A mixed logit analysis of two-vehicle crash severities involving a motorcycle

Accepted Manuscript Title: A Mixed Logit Analysis of Two-Vehicle Crash Severities Involving a Motorcycle Authors: Mohammad Saad B. Shaheed, Konstantina Gkritza, Wei Zhang, Zach Hans PII: DOI: Reference:

S0001-4575(13)00227-3 http://dx.doi.org/doi:10.1016/j.aap.2013.05.028 AAP 3176

To appear in:

Accident Analysis and Prevention

Received date: Revised date: Accepted date:

29-11-2011 1-5-2013 8-5-2013

Please cite this article as: Shaheed, M.S.B., Gkritza, K., Zhang, W., Hans, Z., A Mixed Logit Analysis of Two-Vehicle Crash Severities Involving a Motorcycle, Accident Analysis and Prevention (2013), http://dx.doi.org/10.1016/j.aap.2013.05.028 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

A Mixed Logit Analysis of Two-Vehicle Crash Severities Involving a Motorcycle

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Mohammad Saad B. Shaheed (Corresponding author) Department of Civil, Construction, and Environmental Engineering Institute for Transportation Iowa State University, Ames, Iowa 50011 E-mail: [email protected]

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Konstantina Gkritza Department of Civil, Construction, and Environmental Engineering Institute for Transportation Iowa State University, Ames, Iowa 50011 Phone: 515-294-2343, Fax: 515-294-7424 E-mail: [email protected]

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Wei Zhang Transportation Engineer, WorleyParsons, China E-mail: [email protected]

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Zach Hans Research Engineer Center for Transportation Research and Education Institute for Transportation Iowa State University, Ames, Iowa 50011 Phone: 515-294-2329 E-mail: [email protected]

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Abstract Using motorcycle crash data for Iowa from 2001 to 2008, this paper estimates a mixed logit model to investigate the factors that affect crash severity outcomes in a collision between a

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motorcycle and another vehicle. These include crash-specific factors (such as manner of collision, motorcycle rider and non-motorcycle driver and vehicle actions), roadway and

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environmental conditions, location and time, motorcycle rider and non-motorcycle driver and

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vehicle attributes. The methodological approach allows the parameters to vary across observations as opposed to a single parameter representing all observations. Our results showed

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non-uniform effects of rear-end collisions on minor injury crashes, as well as of the roadway speed limit greater or equal to 55 mph, the type of area (urban), the riding season (summer) and

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motorcyclist’s gender on low severity crashes. We also found significant effects of the roadway

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surface condition, clear vision (not obscured by moving vehicles, trees, buildings, or other), light

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conditions, speed limit, and helmet use on severe injury outcomes.

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Keywords: motorcycle safety; crash severity; mixed logit model; conspicuity; two-vehicle

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1. Introduction Previous studies in the United States (U.S.) and internationally suggest that motorcycle riders are more vulnerable users compared to vehicle drivers because of their lack of protection

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in the event of a crash (Rifaat et al., 2012), as well as because of low motorcycle conspicuity or the inability of the vehicle drivers to see motorcycle riders (Cercarelli et al., 1992; Hurt et al.,

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1981; Williams and Hoffmann, 1979; Wulf et al., 1989). Particularly in two-vehicle crashes

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involving a motorcycle, motorcyclists are more likely to be victims than at fault (Haque et al., 2009), and low motorcycle conspicuity can be an important factor affecting motorcycle crash

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severity.

Studies show that a conspicuity problem is associated with motorcycles, as left-turning

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vehicles in the traffic stream have a more difficult time recognizing motorcycles in daylight

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when compared to other vehicles (Olson et al., 1979; Williams and Hoffmann, 1979). Making a

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left turn requires significantly greater head and eye movements, and mental workload in comparison to driving straight through an intersection (Wulf et al., 1989). Other factors that can

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contribute to the motorcycle conspicuity problem include the size of the motorcycle, difference in frontal surface area, low luminance or contrast with the background environment, and the ability to maneuver in the traffic stream. The brightness contrast between the motorcyclist and the surroundings may also be a contributing factor (Hole et al., 1996). Measures to enhancing conspicuity include wearing reflective or fluorescent clothing (Buonarosa and Sayer, 2007; de Rome, 2006; de Rome et al., 2011; Olson et al., 1981; Wells et al., 2004); wearing white or light colored helmets (Gershon et al., 2012); using headlights during daytime (Elvik, 1993; Muller, 1984; Olson et al., 1981; Perlot and Prower, 2003; Rumar, 1980; Thomson, 1980; Torrez, 2008; Zador, 1985), and using headlamp modulators (Jenkins and

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Wigan, 1985). Safety campaigns also advocate the importance of conspicuity to motorcycle riders (Baer et al., 2010; Baer and Skemer, 2009). Using motorcycle crash data for Iowa from 2001 to 2008, this paper examines the effect

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of crash-specific factors (such as manner of collision, motorcycle rider and non-motorcycle driver actions), roadway and environmental conditions, location and time, motorcycle rider and

