregate view since we can use crash-specific information to test hypotheses about the importance of various covariates (characteristics of road, road hierarchy, environment, etc.) on the type of crash [88] (e.g. motorcycle single-vehicle crash and multi-vehicle crash) and most notably identify those factors associated with increased probability of a particular crash type given in a fatal crash. Following Kim et al. [88], when interpreting coefficients for brevity it can be concluded that a coefficient increases the probability of a particular type of MCFNV. Such statements should be taken in the conditional context of a fatal crash having occurred, i.e. the coefficient on type of particular speed limit type does not predict the probability of encountering the speed limit type but the probability of occurrence a particular crash type category given a crash at or on a road that has the speed limit type.

draws were presented since the underlying model is more parsimonious than the other MXL model.

2.3. Study design, sample and data preparation

3. Results

This study utilized traffic crash data reported by the police in all 14 states of Malaysia. The corresponding data were obtained from the traffic crash report extracted from the Royal Police by the Malaysian Institute of Road Safety Research (MIROS). A total of 9176 motorcycle fatal traffic crash records were successfully drawn from the traffic crash records after filtering all the discrepancies. These discrepancies, for example overlapping case number, incorrect crash location, mismatched road hierarchy to road geometry, incomplete records, etc., had to be excluded, which is up to 5% of the total traffic crash records over the period of 2010 to 2012. For every crash record, there are ninety types of categories e.g. route number, road type, rider's age, motorcycle type, time of crash, weather condition, crash severity, road geometry, vehicle type, etc. For this study, special attention was paid to categories that are related to road environmental elements or characteristics, e.g. road hierarchy, area type, location type, speed limit category, road surface quality, etc. besides time of day, lighting and weather conditions. In this study, the MNL and the associated RRR are estimated using Stata 13. Particularly, the MXL model was fitted by a user-written program in Stata [100]. For the MXL model, it is considered that the random coefficients are normally distributed and all parameters are randomized initially. When their standard deviations are not statistically significant, they are evaluated as fixed parameters. Following the earlier literature [84,94,101] two separate MXL models were fitted using 200 and 500 Halton draws, respectively. However, only the outcome of 500 Halton

Following past studies [74,102,103], Table 5 presents the results of the chi-square test of independence before fitting MNL and MXL models. As shown in Table 5, several risk factors such as area type, road shoulder type and weather condition were not statistically significant at the 95% confidence level. Therefore, these variables were not included in the final fitted MNL and MXL models. The rest of the risk factors under consideration were statistically significant, i.e. they are strongly associated with the outcomes of the dependent variable: MCFNV. The coefficients and standard errors of the estimated MCFNV model are presented in Tables 6 and 7 are computed using the maximum likelihood method for each outcome category of MCFNV, i.e. MCF1V, MCF2V and MCF3V. The outcome of the estimated model is interpreted using the RRRs. The base category of the corresponding model is MCF2V (see justification in Section 3.1) and the coefficient estimates explain the differences compared to other outcome to the MCFNV, e.g. MCF1V and MCF3V. Interpreting the coefficients in Tables 6 and 7 can occasionally be misleading (e.g. since a positive coefficient can reduce the probability as explained in Section 3.2). Therefore, we also present Table 8, which indicates the average direct pseudo-elasticity for each variable (all are 0/1 indicator variables) in the model, which is simply the average percentage change in probability of each outcome category of MCFNV when a variable switches (from 0 to 1 or 1 to 0) for all observations.

2.4. Risk factors Table 4 shows the descriptive statistics of the crashes by the 13 risk factors covering road environmental elements and characteristics, which in turn become our independent variables for the model and the three discrete categorical outcomes of the dependent variable MCFNV, i.e. MCF1V), Motorcycle fatal crash involving another vehicle (MCF2V) and Motorcycle fatal crash involving two or more vehicles (MCF3V). These risk factors range from the type of road by hierarchy, location, road geometry, posted speed limit, road marking type, and weather condition during the fatal crash. Special note on the road surface quality and condition, the police records specified that road surface quality is the description of the road surface after it was built or maintained, since a period of the road commission. While the road surface condition is the description of the road surface during the crash, after being exposed to the weather or subject to spillage of hazardous material that might affect driving behavior, such as oil and sand. All the risk factors are defined as dummy variables to better determine the differences between each sub-category during the modeling process.

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Table 5 Independence test and measure of association for road environmental risk factors and the outcome of MCFNV. Risk factors

Χ2 (P-value)

Likelihood ratio Χ2 (P-value)

Degree of freedom

Road type Road hierarchy Area type Location type Road geometry category Traffic system category Speed limit category Road shoulder type Road marking category Road surface quality Road surface condition Hour category Lighting condition Weather condition

174.3814 (b0.001)⁎⁎ 164.1016 (b0.001)⁎⁎ 13.8681 (0.309) 36.6075 (b0.001)⁎⁎ 457.33637 (b0.001)⁎⁎ 89.8445 (b0.001)⁎⁎ 76.8941 (b0.001)⁎⁎

175.7413 (b0.001)⁎⁎ 165.2059 (b0.001)⁎⁎ 13.5864 (0.328) 36.3853 (b0.001)⁎⁎ 495.88025 (b0.001)⁎⁎ 87.4602 (b0.001)⁎⁎ 76.6099 (b0.001)⁎⁎

1.2107 (0.546) 190.9507 (b0.001)⁎⁎ 49.9146 (b0.001)⁎⁎ 20.2095 (b0.05)⁎⁎ 210.9189 (b0.001)⁎⁎ 112.3733 (b0.001)⁎⁎ 2.0171 (0.918)

1.2108 (0.546) 191.3785 (b0.001)⁎⁎ 47.7485 (b0.001)⁎⁎ 19.7884 (b0.05)⁎⁎ 206.7972 (b0.001)⁎⁎ 112.2262 (b0.001)⁎⁎ 1.9395 (0.925)

8 8 12 6 10 6 10 2 8 6 10 12 6 6

⁎⁎ N95% level of significant.

Additionally, Table 8 follows a design by Holdridge et al. [104] and Kim et al. [88], which summarizes road environmental risk factors affecting the probability of MCFNV outcome categories with respect to average direct pseudo-elasticity. The arrow represents whether the probability is increased (up arrow) or decreased (down arrow) by that variable for each outcome.