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non-motorcycle driver and vehicle attributes on two-vehicle crash severity outcomes. As it is not

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possible to collect information on all motorcycle conspicuity-related factors (such as rider clothing, helmet color, motorcycle color, motorcycle size, motorcycle and headlight

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configurations) from the state crash database, heterogeneity may arise from these unobserved factors and can present a serious specification problem when developing motorcycle crash

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severity models as a function of potential motorcycle conspicuity factors, among others. To

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address this problem, we estimate a random parameter (mixed) logit model to examine the

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factors affecting two-vehicle motorcycle crash severity outcomes. This methodological approach allows the parameters to vary across observations as opposed to a single parameter representing

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all observations. We also estimate direct and cross-elasticities to assess the impact of individual parameter estimates on the crash severity outcome probabilities.

2. Brief Review of Past Studies

To understand the risk factors affecting motorcycle crash severity, various modeling techniques have been used in previous research. The techniques include logistic regression, ordered logit and probit models, multinomial logit models, and log-linear models. Select studies, representative of these different modeling techniques, are discussed next.

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Shankar and Mannering (1996) performed a multinomial logit (MNL) analysis of singlevehicle motorcycle crash severity and showed that the MNL formulation is a promising approach to evaluate the determinants of motorcycle crash severity. The study found that no-helmet use in

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interaction with a fixed object and alcohol-impaired riding increase the likelihood of a disabling injury or fatality. Quddus et al. (2002) utilized ordered probit models to analyze motorcycle

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damage and injury severity resulting from motorcycle crashes in Singapore using nine years of

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crash data. The factors found to increase the probability of severe injuries included increased engine capacity, headlight not turned on during daytime, collision with pedestrians and stationary

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objects, driving during early morning hours, having a pillion passenger, and when the motorcyclist was determined to be at fault for the crash. Savolainen and Mannering (2007)

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estimated probabilistic models of motorcyclists’ injury severity in single and multi-vehicle

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crashes using a nested logit formulation that overcomes the limitations of the multinomial logit

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and ordered probability models (see discussion in Savolainen et al., 2011). Key findings from the study showed that motorcyclist age, collision type, roadway characteristics, alcohol

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consumption, helmet use, and unsafe speed affected crash-injury outcomes significantly. Recently, Haque et al. (2012) developed a set of log-linear models to study the effect and interactions of various roadway, traffic, and environmental factors on the crash risk of motorcycles using five years of crash data in Singapore. The study revealed that reduced conspicuity of motorcycles at night is hazardous for situations like merging and diverging on expressways. Rifaat et al. (2012) estimated three types of crash severity models (ordered logit model, heterogeneous choice model, and partially constrained generalized ordered logit model) to identify the factors along with urban street patterns contributing to increased motorcycle crash severity in Calgary, Canada. The three types of models yielded very similar results in terms of

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statistical significance and the values of the estimated coefficients of the variables in the models. The study found that roads with less connectivity and more frequent curves than traditional grid pattern were not safer for motorcycle riders.

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In addition, there has been some recent research conducted to investigate whether motorcyclists involved in crashes were at fault using a binary logit model (Haque et al., 2009). It

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was found that contributing factors of motorcycle crash involvement have varying effects

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depending on the crash location (intersections, expressways and non-intersections). Pai et al. (2009) estimated mixed logit models to investigate the contributory factors to motorists’ right-of-

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way (ROW) violations in three types of crashes (approach turn, angle crossing and angle merging) at T-junctions. The study found that the ROW of motorcyclists is more likely to be

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violated by turning motorists at unsignalized T-junctions. Schneider et al. (2012) estimated a

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multivariate probit model to examine the factors determining fault in two-vehicle motorcycle

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crashes. It was found that motorcyclists were more likely to be at fault in rear-end collisions. The other vehicle driver tended to be at fault in crashes occurring at intersections and driveways, as

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well as under other situations that may limit sight distance and conspicuity, such as dark lighting conditions.

3. Data and Descriptive Statistics

Data on reported motorcycle crashes were collected for the 8–year period from 2001 to 2008 from the crash database maintained by the Iowa Department of Transportation (DOT). The collected data included information on two-vehicle motorcycle crashes where one vehicle was a motorcycle and the other was a non-motorcycle vehicle. Attributes included: manner of crash, crash severity, major cause of the crash, and events contributing to the crash; year, month, day

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and time of crash; crash location (urban or rural area); road surface and environmental conditions; information about the motorcycle rider (such as gender, age, helmet use, and if under the influence of alcohol, drug, or medication) and the driver of the non-motorcycle vehicle

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involved in the crash (such as gender, age, and if under the influence of alcohol, drug or medication); model year of the motorcycle and the non-motorcycle vehicle involved in the crash,

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and type of the non-motorcycle vehicle. However, potential conspicuity-related factors (as

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established in the literature) such as rider clothing, color of motorcycle, helmet color, and use of daytime running lights, could not be collected from the crash database. The crashes occurring

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within one mile of the corporate city limits were defined as urban, while the crashes occurring outside the city boundaries were defined as rural. Property damage crashes of less than $1,000

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were not included in the crash database maintained by the Iowa DOT.

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A total of 7,325 motorcycle crashes were reported during the 8–year analysis period

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(2001–2008). In 2008, 1,108 motorcycle crashes were reported in Iowa, compared to 782 crashes that were reported in 2001, which represents a 42% increase. Note that from 2001 to 2008,

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motorcycle registrations increased from 120,961 to 162,662 (34%). The analysis presented herein focuses on two-vehicle crashes (one motorcycle collided with a non-motorcycle vehicle), which represented half of the total number of crashes that were reported during the study period. Table 1 shows the summary statistics for select variables for two-vehicle motorcycle crashes on public roads in Iowa.

Crash-specific factors The majority of two-vehicle motorcycle crashes during the analysis period resulted in an injury, with only 20.7% resulting in a PDO outcome, which underlines the importance of this study.

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Angle crashes (with oncoming vehicles) comprised of 18% of the two-vehicle motorcycle crashes and one-quarter of the two-vehicle crashes were rear-end crashes, with broadside crashes having the highest percentage. The two primary driver-contributing factors by non-motorcycle

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vehicles were “failing to yield right of way (ROW) to motorcycles when making left turn”, and “failing to yield ROW to motorcycles at intersections from the stop sign”. The three primary

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driver-contributing factors by motorcycles involved in two-vehicle crashes were “lost control”,

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“following too close”, and “exceeded authorized speed”. Motorcyclists who tend to ride too close to other vehicles and travel at higher speeds are more likely to be at fault in two-vehicle

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crashes (Schneider et al., 2012). For the majority of crashes, the vision of the motorcycle rider or non-motorcycle vehicle driver was not obscured by moving vehicles, trees, buildings, or other.

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Non-motorcycle vehicles were assumed at fault if the major driving contributing factor

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by motorcycles involved in two-vehicle crashes was identified as “no improper action”.

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Likewise, if the major driving contributing factor by non-motorcycle vehicles involved in twovehicle crashes was “no improper action”, it was assumed that the motorcycle was at fault. In

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more than half (56%) of two-vehicle crashes, the driver of the non-motorcycle vehicle was imputed at fault, while in a quarter of two-vehicle crashes motorcyclists were imputed at fault. This is consistent with previous work that stated that in multi-vehicle crashes motorcyclists are more likely to be victims than at fault (Haque et al., 2009). One-quarter of crashes involved a motorcycle and another vehicle moving straight, while one-third of crashes involved one vehicle turning left and the other going straight. The analysis of crashes where one vehicle was turning left and the other was going straight showed that in 91.4% of the cases, the motorcycle was going straight and the non-motorcycle vehicle was turning left. This is consistent with previous work (Olson, 1989; Pai et al., 2009) that found that

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in angle crashes, the ROW of a traveling-straight motorcycle is more likely to be violated by non-motorcycle vehicles. Further analysis also showed that 10% of these crashes occurred when a pickup, van, minivan, or SUV driver was turning left.

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Roadway and environmental conditions

More than two-third of the two-vehicle motorcycle crashes reported were under clear weather (as

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clear conditions encourage motorcycle riding), and one-quarter were reported under cloudy or

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partly cloudy conditions. 92.5% of the crashes occurred under dry roadway surface conditions. Approximately 80% of the crashes occurred in daylight, while almost one fifth of the crashes

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occurred under dark conditions. These findings are likely attributed to the higher exposure of motorcycles in daylight compared to nighttime and the greater associated crash risk.

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Location and time

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Most two-vehicle crashes occurred on urban roads (81%). The majority of two-vehicle crashes

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occurred on roads with low speed limit (25 mph-35 mph). Further analysis showed that 50% of the fatal two-vehicle motorcycle crashes occurred on high-speed roads (of 55 mph-speed limit or

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higher). More than half of the two-vehicle crashes occurred on an intersection (of which 67% occurred on four-way intersections and 20% on T-intersections). The non-motorcycle vehicle was reported as the major driving contributing factor in the majority of the non-intersection and intersection crashes. This is in line with a recent study (Schneider et al., 2012), which found that motorcyclists were less likely to be at fault when the crashes occurred at intersections. As expected, the majority of crashes involving motorcycles occurred between May and September, with a higher number occurring in June and July. Turning to the distribution of crashes by day of week, two-vehicle crashes involving motorcycles were more likely to occur on a weekend, which suggests that more recreational trips than work trips were made by

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motorcycles. The temporal distribution of crashes during a day showed an increasing trend of two-vehicle crashes occurring from 1 p.m. to 12 a.m. and a decreasing trend thereafter. Motorcycle rider and non-motorcycle driver attributes

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The majority of the motorcycle riders (92%) involved in the crashes were male, while 54% of the drivers of the non-motorcycle vehicles were male. Half of the two-vehicle crashes involved

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motorcycle riders between 21 and 30 years old or between 41 and 50 years old. The distribution

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of crashes by the age of the non-motorcycle vehicle’s driver showed that a high percentage of older drivers (over 60 years old), followed by young drivers (under 30 years old) were involved

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in a crash with the motorcycle. Only 2.5% of the non-motorcycle drivers and 0.8% of the motorcycle riders were under the influence of alcohol, drug, or medication. Helmet use rates of

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motorcycle riders involved in two-vehicle crashes during the analysis period were very low

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(15%), probably because there is no mandatory helmet law in Iowa.