As shown in Table 6, the MNL model fits the data fairly well overall with a large chi-square statistic (Chi-square = 1125.73) and a very small P-value (0.000) for the goodness-of-fit. In particular, the significant risk factors are quite different for MCF1V and MCF3V, which imply that the choice of a multinomial logit model instead of an ordered logit is justified [87]. The results of the final MNL and MXL models also show

Table 6 The MNL model for motorcycle fatal crashes based on MCFNV. Variable

Estimated coefficienta

t-statistics

Risk ratiob

Constant [MCF1V] Constant [MCF3V] Road hierarchy: expressways [MCF1V] Road hierarchy: expressways [MCF3V] Road hierarchy: primary roads [MCF1V] Road hierarchy: secondary roads [MCF1V] Road hierarchy: collector roads [MCF1V] Location type: small town [MCF1V] Location type: rural [MCF1V] Location type: rural [MCF3V] Road geometry category: straight [MCF1V] Road geometry category: curve [MCF1V] Road geometry category: roundabout [MCF1V] Speed limit category: 110 KMJ [MCF3V] Speed limit category: 90 KMJ [MCF1V] Speed limit category: 90 KMJ [MCF3V] Speed limit category: 70 KMJ [MCF3V] Road marking category: double lane line [MCF1V] Road marking category: double lane line [MCF3V] Road marking category: no marking [MCF1V] Road surface quality: smooth [MCF1V] Road surface quality: rut [MCF1V] Road surface quality: corrugation [MCF1V] Road surface condition: flooded [MCF1V] Road surface condition: wet [MCF3V] Hour category: 00 am–6 am [MCF1V] Hour category: 6 am–9 am [MCF1V] Hour category: 9 am–12 pm [MCF1V] Hour category: 7 pm–12 pm [MCF1V] Hour category: 7 pm–12 am [MCF3V] Lighting condition: daylight [MCF1V]

−1.530 (1.360)⁎ −2.728 (1.360)⁎⁎ −0.521 (0.184)⁎⁎ 0.598 (0.362)⁎ −0.705 (0.150)⁎⁎⁎ −0.613 (0.153)⁎⁎⁎ −0.293 (0.152)⁎ −0.208 (0.114)⁎ −0.213 (0.101)⁎⁎ −0.382 (0.156)⁎⁎ 1.373 (0.820)⁎ 1.832 (0.822)⁎⁎ 2.120 (0.893)⁎⁎ 0.583 (0.211)⁎⁎⁎ −0.325 (0.079)⁎⁎⁎ 0.214 (0.111)⁎ 0.225 (0.091)⁎⁎ −0.431 (0.139)⁎⁎ 0.414 (0.234)⁎ 0.500 (0.147)⁎⁎ 0.946 (0.396)⁎⁎ 1.432 (0.444)⁎⁎ 1.297 (0.420)⁎⁎ −1.312 (0.658)⁎⁎ −0.676 (0.363)⁎ 0.850 (0.110)⁎⁎⁎ 0.303 (0.096)⁎⁎ 0.201 (0.099)⁎⁎ 0.272 (0.103)⁎⁎ 0.260 (0.153)⁎ −0.203 (0.093)⁎⁎

−2.01 −2.01 −2.84 1.65 −4.7 −4.02 −1.93 −1.83 −2.1 −2.44 1.67 2.23 2.37 2.76 −4.1 1.93 2.48 −3.09 1.77 3.41 2.39 3.23 3.08 −1.99 −1.86 7.75 3.16 2.04 2.64 1.69 −2.18

0.59 (0.41, 0.85) 1.82 (1.00, 3.30) 0.49 (0.34, 0.73) 0.54 (0.37, 0.80) 0.75 (0.58, 0.96) 0.81 (0.67, 0.98) 0.81 (0.66, 0.99) 0.68 (0.50, 0.93) 3.95 (1.02, 15.22) 6.25 (1.25, 31.29) 8.34 (1.45, 48.02) 1.79 (1.04, 3.09) 0.72 (0.59, 0.89) 1.24 (1.03, 1.49) 1.25 (1.05, 1.50) 0.65 (0.49, 0.85) 1.51 (1.03, 2.22) 1.65 (1.24, 2.20) 2.58 (1.19, 5.60) 4.19 (1.75, 10.00) 3.66 (1.60, 8.33) 0.27 (0.07, 0.98) 0.51 (0.28, 0.92) 2.34 (1.79, 3.10) 1.35 (1.12, 1.63) 1.22 (1.01, 1.48) 1.31 (1.07, 1.61) 1.30 (1.01, 1.67) 0.82 (0.68, 0.98)

Number of observations: 9176. Log-likelihood at convergence: −7920.014. Chi-square: 1125.73. Pseudo R2: 0.0664. P-value: 0.000. AIC: 15,996.03. BIC: 16,551.73. [MCF1V]: Motorcycle single-vehicle fatal crash, [MCF3V]: Motorcycle fatal crash involving two or more vehicles. ⁎ N90% level of significance. ⁎⁎ N95% level of significance. ⁎⁎⁎ N99% level of significance. a Standard errors are in parentheses. b Lower and upper limits at the 90%, 95% and 99% confidence intervals are in parentheses which is based on the respected level of significance.

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3.1. Road hierarchy factors

Table 7 The MXL model for motorcycle fatal crashes based on MCFNV. Variable

Estimated coefficient

t-statistic

Road hierarchy: expressways [MCF1V] Road hierarchy: collector roads [MCF1V] Road hierarchy: Collector roads [MCF3V] Location type: town [MCF1V] Location type: town [MCF3V] Location type: small town [MCF1V] Location type: small town [MCF3V] Location type: rural [MCF1V] Location type: rural [MCF3V] Road geometry: roundabout [MCF1V] Speed limit: 70 km/h [MCF3V] Speed limit: 90 km/h [MCF1V] Speed limit: 110 km/h [MCF3V] Road marking: double lane line [MCF1V] Road marking: single lane line [MCF1V] Road marking: single lane line [MCF3V] Road marking: no marking [MCF1V] Road marking: no marking [MCF3V] Road surface quality: rut [MCF1V] Road surface quality: corrugation [MCF1V] Hour category: 00 am–6 am [MCF1V] Hour category: 6 am–9 am [MCF1V] Hour category: 6 am–9 am [MCF3V] Hour category: 9 am–12 pm [MCF3V] Hour category: 12 pm–2 pm [MCF3V] Lighting condition: daylight [MCF1V] Lighting condition: daylight [MCF3V] Lighting condition: Night time with light [MCF1V] Lighting condition: Night time with light [MCF3V]

0.314⁎⁎⁎ 0.234⁎⁎⁎ −0.526⁎⁎⁎ −0.311⁎⁎⁎ −0.464⁎⁎⁎ −0.504⁎⁎⁎ −0.736⁎⁎⁎ −0.371⁎⁎⁎ −0.854⁎⁎⁎ 0.986⁎⁎⁎

−0.719 (1.301)⁎ −0.164⁎⁎ −0.458⁎⁎⁎ 0.478⁎⁎⁎ −1.079⁎⁎⁎ 0.660⁎⁎⁎ 0.542⁎⁎⁎ 0.740⁎⁎⁎ 0.208⁎⁎ −0.294⁎ −0.511⁎⁎⁎ −0.391⁎⁎⁎ −0.366⁎⁎⁎ −0.524⁎⁎⁎ −0.311⁎⁎⁎

2.73 3.83 −3.99 −3.18 −2.68 −5.12 −4.17 −4.39 −5.72 2.71 0.70 −4.03 2.12 −2.44 −2.17 −3.56 4.26 −3.90 3.18 3.56 7.43 2.35 −1.82 −2.76 −2.00 −4.61 −3.71 −3.26