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Motorcycle and non-motorcycle vehicle attributes Passenger cars comprised of 57% of the non-motorcycle vehicles involved in the two-vehicle

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crashes, almost 14% were pick-up trucks, while sports utility vehicles (SUVs), vans or minivans accounted for almost 17% of the non-motorcycle vehicles. 75% of the non-motorcycle vehicles were eight years or older, and 46% of the motorcycles involved in the crashes were built within eight years of the study.

4. Methodology In selecting our methodology, we noted that conspicuity-related factors, such as rider clothing, color of motorcycle, helmet color, daytime running lights, motorcycle type and other factors could not be collected from the crash database and thus were not considered in this paper.

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This may introduce unobserved heterogeneity when developing crash severity models as a function of potential motorcycle conspicuity factors (among other factors), and may cause the estimated parameters to be biased and inefficient. This issue can be addressed by allowing the

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effect of the parameters to vary across observations through the estimation of random-parameter (mixed logit) models. In addition, mixed logit models do not suffer from the independence of

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irrelevant alternatives (IIA) problem (a restrictive property of MNL models) (Washington et al.,

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2011). In view of the above, a mixed logit model was estimated that allows for the possibility that the influence of potential conspicuity-related variables affecting crash severity may vary

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across the observations.

The application of the random parameter (mixed) logit model is undertaken by

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considering crash severities (based on crash data). Crash severity was reported as one of five

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categories, property damage only (PDO), possible or unknown injury, minor (non-incapacitating)

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injury, major (incapacitating) injury, and fatality. In our mixed logit analysis, the fatal injury crashes and major injury crashes were combined into one category named “fatal or major injury”

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crash as the number of fatal injury crashes was low. Following Gkritza and Mannering (2008), Milton et al. (2008) and Moore et al. (2011), a crash severity function determining crash severity outcome is defined as:

(1)

where Win is a function determining the crash severity category i for crash n, Xin is a vector of measurable characteristics (such as crash/roadway/environment/driver/vehicle-specific factors) that determine the injury outcome for crash n, βi is a vector of estimable coefficients, and εin is the error term which is assumed to be generalized extreme value distributed (McFadden, 1981). The factors considered in the mixed logit estimation were presented in Table 1. For modeling

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purposes, most of these factors were considered as indicator variables, taking the value of one or zero. McFadden (1981) has shown that if εin are assumed to be extreme value distributed, then

(2)

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a standard multinomial model results:

where Pn(i) is the probability that crash n will result in crash severity outcome i, and I is the set

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of possible crash severity outcomes. The mixed logit model is a generalization of the

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multinomial model structure which allows the parameter vector βi to vary across the observations. This model formulation allows for the heterogeneity within the observed crash

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dataset by varying the elements of βi. The outcome specific constants and the elements of βi can be either fixed or randomly distributed over all parameters with fixed means. Thus, a mixing

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distribution is introduced giving crash severity outcome probabilities (Train, 2003): (3)

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where f (βi|φ) is the density function of βi and φ is a vector of parameters describing the density function (mean and variance) with all other terms as previously defined (Milton et al., 2008). For this mixed logit model estimation, βi can now account for unobserved heterogeneity of the effect of X on crash severity outcome probabilities, with the density function f (βi|φ) used to determine βi. Mixed logit probabilities are then a weighted average for different values of βi across crashes where some elements of the vector βi may be fixed and some may be randomly distributed. The mixed logit weights are determined by the density function f (βi|φ) if the parameters are random. When all parameters are fixed, the model reduces to the standard multinomial logit formulation. Estimation of the mixed logit model shown in Eq. (3) can be undertaken using simulated maximum likelihood approaches, in which the logit probabilities are approximated by drawing 12

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values of βi from f (βi|φ) for given values of φ. Past research (Bhat, 2003; Gkritza and Mannering, 2008, Anastasopoulos and Mannering, 2009) suggest that using 200 Halton draws is usually sufficient for accurate parameter estimation (this number was used in the model

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estimation). For the functional form of the parameter density functions, consideration is given to normal, lognormal, triangular and uniform distributions. The prevailing literature conclude that

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normal distribution generally have the best fit for crash-injury severity data (Gkritza and

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Mannering, 2008; Milton et al., 2008; Moore et al., 2011; Pai et al., 2009). With the functional forms of the parameter density functions specified, values of βi are drawn from f(βi|φ), logit

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probabilities are calculated and the likelihood function is maximized.

Elasticities can be computed to assess the effect of individual parameter estimates on the

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crash severity outcome probabilities, as follows (Morgan and Mannering, 2011; Washington et

(4)

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al., 2011):

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where P(i|φ) is the probability of crash severity outcome i and xk is the value of variable k. Elasticity values can be roughly interpreted as the percent effect that a 1% change in xki has on the crash severity outcome probability P(i|φ). Elasticities are not applicable to indicator variables (those variables taking on values of 1 or 0). In these cases, a pseudo-elasticity can be calculated that represents the percent change in the probability of that crash severity category when the variable is changed from zero to one. Indicator variables that have an average pseudo-elasticity larger than 100% are elastic and have important effects. Finally, cross-elasticities can determine the effect that a variable influencing the probability of crash severity outcome j (for example, major injury) may have on the probability of crash severity outcome i (for example, possible injury).

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5. Estimation Results Table 2 shows the results of the mixed logit estimation of crash severity outcomes in twovehicle crashes involving a motorcycle in Iowa, and Table 3 shows the corresponding direct and

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cross-elasticities. All the parameters included in the model are statistically significant at a 0.10 significance level or higher. Parameters producing statistically significant standard errors for

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their assumed distribution were found to be random. When the estimated standard errors were

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not statistically different from zero, the parameters were fixed to be constant across the observations. The parameters found to be random were: the indicator variable for crash type

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(rear-end) for minor injury crashes, the indicator variables for crash location (urban or rural), roadway speed limit greater or equal to 55 mph, and riding season (June, July, or August) for

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possible or unknown injury crashes; and the indicator variable for motorcycle rider’s gender for

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parameters.

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PDO crashes. Normal distribution appeared to provide the best statistical fit for these random

The significant variables for each severity outcome are discussed next and are

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categorized in crash-specific factors, roadway and environmental conditions, location and time, motorcycle rider and non-motorcycle driver attributes, and last, motorcycle and non-motorcycle vehicle attributes.

Crash-specific factors

Regarding the effect of the manner of crash/collision, the parameter estimate of the indicator variable for angle crash was significant in the fatal or major injury function with a fixed parameter. Crashes that occurred when a non-motorcycle vehicle was turning left and a motorcycle was moving straight were more likely (by 2.5%) to result in a fatal or major injury outcome. The indicator variable for rear-end crash was found to be significant and random across

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minor injury crashes but fixed for PDO crashes. Rear-end crashes were more likely to result in a PDO crash outcome, but for minor injury crashes, this parameter is normally distributed with a mean 0.41 and standard deviation 1.54. This means that for the majority (60.5%) of the two-

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vehicle motorcycle crashes during the analysis period, a rear-end collision increased the likelihood of a minor injury outcome, while for 39.5% of the crashes a rear-end collision

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deceased the likelihood of a minor injury outcome. This random effect is likely picking up a

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complex interaction among vehicle and driver actions that led to a rear-end collision (for example, whether a motorcycle hit the other vehicle on the rear or vice versa, and the

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corresponding motorcycle driver and rider attributes).

The variable representing the interaction of the non-motorcycle vehicle action (turning

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left) and vehicle configuration (pick-up, van, mini-van, or SUV) had a significant uniform effect

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on the PDO crash severity function. The results suggest that a crash involving a left-turning pick-

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up, van or mini-van, SUV and a motorcycle is less likely (by 3%) to result in a PDO outcome. This finding corroborates previous research finding that collisions of motorcycles with heavier

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and larger vehicles are less likely to result in a non-incapacitating injury outcome (Savolainen and Mannering, 2007). Similarly, the indicator variables for non-motorcycle drivers failing to yield ROW at stop sign and failing to yield ROW to motorcycles while turning left are found to decrease the likelihood of a PDO crash by 2.8% and 6.3%, respectively. These findings emphasize the importance of improving driver awareness of motorcycles at stop signs and while turning left. We also examined whether obscured vision was associated to crash severity outcomes. It has been established in the literature that blockages such as a larger automobile nearby or a natural obstruction may cause drivers not to see an oncoming motorcycle or see it in time to

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avoid a crash (Williams and Hoffman, 1979; Hurt et al., 1981). Interestingly, the estimation results show that crashes where it was reported that the non-motorcycle vehicle driver’s vision was not obscured by trees, moving vehicles, buildings or others were more likely (by 22.22%) to

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result in a fatal or major injury crash, and crashes where it was reported that the motorcycle rider’s vision was not obscured by trees, moving vehicles, buildings or others were more likely

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(by 27.4%) to result in a minor injury. These findings seems to suggest that human-factor related

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elements such as driver inattention to the road (due to the lack of vision obstructions) rather than limited sight distance and potentially conspicuity are more likely to be associated with severe

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injury outcomes. Roadway and environmental conditions

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Crashes on roads with speed limit over 55 mph were found to be more likely to result in an