−0.362⁎⁎⁎

−2.05

0.091 (6.383)⁎ −0.319⁎⁎⁎ 0.479⁎⁎

9

Number of observations: 9176. Log-likelihood at convergence: −8192.565. P-value: 0.0001. AIC: 16,507.13. BIC: 17,008.73. [MCF1V]: Motorcycle single-vehicle fatal crash, [MCF3V]: Motorcycle fatal crash involving two or more vehicles. () - Standard deviations are in parentheses for random effect parameters. Pseudo-elasticity and average direct pseudo-elasticities are not presented of the MXL model as the user-written command in Stata for this model does not produce such values. ⁎ N90% level of significance. ⁎⁎ N95% level of significance. ⁎⁎⁎ N99% level of significance.

consistency with the chi-square independence test in Table 8. Since the AIC value is smaller for the MNL, it is more parsimonious than the MXL, thus the analysis interpretations were based on the MNL (i.e. RRR), while the MXL model was used to identify which risk factors that are the random effect parameter, which is the possible unobserved heterogeneity within the outcome that is not measured in the data set. The main issue of shared unobservable terms is considered as an independence of irrelevant alternatives (IIA) error. The Small-Hsiao test of the IIA assumption by Small and Hsiao [105] was used to test for the possibility of such an error. The test results confirmed that IIA violations were not statistically significant at 90% confidence level and above in the MNL model. Thus, it can be concluded that the MNL model is properly specified regarding the IIA assumption [65]. Another model specification error for both the MNL and MXL correspond to the correlation between the explanatory variables, the omitted variables and the presence of an irrelevant variable [74,98]. In regard to the Wald test results, no significant variables were omitted from the final MNL and MXL models. In addition, several variables were found to be irrelevant to the dependent variable after chi-square (see Table 5) and they are not included in both models. Based on these specification error tests, it can be concluded that the model is reasonably stable across the data and is properly specified. The fitted MXL model is capturing the unobserved heterogeneity caused by underreporting of fatal crash data.

Estimation results reveal that fatal motorcycle crashes on expressway roads were found to be less likely (RRR = 0.59, 95% CI = 0.41– 0.85) to result in a MCF1V crash. On the contrary, fatal motorcycle crashes were found to be almost two times (RRR = 1.82, 90% CI = 1.00–3.30) more likely to result in a MCF3V than a MCF2V. The average pseudo-elasticity value (see Table 8) also confirms that the probability of a MCF3V crash increases by 6.2%. However, on primary and secondary roads, MCF3V has a much higher probability of occurrence by 24.6% and 14.1% respectively. At the same time, MCF2V have also an average pseudo-elasticity value of probability of increase of occurrence by 7.9% on primary roads and 3.8% on secondary roads. Similarly, fatal motorcycle crashes on collector roads were found to be less likely (RRR = 0.75, 90% CI = 0.58, 0.96) to result in a MCF1V crash, while the probability of a MCF1V crash decreases by 5.2% on a collector road. The outcome of this study in terms of road hierarchy shows consistency with previous studies [2,49,66,106,107] which underline the significance of road hierarchy as a potential risk factor of motorcycle crashes. 3.2. Location type and road geometry factors Both fitted MNL and MXL models confirm the significant association between location type and type of MCFNV. Particularly, estimation results revealed that fatal motorcycle crashes were found to be less likely (RRR = 0.81, 90% CI = 0.67–0.98) to result in a MCF1V crash than a MCF2V when they were occurred on small town roads than cities. Similarly, MCF1V (RRR = 0.81, 95% CI = 0.66–0.99) and MCF3V (RRR = 0.68, 95% CI = 0.50–0.93) had a decreased likelihood when a fatal motorcycle crash was occurred in rural environment. This result might be expected for MCF1V in small towns when the increasing number of motorcycles and other motor vehicles are considered. On the other hand, despite a relatively high traffic volume in rural environment, the probability of MCF2V crashes increases by almost 7% with a comparison of a 17.5% decrease on MCF3V crashes. This specific result may be explained by motorcyclists' behavior on various types of roads with low traffic volume. For instance, a very recent study in Malaysia [107] found that motorcyclists were observed not to turn their heads to look for vehicles when entering a road with a low traffic volume compared to their behavior on roads with high traffic volume. This in turn may increase the risk of a fatal crash if the motorcyclist fails to notice an approaching vehicle on the main road while making a turn into that road. Estimation results revealed that road geometric factors such as straight sections, curve sections and roundabouts affect statistically significantly the probability of MCF1V crashes in relation to MCF2V crashes. For instance, MCF1V (RRR = 3.95, 90% CI = 1.02–15.22) crashes are almost four times more likely than MCF2V crashes to occur on straight road sections, while eight times (RRR = 8.34, 95% CI = 1.45–48.02) more likely than MCF2V crashes to occur on roundabout. In addition, MCF1V (RRR = 6.25, 90% CI = 1.25–31.29) crashes are 6 times likely than MCF2V crashes to occur on curve road sections, and its probability of occurrence increases by 17.5% compared to any other motorcycle fatal crash outcome. 3.3. Speed limit and road marking factor MCF3V (RRR = 1.79, 99% CI = 1.04–3.09) crashes are almost twice more likely than MCF2V crashes to occur on roads with 110 km/h speed limit, while 24% to 25% likely to than MCF2V to occur on roads with 90 km/h speed limit (RRR = 1.24, 90% CI = 1.03–1.49) and 70 km/h speed limit (RRR = 1.25, 95% CI = 1.05–1.50). Moreover, based on the pseudo-elasticity value, the probability of occurrence of MCF3V may increase by 2.7% on 110 km/h roads, 4.2% on 90 km/h roads and 5.9% on 70 km/h roads. The results of the mixed logit model showed that 70 km/h speed limit is normally distributed with mean 0.091 and standard deviation 6.383 for MCF3V. This variability is likely

Please cite this article as: M.M. Abdul Manan, et al., Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia, IATSS Research (2017), https://doi.org/10.1016/j.iatssr.2017.11.001

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M.M. Abdul Manan et al. / IATSS Research xxx (2017) xxx–xxx Table 8 Average direct pseudo-elasticity of variables.

[MCF1V]: Motorcycle single-vehicle fatal crash, [MCF2V]: Motorcycle fatal crash involving another vehicle, [MCF3V]: Motorcycle fatal crash involving two or more other vehicles. The empty cell represents no likelihood of increase or decrease in probability of occurrence.

to capture the unobserved heterogeneity in MCF3V that could include factors that is not measured in the data set [83]. On the other hand, MCF1V (RRR = 0.65, 95% CI = 0.49–0.85) crashes are less likely than MCF2V crashes to occur on roads with marking that indicate prohibition of overtaking, i.e. a continuous double line. In contrast, MCF1V (RRR = 1.65, 95% CI = 1.24–2.20) crashes are almost twice more likely than MCF2V crashes to occur on roads with no markings, and the probability of its occurrence increases by 2.5%, based on the average pseudo-elasticity value. On the other hand, MCF3V (RRR = 1.51, 90% CI = 1.03–2.22) crashes are 51% more likely than MCF2V crashes to occur on road with continuous double line. The average pseudo-elasticity value also confirms that the probability of occurrence for a MCF3V increases on roads with double lines by 8.6%. The results of the mixed logit model revealed that double-line marking is another random effect parameter with mean − 0.719 and standard deviation 1.301 for MCF1V. Again, this result is likely to capture the possible unobserved heterogeneity due to the underreporting of the data. 3.4. Road surface quality and condition factor MCF1V crashes are almost four times more likely than MCF2V crashes to occur on road with rutting and corrugation (Rut: RRR = 4.19, 95% CI = 1.75–10.00, Corrugation: RRR = 3.66, 95% CI = 1.60– 8.33). However, based on the average pseudo-elasticity value, MCF1V crashes has a much higher probability of occurrence on smooth road surface by N 50% compared to other type of road surface. As for the road surface condition, MCF1V (RRR = 0.27, 95% CI = 0.07–0.98) and MCF3V (RRR = 0.51, 90% CI = 0.28–0.92) crashes is less likely than MCF2V crashes to occur on flooded and wet road condition. In other words, MCF2V is two (1/0.51) to four times (1/0.27) likely than MCF1V and MCF3V crashes to occur on wet and flooded road surface condition.

3.5. Time of day and lighting condition As for the time of day factor, MCF1V (RRR = 2.34, 99% CI = 1.79– 3.10) crashes are twice likely than MCF2V to happen during ‘wee hours’ (i.e. the hours very late at night or very early in the morning), i.e. 00:00 am–6 am. Moreover, a MCF1V crash during wee hours is likely to increase its probability to occur by 8.5%. At the same time, MCF1V and MCF3V are also predicted to increase its probability of occurrence during evening hours, i.e. 7 pm–12 pm, by 4.3% and 3.9% respectively. As for the lighting condition, daylight has an effect of increasing the likelihood of occurrence of MCF2V by 3.2% and MCF3V by 7.6%, based on the average pseudo-elasticity value. In other perspective, MCF1V (RRR = 0.82, 95% CI = 0.68–0.98) crashes are less likely than MCF2V to occur during daylight conditions.

4. Discussion The fundamental contribution of this paper to the research literature is the identification of various road characteristics and environmental risk factors that affect three types of fatal crashes of motorcyclists in Malaysia, i.e. 1) motorcycle single-vehicle fatal crash (MCF1V), 2) motorcycle fatal crash involving another vehicle (MCF2V) and 3) motorcycle fatal crash involving two or more vehicles (MCF3V), by adopting both Multinomial Logit (MNL) and Mixed Logit (MXL) models to predict the probability of these types of fatal crashes. The study examined 9176 fatal cases involving motorcycles in Malaysia between 2010 and 2012. The independent variables were road characteristic and environment including types of road hierarchy, location, road geometry, posted speed limit, road marking type, time of day, lighting and weather condition during the fatal crash. The results suggest that certain road characteristic and environmental factors increase the probability of MCF1V such as curve road sections, no road marking, the quality of the road

Please cite this article as: M.M. Abdul Manan, et al., Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia, IATSS Research (2017), https://doi.org/10.1016/j.iatssr.2017.11.001

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Fig. 1. The road characteristic and environmental risk factors that increase the probability of the motorcycle fatal crashes based on the number of vehicles involved.

surface, as well as wee hour, i.e. between 00 am to 6 am (see Fig. 1 for the visual representation of the results). As for the motorcycle fatal crashes involving multiple vehicles (i.e. MCF2V and MCF3V), factors such as expressway, primary and secondary road, speed limit over 70 km/h, roads with double lines and daylight condition may increase the probability of their occurrence. Additionally, the results also suggest that during the s between 7:00 p.m. to 12:00 a.m., the probability of MCF1V and MCF3V occurrence may also increase. 4.1. Discussion on motorcycle single-vehicle fatal crashes The factors contributing to single vehicle fatal crashes involving motorcycles in Malaysia are quite similar to the findings in the developed countries. N 60% of the motorcycle single-vehicle crashes occurred on bend road sections in Spain and Sweden, which in turn resulted in a higher number of fatalities compared to straight road sections [9,14]. As for Malaysian motorcycle single-vehicle fatal crashes, the finding of this study supports the finding of Liu and Subramanian [21], Preusser et al. [44], Clarke et al. [45] and Schneider et al. [46], who found that a high portion of motorcycle crashes involved going out of control on curve road sections. The occurrence of motorcycle single-vehicle fatal crash is predicted to increase compared to multiple-vehicle motorcycle fatal crashes on roads with no road marking. Roads with no markings are categorized by the police as narrow rural roads that connect plantations and villages, roads that are under construction or diverted roads that are open to public without any markings, and roads with faded road markings. Other studies do not specifically mention the risk of having no road markings, except for Elliott et al. [38], who claimed that ineffective marking can induce instability for motorcycle riders. On a similar note, narrow rural roads, which may not have road marking, have account for a significant number of fatalities among motorcyclists [22,37,38]. Another possible explanation for this risk factor is that roads with no road marking may not provide proper guidance for motorcyclists; especially at curve road sections that may increase the probability of motorcycle single-vehicle fatal crashes. Based on the model estimates, motorcycles single-vehicle fatal crash occurrences are likely to increase by 56.1% on smooth road surface quality compared to motorcycle fatal crashes involving multiple vehicles (i.e. MCF2V and MCF3V). The increase of this probability may be due to the effect of motorcyclists speeding on smooth road surface. This is an example of the theory of behavioral adaptation [52,108], which claims that people make adjustments of their behavior according to

the perceived level of risk, as they may perceive the risk to be less when riding on a smooth road surface and tend to be bolder (e.g. maintaining higher speed than on road surface with some defects). The results of this study are in line with studies by Saleh et al. [14] and Shaheed and Gkritza [23], who showed that the majority of motorcycle single-vehicle crashes occur during fine weather and in dry road surface conditions. Lower speeds are maintained by riders in adverse situations as they adjust for the perceived higher risk and appear to exercise more care while riding on wet road surfaces [31]. In terms of the temporal factor, i.e. hour category, the model estimates that ‘wee hours’ (i.e. 00 am–6 am), 6 am–9 am, 9 am–12 am increase motorcycle single-vehicle fatal crash probability. This estimation corresponds to Haworth, et al. [48] findings by which motorcycle single-vehicle crashes were most common from midnight to 6 am and midday to 6 pm. Moreover, our study is also in line with the study by Haque, et al. [49], which showed that motorcyclists were more vulnerable during nighttime on expressways, perhaps because of increased speeds and hence stronger resulting impacts. 4.2. Discussion on motorcycle multi vehicle fatal crash While there are similarities in the findings concerning motorcycle single-vehicle fatal crashes in the developed countries, and in Malaysia, when it comes to motorcycle multiple-vehicle fatal crashes (MCF2V and MCF3V) there are significant differences. This study shows that factors such as expressway, primary and secondary roads, speed limit over 70 km/h, roads with double lines and daylight condition, may increase the probability of occurrence of multiple-vehicle crash involving motorcycles. As an earlier study [52] showed, high-speed roads are associated with higher injury-severity levels while roads with speed limits exceeding 70 km/h have a 132% higher likelihood of a fatal injury. A study done in Singapore has shown that high motorcycle crash risk involving multiple vehicles are associated with expressways [31]. Another risk factor besides expressways is the primary and secondary roads. A study found the highest motorcycle fatality rate per 100 km and per 100,000 motorcycles along Malaysian primary roads, and the rate is higher than that on secondary roads, local streets and minor roads combined [20]. One of the reasons that may contribute to the high probability of occurrence of motorcycle fatal crash involving multiple vehicles on primary and secondary roads is probably the mix traffic environment [6,18,31,47], where motorcycles move together with larger vehicles at higher speed differences [107]. High speed difference seems to occur on high-speed roads [108], which have been found to result in a high