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injury crash outcome and less likely to result in a PDO outcome. This is in line with previous

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research (Rifaat et al., 2012; Savolainen and Mannering, 2007). The parameter for the speed limit over 55 mph variable was fixed for fatal or major injury and minor injury crash outcomes,

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and normally distributed with a mean -4.85 and standard deviation 7.27 for possible or unknown injury crashes. This means that the parameter is less than zero for 74.7% of the two-vehicle motorcycle crashes and greater than zero for 25.3% of the crashes, which further implies that the majority of two-vehicle motorcycle crashes on roads with speed limit greater than 55 mph are less likely to result in a possible injury crash. The unobserved heterogeneity may include important variables, such as roadway geometrics, drivers’ speed or the difficulty to see motorcyclists riding at a higher speed that were not accounted for in the model. Dry roadway surface was a significant indicator for fatal or major injury crash and minor injury crash severity. The likelihood of both these crash severity outcomes increases on dry

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roadway surface condition, possibly because of risk compensating behavior (such as speeding) of motorcyclists and/or other drivers operating their vehicles on a dry roadway surface. The net effect of the variable (shown in Table 3) suggests a larger increase of the likelihood of a fatal or

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major injury crash than a minor injury crash. The fixed parameter for the light condition suggests that fatal or major injury crashes are less likely (by 23.4%) to occur during daylight. Previous

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research (Pai et al., 2009) has established that riding during evening/nighttime hours and under

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diminished street light conditions is a safety concern for motorcycles, and further, that ROW violation by other drivers in multi-vehicle motorcycle crashes is associated with diminished

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lighting conditions. Location and time

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Urban locations were more likely to be associated with minor injury crashes. However, this

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variable was found to have a random parameter in the possible injury crash severity function

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with a mean -6.58 and standard deviation 12.92. This implies that the parameter is less than zero for 69.5% of the crashes, while it is greater than zero for 30.5% of the crashes, suggesting that

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the majority of the two-vehicle motorcycle crashes on urban locations are less likely to result in a possible injury outcome, while almost one third of the crashes have increased likelihood to result in a possible injury outcome. The net effect of the variable (shown in Table 3) is positive for the interior injury categories (minor and possible/unknown injury) and negative for the fatal/ major or PDO crash outcomes. The unobserved heterogeneity may include important variables, such as roadway geometrics, motorcycle rider’s speed or the difficulty to identify the presence of a motorcycle in an urban environment (possibly due to low contract with the background environment) that were not accounted for in the model. For example, Brenac et al. (2006) reported that a motorcyclist’s speed is significantly higher for motorcycle-related crashes in

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urban areas than others; Gershon et al. (2012) found that the average reaction time to identify the presence of a motorcycle is shorter in rural environments; and Shaheed et al. (2012) found that motorcycles in urban environments were detected at a greater distance compared to those in rural

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environments. Previous studies have also provided evidence of lower helmet use rates on city roads compared to county roads or highways (Gkritza, 2009; Li et al., 2008; Skalkidou et al.,

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1999).

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Riding season (June, July or August) is fixed indicator for the minor injury outcome but has non-uniform effects for the possible injury outcome. The random parameter for the possible

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injury outcome is normally distributed with a mean -1.82 and standard deviation 2.72. This means that the parameter is less than zero for 74.8% of the crashes while it is greater than zero

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for 25.2% of the crashes. The elasticity results in Table 3 suggest that the majority of the crashes

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that occurred in June, July or August are less likely to result in a possible injury crash and more

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likely to result in a minor injury outcome. The unobserved heterogeneity may include important conspicuity-related variables, such as helmet use and clothing. For example, lower helmet use

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rates have been observed on sunny and likely, warm days (that are more frequent in the summer months) than those on cloudy or rainy days (Gkritza, 2009). Past research has attributed this to thermal discomfort and/or unfavorable temperature perceptions that might make riders find a helmet less tolerable on warm days (Li et al., 2008; Patel and Mohan, 1993; Skalkidou et al., 1999). Moreover, motorcyclist protective clothing is thermally insulating which potentially constitutes a thermal discomfort in warm weather (Bogerd et al., 2011). Motorcycle rider and non-motorcycle driver attributes The parameter of the indicator variable for motorcyclists’ gender is random and significant in the PDO crash severity function. It is normally distributed with a mean -2.3 and standard deviation

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2.91, rendering a 78.5% of the distribution less than zero and 21.5% of the distribution greater than zero. The majority of the two-vehicle motorcycle crashes involving a male motorcycle rider are less likely to result in a PDO crash (and more likely to result in an injury), while around one

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fifth of the crashes are more likely to result in a no-injury outcome crash. The elasticity results in Table 3 shows a higher likelihood of a severe crash involving a male rider than a less severe

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crash outcome. This random effect across the male driver population is likely picking up a

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complex interaction among various human-factor related or physiological elements (such as perception/reaction times, experience, attention to the road, visual acuity, weight, height, and

an

other) and the physics of the collision.