Please cite this article as: M.M. Abdul Manan, et al., Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia, IATSS Research (2017), https://doi.org/10.1016/j.iatssr.2017.11.001

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motorcycle crash risk [31]. Another contributory factor on Malaysian primary and secondary roads, increasing the probability of motorcycle fatal crash involving multiple-vehicles is the presence of access points [107], i.e. small un-signalized T-junctions. According to research done by Abdul Manan and Várhelyi [107], motorcycle crash fatalities per kilometer on Malaysian primary roads are affected by the number of access points per kilometer and the number of average daily traffic of motorcycles. Double line marking, i.e. marking to notify that overtaking is not permitted, is estimated to be one of the road characteristic risk factors that increase the likelihood of motorcycle fatal crash involving multiplevehicles by 2%–9%. Double line marking is used on undivided roadways, either on rural primary or rural secondary roads in Malaysia. Several studies showed that undivided roadways account for a majority of motorcycle fatalities [22,26,108], especially for motorcycle single-vehicle fatal crashes [22]. According to the Malaysian police records, head-on collisions on roads with double line marking are the most frequent among all types of collisions involving motorcycles for 3 consecutive years, i.e. 2009, 2010 and 2011 [109]. One of the unique aspects of multiple vehicle fatal crashes and single vehicle fatal crashes involving motorcycle is the effect of motorcycle type on injury severity. As previously mentioned (see Table 1), motorcycle crashes involving two or more other vehicles in Malaysia are most likely to end in a fatal outcome compared to a motorcycle singlevehicle crash. This is probably due to the differences in weight and speed between the vehicle types mixing on these roads in Malaysia. Studies have shown that the impacts of a collision with a fixed (static) object in single-vehicle crashes are different from the impacts of collision with other vehicles (mostly moving objects) in multi-vehicle crashes [104,110]. In Malaysia, the likelihood of a single-vehicle fatal outcome involving a motorcycle may be less compared to the fatal outcome of multi-vehicle crashes, due to the speed and size of Malaysian motorcycles. Ninety five percent of Malaysian motorcycles are with engines below 150 cm3 and the weight and operating speed are smaller than of motorcycles in developed countries [6,7,111]. With smaller size and lower operating speed, Malaysian motorcycles may have a lower impact force when colliding with fixed roadside objects, resulting from a single-vehicle crash. The fact that the likelihood of fatal outcome of motorcycle crashes involving two or more vehicles is higher than single-vehicle fatal outcome is maybe due to motorcycle higher impact force with moving vehicles and also may be due multiple impacts from multiple vehicles. Unlike in the developed countries [22,28], Malaysian motorcycles are used for commuting purposes mixing with other traffic [6,17], which increases the likelihood of being involved in multi vehicle crashes [108,112]. A few limitations have thwarted our attempts to work efficiently and accurately on some occasions in this study. In addition to this, even if the main aim of the study was to find road characteristics and environmental risk factors associated with the motorcycle fatal crashes, an attempt was made to include human factors variables (age, gender, motorcycle type, etc.), however due to the inconsistency and inaccuracy of the available data from the police we had to leave out these variables. Moreover, the data collected from the police is not 100% accurate in terms of pinpointing the exact location of the fatal crashes. Most of the police database has identified the correct road hierarchy; however, in some cases it fails to give the correct road geometry in terms of number of lanes and road width, hence these were excluded from the data set. Secondly, reporting of weather conditions by the police may not be accurate because the police relied in their reporting on witnesses of the crash, which may have led to a variation in the descriptions [90,113]. 5. Conclusion and recommendation This study is the first and one of its kind in Malaysia and probably in South East Asian countries, as it looks into the road and environmental factors contributing to motorcycle fatal crashes concerning three fatal

crash types in Malaysia. For motorcycle single-vehicle fatal crashes, installing motorcycle-friendly roadside barriers as a protection from hitting trees, lighting poles, etc., is recommended. Also, maintenance of road surface and road markings is of high relevance and curve speed markings to lower the approach speed at curves are to be explored. In order to reduce multi vehicle crashes involving motorcycles, we recommend that motorcyclist be separated away from the main traffic via an exclusive motorcycle path or motorcycle lane along high-speed roads, as it is proven to be successful in significantly reducing motorcycle crash. If complete separation is not feasible, introducing road shoulder along rural roads can also be effective in providing a space for the motorcycle to move safely. A countermeasure to eliminate multi vehicle crashes involving motorcycles on roads with continuous double line marking is to install middle barrier or milling the surface along the middle lines to alert drivers passing the line on these types of roads. Other countermeasures of interest, based on our findings, are making the motorcyclists more visible by, among others more striking clothing and the use of daytime running light. Future research should be along this topic is encouraged to look deeper into the course of events (i.e. risky riding behavior observation of motorcyclists in a mix traffic condition), as well as into human factors concerning motorcycle fatal crashes involving two or more vehicles, concentrating on primary and secondary roads.

References [1] G. Yannis, J. Golias, E. Papadimitriou, Driver age and vehicle engine size effects on fault and severity in young motorcyclists accidents, Accid. Anal. Prev. 37 (2) (2005) 327–333. [2] D. Eustace, V.K. Indupuru, P.W. Hovey, Identification of Risk Factors Associated with Motorcycle-related Fatalities in Ohio, vol. Paper 8, Civil and Environmental Engineering and Engineering Mechanics Faculty Publications, 2011. [3] S.M. Rifaat, R. Tay, A. de Barros, Severity of motorcycle crashes in Calgary, Accid. Anal. Prev. 49 (2012) 44–49. [4] WHO, Global Status Report on Road Safety 2013: Supporting a Decade of Action, World Health Organization, Geneva, Switzerland, 2013. [5] N. Haworth, Powered two wheelers in a changing world, challenges and opportunities, Accid. Anal. Prev. 44 (1) (2012) 12–18. [6] T.-P. Hsu, M.S. Ahmad Farhan, X.D. Nguyen, A comparative study on motorcycle traffic development of Taiwan, Malaysia and Vietnam, J. Eastern Asia Soc. Transp. Stud. 5 (2003) 179–193. [7] H. Hussain, R.S. Radin Umar, M.S. Ahmad Farhan, M.M. Dadang, Key components of a motorcycle-traffic system - a study along the motorcycle path in Malaysia, IATSS Res. 29 (1) (2005) 50–56. [8] M. Senbil, J. Zhang, A. Fujiwara, Motorization in Asia - 14 countries and three metropolitan area, IATSS Res. 31 (1) (2007) 46–58. [9] Trafikverket, Improved Safety for Motorcycle and Moped Riders: Joint Strategy for the Period 2010–2020, Version 1.0, The Swedish Transport Administration, 2010 043 (2010). [10] M. Wick, E.J. Müller, A. Ekkernkamp, G. Muhr, The motorcyclist: easy rider or easy victim? An analysis of motorcycle accidents in Germany, Am. J. Emerg. Med. 16 (3) (1998) 320–323. [11] D. Albalate, L. Fernández-Villadangos, Motorcycle injury severity in Barcelona: the role of vehicle type and congestion, Traffic Inj. Prev. 11 (6) (2010) 623–631. [12] N. Clabaux, T. Brenac, C. Perrin, J. Magnin, B. Canu, P. Van Elslande, Motorcyclists' speed and “looked-but-failed-to-see” accidents, Accid. Anal. Prev. 49 (0) (2012) 73–77. [13] ICCS, Understanding Risk Taking Behaviour Within the Context of PTW Riders: A Report on Rider Diversity with Regard to Attitudes, Perceptions and Behavioural Choices, Institute of Communication and Computer Systems - National Technical University of Athens (ICCS-NTUA), Athens, Greece, 2010. [14] P. Saleh, J. Golias, G. Yannis, E. Vlahogianni, P. Papantoniou, J. Diez, D. Basacik, M. Chattington, A. Weare, N. Baldanzini, D. Grassi, A. Hegewald, D. Ebersbach, Interaction between Powered Two-wheeler Accidents and Infrastructure Austrian Institute of Technology & European Union, 2010. [15] M.M. Abdul Manan, Factors Associated With Motorcyclists' Safety at Access Points Along Primary Roads in Malaysia(Doctor of Philosophy in Engineering PhD Thesis) Bulletin 290, Department of Technology and Society, Department of Technology and Society, Lund University, Sweden, Department of Technology and Society, Lund, Sweden, 2014. [16] H.-L. Chang, T.-H. Yeh, Risk factors to driver fatalities in single-vehicle crashes: comparisons between non-motorcycle drivers and motorcyclists, J. Transp. Eng. 132 (2006) 227–236. [17] ESCAP, Road Safety, Economic and Social Commission for Asia and the Pacific, New York, USA, 2009. [18] R.G. Sigua, H.S.O. Palmiano, Assessment of road safety in the ASEAN Region, East Asia Society for Transportation Studies 2005, pp. 2032–2045.

Please cite this article as: M.M. Abdul Manan, et al., Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia, IATSS Research (2017), https://doi.org/10.1016/j.iatssr.2017.11.001

M.M. Abdul Manan et al. / IATSS Research xxx (2017) xxx–xxx [19] ADB-ASEAN, ADB-ASEAN: Regional Road Safety Program Country Report CR 5, Asian Development Bank–Association of Southeast Asian Nations, Malaysia, 2003. [20] M.M. Abdul Manan, A. Várhelyi, Motorcycle fatalities in Malaysia, IATSS Res. 36 (1) (2012) 30–39. [21] C. Liu, R. Subramanian, Factors Related to Fatal Single-Vehicle Run-Off-Road Crashes, NHTSA Technical Report, DOT HS 811 232 National Highway Traffic Safety Administration, Washington DC, USA, 2009. [22] NHTSA, Fatal Single Vehicle Motorcycle Crashes, Mathematical Analysis Division, National Center for Statistics and Analysis, National Highway Traffic Safety Administration, Washington, D.C, 2001. [23] M.S. Shaheed, K. Gkritza, A latent class analysis of single-vehicle motorcycle crash severity outcomes, Anal. Methods Accident Res. 2 (0) (2014) 30–38. [24] A. Longthorne, C. Varghese, U. Shankar, Fatal Two-vehicle Motorcycle Crashes, National Highway Traffic Safety Administration, Washington, DC, USA, 2007 (DOT HS 810 834). [25] Trafikverket, Increased Safety for Motorcycle and Moped Riders: Joint Strategy Version 2.0 for the Years 2012–2020, 194, The Swedish Transport Administration, 2012 (2012). [26] HVU, Motorcycle Accidents, Denmark, 2009. [27] C. Stefan, S. Höglinger, K. Machata, Case Study: Motorcycle Accidents, KfV – Kuratorium für Verkehrssicherheit, Austria, 2003. [28] P. Johnston, C. Brooks, H. Savage, Fatal and serious road crashes involving motorcyclists, in: T. Department of Infrastructure, Regional Development and Local and Government (Ed.), Sydney: Road Safety Infrastructure and Surface Transport Policy Department of Infrastructure, Transport, Regional Development and Local Government, 2008. [29] M.C. McCarthy, L.K. Walter, R. Hutchins, R. Tong, M. Keigan, Comparative Analysis of Motorcycle Accident Data from OTS and MAIDS, Transport Research Lab (TRL), PPR 168, 2007. [30] DfT, Motorcycle Casualties in Reported Road Accidents: GB 2008 - Road Accident Statistics Factsheet No. 7 – 2010, Department for Transport, National Statistics, UK, 2010. [31] M.M. Haque, H.C. Chin, A.K. Debnath, An investigation on multi-vehicle motorcycle crashes using log-linear models, Saf. Sci. 50 (2) (2012) 352–362. [32] H.H. Nguyen, A comprehensive review of motorcycle safety situation in Asian countries, J. Soc. Transp. Traffic Stud. 4 (3) (2013) 20–29. [33] T.Y. Pang, R.S. Radin Umar, A. Azhar Abdul, S. Harwant, A.W. Shahrom, M. Abdul Halim, N. Zahari, O. Mohd Shafiee, Fatal injuries in Malaysian motorcyclist, Int. Med. Res. J. 3 (2) (1999) 115–119. [34] R.S. Radin Umar, Road accidents in Malaysia, IATSS Res. 18 (2) (1994) 38–41. [35] R.S. Radin Umar, A. Aminuddin, Accident Investigation by Conflict Study, ROAD SAFETY RESEARCH CENTRE (RSRC), Faculty of Engineering, University Putra Malaysia, 2000. [36] WHO, Violence and Injury Prevention Country Profiles, World Health Organization, Geneva, Switzerland, 2013. [37] H.H. Hurt, J.V. Ouellet, D.R. Thom, Motorcycle Accident Cause Factors and Identification of Countermeasures, HS-5-01160, National Highway Traffic Safety Administration, Washington D.C., USA, 1981. [38] M.A. Elliott, C.J. Baughan, J. Broughton, B. Chinn, G.B. Grayson, J. Knowles, L.R. Smith, H. Simpson, Motorcycle safety: a scoping study, TRL Report TRL 581, TRL, UK, 2003. [39] ACEM, MAIDS—Motorcycle Accident In-depth Study, Association des Constructeurs Europeens de Motorcycles, Brussels, 2003. [40] CARRS-Q, State of the Road: A Fact Sheet of the Center for Accident Research & Road Safety - Queensland, Center for Accident Research & Road Safety, Queensland, 2010. [41] M.-R. Lin, W. Huang, H.-F. Hwang, H.-D.I. Wu, L.-L. Yen, The effect of crash experience on changes in risk taking among urban and rural young people, Accid. Anal. Prev. 36 (2) (2004) 213–222. [42] T.R. Jaafar, M.F. Mustafa, S. Kemin, R. Kasiran, Kemalangan jalan raya: Analysis Data membabitkan pengguna motosikal, Jurnal Teknologi 38 (B) (2003) 1–14. [43] NPRA, Handbook 245e: MC Safety—Design and Operation of Roads and Traffic Systems, Norwegian Public Roads Administration, Norway, 2004. [44] D.F. Preusser, A.F. Williams, R.G. Ulmer, Analysis of fatal motorcycle crashes: crash typing, Accid. Anal. Prev. 27 (6) (1995) 845–851. [45] D.D. Clarke, P. Ward, C. Bartle, W. Truman, The role of motorcyclist and other driver behaviour in two types of serious accident in the UK, Accid. Anal. Prev. 39 (5) (2007) 974–981. [46] W.H. Schneider, P.T. Savolainen, D.N. Moore, Effects of horizontal curvature on single-vehicle motorcycle crashes along rural two-lane highways, Transp. Res. Rec. 2194 (1) (2010) 91–98. [47] R.S. Radin Umar, The value of daytime running headlights initiavites on motorcycles crashes in Malaysia, Transp. Commun. Bull. Asia Pacific (74) (2005) 17–31. [48] N. Haworth, V.P.L. Bowland, N. Pronk, Case-control Study of Fatal Single Vehicle Crashes, Monash University Accident Research Centre, Monash University Accident Research Centre, Australia, 1997. [49] M.M. Haque, H.C. Chin, H. Huang, Modeling fault among motorcyclists involved in crashes, Accid. Anal. Prev. 41 (2) (2009) 327–335. [50] L.M. Hurst, Motorcyclists: A Literature Review and Exploratory Analysis of Fatalities and Serious Injury Collisions in Relation to Motorcyclists: Implications for Education, Engineering and Enforcement Initiatives, Plymouth University, UK, 2011. [51] P.O. Wanvik, Effects of road lighting: an analysis based on Dutch accident statistics 1987–2006, Accid. Anal. Prev. 41 (1) (2009) 123–128. [52] P. Savolainen, F. Mannering, Probabilistic models of motorcyclists' injury severities in single- and multi-vehicle crashes, Accid. Anal. Prev. 39 (5) (2007) 955–963.