The variable indicating helmet use by motorcyclists was found to be significant with a

M

fixed effect for fatal or major injury outcome crashes. Helmet use would decrease the probability

d

of fatal or major injuries in two-vehicle collisions involving a motorcycle by 3.5%. This is in line

te

with past research (Savolainen and Mannering, 2007) that found that helmet use in right-angle collisions (involving a motorcycle) decreased the likelihood of a fatal injury outcome. This

Ac ce p

finding provides evidence of the effectiveness of using a helmet for preventing severe injury outcomes. Additional information on whether the helmet use that was reported complied with the federal safety standards could help evaluate this finding. Motorcycle and non-motorcycle vehicle attributes The parameter for the indicator variable for collision of motorcycles with passenger cars was found to be statistically significant with a fixed parameter for fatal or major injury crash outcomes. The likelihood of a fatal or major injury crash is lower (by 10.7%) in a two-vehicle motorcycle crash involving a passenger car than larger-sized vehicles. Previous research also found that the likelihood of a fatal or major injury outcome increases in multi-vehicle motorcycle

19

Page 19 of 35

crashes involving larger-sized vehicles such as pick-up trucks or tractor-trailers (Savolainen and Mannering, 2007). We also found that the indicator variable for collision with a pick-up truck is associated with a lower likelihood of a no-injury outcome (by 4.7%). Crashes involving newer

ip t

non-motorcycle vehicles (model year above 2000) are less likely to result in a fatal or major injury and PDO crash severity outcome. This could be attributed to the enhanced safety features

cr

in the newer vehicles, such as airbags, that can reduce the risk of a major injury or fatality but

us

can result in a minor injury. Past research has also suggested that offset behavior (such as speeding) occurs that counters the benefits of owning a vehicle equipped with airbags and

M

6. Conclusions

an

antilock brakes (Winston et al., 2006).

d

Using data for motorcycle crashes on public roads in Iowa from 2001 to 2008, this paper

te

identified the factors that influence the outcome of a collision between a motorcycle and another vehicle. Factors that were examined included: crash-specific factors such as manner of collision,

Ac ce p

driver contributing factors and vehicle action; roadway and environmental conditions such as speed limit, road surface and light conditions; location and time such as type of area, riding season; motorcycle rider and non-motorcycle driver attributes such as gender, age, helmet use; and motorcycle and non-motorcycle vehicle attributes such as vehicle type and model year. The mixed logit analysis showed non-uniform effects of rear-end collisions on minor injury crashes, as well as of the roadway speed limit over 55 mph, the type of area (urban), the riding season (summer), and motorcyclist’s gender on low severity crashes. These findings suggest a complex interaction among the roadway, vehicle and driver actions and attributes that can lead to a collision between a motorcycle and another vehicle. The estimation results also

20

Page 20 of 35

showed the effect of a combination of motorcycle and non-motorcycle vehicle actions on injury severity outcomes, such as turning left or moving straight. Public education on the consequences of failing to yield right of way to motorcyclists at stop sign or while turning left as well as

ip t

enforcement might improve motorcycle safety and reduce the severity of two-vehicle crashes involving a motorcycle.

cr

Results from the mixed logit model also underscore the importance of enforcing the

us

speed limit on high-speed roads. Riders should be also encouraged to wear helmets since helmet use can reduce the likelihood of a fatal crash and can improve conspicuity depending on the

an

surroundings (Gershon et al., 2012). This is particularly important in the summer months where the motorcyclists have a higher risk of injury crashes; motorcycle helmet ventilation systems

M

could reduce the thermal discomfort in warm weather (Bogerd et al., 2011). Moreover, the

d

elasticity analysis showed that two-vehicle motorcycle crashes that occurred on dry roadway

te

surface conditions were more likely to result in a fatal or major injury outcome than lower injury severity outcomes, possibly due to risk compensating behavior. Similarly, clear vision (not

Ac ce p

obscured by moving vehicles, trees, buildings or other) was found more likely to be associated with severe injury outcomes, possibly because of the driver inattention to the road or other behavioral factors. Furthermore, we confirmed that riding during daylight decreases the probability of severe injury crashes.

However, the crash data used in this study are limited; potential conspicuity-related factors such as rider clothing, color of motorcycle, helmet color, and motorcycle type were not reported. While the adopted methodology addresses the problem of heterogeneity, additional and/or improved data collection is recommended. For example, while there is information on the speed limit on the roads where the motorcycle and the other vehicle involved in a collision with a

21

Page 21 of 35

motorcycle were traveling, this information is likely to be imprecise as a surrogate of the motorcycle and the non-motorcycle vehicle’s speeds. Obtaining speed information would be useful in understanding the dynamics of the motorcycle-vehicle interaction. In addition, accurate

ip t

information on vehicle-miles traveled by motorcycles that would be essential in a comparison of exposure during daylight and night is missing or, when available, is of poor quality (Bigham et

cr

al., 2009). Naturalistic driving and driving simulator studies that would allow for such

us

information to be collected are promising avenues for future research on motorcycle conspicuity

an

and safety.