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[53] C.-W. Pai, W. Saleh, Modelling motorcyclist injury severity by various crash types at T-junctions in the UK, Saf. Sci. 46 (8) (2008) 1234–1247. [54] C.-W. Pai, W. Saleh, An analysis of motorcyclist injury severity under various traffic control measures at three-legged junctions in the UK, Saf. Sci. 45 (8) (2007) 832–847. [55] T.Y. Pang, R.S. Radin Umar, A.A. Azhar, M.H.M. Megat Ahmad, M.T. Mohd Nasir, S. Harwant, Accidents characteristics of injured motorcyclist in Malaysia, Med J Malaysia 55 (1) (2000) 45–50. [56] V. Kasantikul, Motorcycle Accident Causation and Identification of Countermeasures in Thailand, Volume 1: Bangkok study, Chulalongkorn University, Thailand, 2001. [57] E.I. Vlahogianni, G. Yannis, J.C. Golias, Overview of critical risk factors in PowerTwo-Wheeler safety, Accid. Anal. Prev. 49 (0) (2012) 12–22. [58] A. Jimenez, J.P. Bocarejo, R. Zarama, J. Yerpez, A case study analysis to examine motorcycle crashes in Bogota, Colombia, J. Saf. Res. 52 (2015) 29–38. [59] D.J. Gabauer, X. Li, Influence of horizontally curved roadway section characteristics on motorcycle-to-barrier crash frequency, Accid. Anal. Prev. 77 (2015) 105–112. [60] E.R. Teoh, M. Campbell, Role of motorcycle type in fatal motorcycle crashes, J. Saf. Res. 41 (6) (2010) 507–512. [61] C.-W. Pai, Motorcyclist injury severity in angle crashes at T-junctions: identifying significant factors and analysing what made motorists fail to yield to motorcycles, Saf. Sci. 47 (8) (2009) 1097–1106. [62] M.A. Quddus, R.B. Noland, H.C. Chin, An analysis of motorcycle injury and vehicle damage severity using ordered probit models, J. Saf. Res. 33 (4) (2002) 445–462. [63] R.A. Blackman, N.L. Haworth, Comparison of moped, scooter and motorcycle crash risk and crash severity, Accid. Anal. Prev. 57 (0) (2013) 1–9. [64] Y. Chung, T.-J. Song, B.-J. Yoon, Injury severity in delivery-motorcycle to vehicle crashes in the Seoul metropolitan area, Accid. Anal. Prev. 62 (2014) 79–86. [65] V. Shankar, F. Mannering, An exploratory multinomial logit analysis of single-vehicle motorcycle accident severity, J. Saf. Res. 27 (3) (1996) 183–194. [66] S. Jung, Q. Xiao, Y. Yoon, Evaluation of motorcycle safety strategies using the severity of injuries, Accid. Anal. Prev. 59 (2013) 357–364. [67] P. Wedagama, A. Multinomial Logit, Model for estimating the influence of household characteristics on motorcycle ownership: a case study in Denpasar City, Bali, ITS, J. Civ. Eng. 29 (1) (2009) 2–9. [68] S.R. Geedipally, P.A. Turner, S. Patil, Analysis of motorcycle crashes in Texas with multinomial logit model, J. Transp. Res. Board 2265 (2011) 62–69. [69] D. Chimba, T. Sando, Multinomial probability assessment of motorcycle injury severities, Adv. Transp. Stud. Section B (21) (2010) 73–80. [70] C.-W. Pai, J.-J. Hsu, J.-L. Chang, M.-S. Kuo, Motorcyclists violating hook-turn area at intersections in Taiwan: an observational study, Accid. Anal. Prev. 59 (0) (2013) 1–8. [71] M.S.B. Shaheed, K. Gkritza, W. Zhang, Z. Hans, A mixed logit analysis of two-vehicle crash severities involving a motorcycle, Accid. Anal. Prev. 61 (2013) 119–128. [72] A. Maistros, W.H. Schneider Iv, P.T. Savolainen, A comparison of contributing factors between alcohol related single vehicle motorcycle and car crashes, J. Saf. Res. 49 (2014), 129.e1-135. . [73] C.-J. Lan, J. Li, Statistical evaluation of motorcycle crash injury severities by using multinomial modelsTransportation Research Board 85th Annual Meeting, 2006. [74] A.K. Celik, E. Oktay, A multinomial logit analysis of risk factors influencing road traffic injury severities in the Erzurum and Kars Provinces of Turkey, Accid. Anal. Prev. 72C (2014) 66–77. [75] D.L. Rosman, M.W. Knuiman, A comparison of hospital and police road injury data, Accid. Anal. Prev. 26 (2) (1994) 215–222. [76] F. Ye, D. Lord, Investigation of effects of underreporting crash data on three commonly used traffic crash models, Transportation Research Board, J. Transp. Res. Board 2241 (2011) 51–58. [77] F. Ye, D. Lord, Comparing three commonly used crash severity models on sample size requirements: multinomial logit, ordered probit and mixed logit models, Anal. Methods Accident Res. 1 (2014) 72–85. [78] R. Majdzadeh, K. Khalagi, K. Naraghi, A. Motevalian, M. Eshraghian, Determinants of traffic injuries in drivers and motorcyclists involved in an accident, Accid. Anal. Prev. 40 (1) (2008) 17–23. [79] M.-D. Li, J.-L. Doong, W.-S. Huang, C.-H. Lai, M.-C. Jeng, Survival hazards of road environment factors between motor-vehicles and motorcycles, Accid. Anal. Prev. 41 (5) (2009) 938–947. [80] N.V. Malyshkina, F.L. Mannering, Markov switching multinomial logit model: an application to accident-injury severities, Accid. Anal. Prev. 41 (4) (2009) 829–838. [81] A. Agresti, Categorical Data Analysis, John Wiley & Sons, second edition, 2002. [82] B.G. Tabachnick, L.S. Fidell, Using Multivariate Statistics, 5th edition Pearson, California, USA, 2008. [83] J.C. Milton, V.N. Shankar, F.L. Mannering, Highway accident severities and the mixed logit model: an exploratory empirical analysis, Accid. Anal. Prev. 40 (1) (2008) 260–266. [84] K. Gkritza, F.L. Mannering, Mixed logit analysis of safety-belt use in single- and multi-occupant vehicles, Accid. Anal. Prev. 40 (2) (2008) 443–451. [85] D.N. Moore, W.H. Schneider Iv, P.T. Savolainen, M. Farzaneh, Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations, Accid. Anal. Prev. 43 (3) (2011) 621–630. [86] D. McFadden, Regression-based specification tests for the multinomial logit model, J. Econ. 34 (1–2) (1987) 63–82. [87] S.M. Rifaat, R. Tay, A. de Barros, Effect of street pattern on the severity of crashes involving vulnerable road users, Accid. Anal. Prev. 43 (1) (2011) 276–283. [88] J.K. Kim, S. Kim, G.F. Ulfarsson, L.A. Porrello, Bicyclist injury severities in bicyclemotor vehicle accidents, Accid. Anal. Prev. 39 (2) (2007) 238–251.

Please cite this article as: M.M. Abdul Manan, et al., Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia, IATSS Research (2017), https://doi.org/10.1016/j.iatssr.2017.11.001

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M.M. Abdul Manan et al. / IATSS Research xxx (2017) xxx–xxx

[89] ETSC, Social and Economic Consequences of Road Traffic Injury in Europe European Transport Safety Council, Brussels, 2007. [90] M.W. Maas, S. Harris, Police recording of road accident in-patients: investigation into the completeness, representativity and reliability of police records of hospitalized traffic victims, Accid. Anal. Prev. 16 (3) (1984) 167–184. [91] T. Yamamoto, J. Hashiji, V. Shankar, Underreporting in traffic accident data, bias in parameters and the structure of injury severity models, Accid. Anal. Prev. 40 (4) (2008) 1320–1329. [92] D.G. Kim, Y. Lee, S. Washington, K. Choi, Modeling crash outcome probabilities at rural intersections: application of hierarchical binomial logistic models, Accid. Anal. Prev. 39 (1) (2007) 125–134. [93] K. Train, Discrete Choice Methods with Simulation, Cambridge University Press, UK, 2003. [94] C.R. Bhat, Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences, Transp. Res. B Methodol. 37 (9) (2003) 837–855. [95] W.H. Greene, Econometric Analysis, 5th ed Prentice Hall, Upper Saddle River, New Jersey, 2002. [96] A. Bayaga, Multinomial logistic regression: usage and application in risk analysis, J. Appl. Quant. Methods 5 (2) (2010) 288–297. [97] STATA, STATA Multilevel Mixed-effects Reference Manual Release 13, Texas, StataCorp LP, 2013. [98] S.P. Washington, M.G. Karlaftis, F.L. Mannering, Statistical and Econometric Methods for Transportation Data Analysis, CRC Press LLC, Washington, D.C., 2003 [99] P.T. Savolainen, F.L. Mannering, D. Lord, M.A. Quddus, The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives, Accid. Anal. Prev. 43 (5) (2011) 1666–1676. [100] A.R. Hole, Fitting mixed logit models by using maximum simulated likelihood, Stata J. 3 (7) (2007) 388–401.

[101] P. Haan, A. Uhlendorff, Estimation of Multinomial Logit Models With Unobserved Heterogeneity Using Maximum Simulated Likelihood, German Institute for Economic Research, 2006. [102] K. Yau, Risk factors affecting the severity of single vehicle traffic accidents in Hong Kong, Accid. Anal. Prev. 36 (3) (2004) 333–340. [103] Q. Wu, F. Chen, G. Zhang, X.C. Liu, H. Wang, S.M. Bogus, Mixed logit model-based driver injury severity investigations in single- and multi-vehicle crashes on rural two-lane highways, Accid. Anal. Prev. 72 (2014) 105–115. [104] J.M. Holdridge, V.N. Shankar, G.F. Ulfarsson, The crash severity impacts of fixed roadside objects, J. Saf. Res. 36 (2) (2005) 139–147. [105] K.A. Small, C. Hsiao, Multinomial logit specification tests, Int. Econ. Rev. 26 (3) (1985) 619–627. [106] C.-W. Pai, K.P. Hwang, W. Saleh, A mixed logit analysis of motorists' right-of-way violation in motorcycle accidents at priority T-junctions, Accid. Anal. Prev. 41 (3) (2009) 565–573. [107] M.M. Abdul Manan, A. Várhelyi, Motorcyclists' road safety related behavior at access points on primary roads in Malaysia – a case study, Saf. Sci. 77 (0) (2015) 80–94. [108] R. Elvik, A. Høye, T. Vaa, M. Sørensen, The Handbook of Road Safety Measures, second edition Emerald Group Publishing Limited, Bingley, United Kingdom, 2009. [109] PDRM, Statistical report on malaysia road accident, in: R.M. Police (Ed.), Bukit Aman, Kuala Lumpur, Malaysia, 2012. [110] C. Lee, X. Li, Analysis of injury severity of drivers involved in single- and two-vehicle crashes on highways in Ontario, Accid. Anal. Prev. 71 (2014) 286–295. [111] H. Hussain, T.H. Law, R.S. Radin Umar, Preliminary Study of Motorcyce lane Capacity, 2001 1–9. [112] R. Elvik, Dimensions of road safety problems and their measurement, Accid. Anal. Prev. 40 (3) (2008) 1200–1210. [113] K. Austin, The identification of mistakes in road accident records: part 1, locational variables, Accid. Anal. Prev. 27 (2) (1995) 261–276.

Please cite this article as: M.M. Abdul Manan, et al., Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia, IATSS Research (2017), https://doi.org/10.1016/j.iatssr.2017.11.001