Acknowledgments

M

The authors would like to acknowledge Inya Nlenanya with the Institute for Transportation for

d

his assistance with data integration. The contents of this paper reflect the views of the authors,

Ac ce p

References

te

who are responsible for the facts and the accuracy of the data presented herein.

Baer, J., and Skemer, M. 2009. “Review of State Motorcycle Safety Program Technical Assessments”, DOT HS 811 082, American Institute for Research. Prepared for National Highway Traffic Safety Administration, Washington, D.C. Baer, J., Ayotte, K., and Baldi, S. 2010. Evaluation of state motorcycle safety programs. Report No. DOT HS 811 269. U.S. Department of Transportation and National Highway Traffic Safety Administration. Bhat, C., 2003. Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model. Transportation Research Part B 37(1), 837–855.

22

Page 22 of 35

Bigham, J., Rice, T. M. and Ragland, D.R., 2009. A Critical Review of Motorcycle Safety Countermeasure Studies and Recommendations for Future Research in the United States. Proceedings of the 137th American Public Health Association Conference. Philadelphia, PA. Bogerd, C.P., Rossi, R.M., Brühwiler, P.A., 2011. Thermal Perception of Ventilation Changes in

ip t

Full-Face Motorcycle Helmets: Subject and Manikin Study. The Annals of Occupational Hygiene 55(2), 192–201.

cr

Brenac, T., Clabaux, N., Perrin, C., and Van Elslande, P., 2006. Motorcycle conspicuity-related accidents in urban areas: A speed problem? Advances in Transportation Studies 8, 23–29.

us

Buonarosa, M. L., and Sayer, J.R., 2007. Seasonal variations in conspicuity of high-visibility garments. The University of Michigan, Transportation Research Institute, UMTRI-2007-42,

an

Ann Arbor, Michigan.

Cercarelli, L. R., Arnold, P. K., Rosman, D. L., Sleet, D., and Thornett, M. L., 1992. Travel exposure and choice of comparison crashes for examining motorcycle conspicuity by

M

analysis of crash data. Accident Analysis and Prevention 24(4), 363–368. de Rome, L., 2006. The injury reduction benefits of motorcycle protective clothing. National

d

Transportation Safety Board, Motorcycle Safety Forum, September 12-13, Sydney, Australia. de Rome, L., Ivers, R., Du, W., Haworth, N., Heriter, S., and Richardson, D., 2011. Motorcycle

te

protective clothing: Protection from injury or just the weather? Accident Analysis and

Ac ce p

Prevention 43(6), 1893–2000.

Elvik, R., 1993. The effects on accidents of compulsory use of daytime running lights for cars in Norway. Accident Analysis and Prevention 25(4), 383–398. Gershon, P., Ben-Asher, N., and Shinar, D., 2012. Attention and search conspicuity of motorcycle as a function of their visual context. Accident Analysis and Prevention 44(1), 97– 103.

Gkritza, K., and Mannering, F., 2008. Mixed logit analysis of safety-belt use in single- and multi-occupant vehicles. Accident Analysis and Prevention 40(2), 443-451. Gkritza, K. 2009. Modeling motorcycle helmet use in Iowa: evidence from six roadside observational surveys. Accident Analysis and Prevention 41(3), 479–484. Haque, M.M., Chin, H.C., and Huang, H, 2009. Modeling fault among motorcyclists involved in crashes. Accident Analysis and Prevention 41(2), 327–335.

23

Page 23 of 35

Haque, M.M., Chin H.C., Debnath, A.K. 2012. An investigation on multi-vehicle motorcycle crashes using log-linear models. Safety Science 50(2), 352–362. Hole, G. J., Tyrrella, L., and Langhama, M., 1996. Some factors affecting motorcyclists' conspicuity. Ergonomics 39(7), 946–965.

ip t

Hurt, H.H., Ouellet, J.V., and Thom, D.R., 1981. Motorcycle accident cause factors and identification of countermeasures, Volume I: Technical Report. Report No. DOT HS-5-

cr

01160. US Department of Transportation, National Highway Traffic Safety Administration, Washington, D.C.

us

Jenkins, S.E., and Wigan, M.R., 1985. The applicability of a motorcycle headlamp modulator as a device for enhancing daytime conspicuity. Transportation Research Record 1047, 88–92.

an

Li, L.P., Li, G.L., Cai, Q.E., Zhang, A.L., Lo. S.K., 2008, Improper motorcycle helmet use in provincial areas of a developing country. Accident Analysis and Prevention 40(6), 1937– 1942.

M

McFadden, D., 1981. Econometric Models of Probabilistic Choice. In: Manski, C, McFadden, D. (Eds.), A Structural Analysis of Discrete Data with Econometric Applications. The MIT

d

Press, Cambridge, MA.

Milton, J. C., Shankar, V. N., and Mannering, F., 2008. Highway accident severities and the

Ac ce p

40(1), 260–266.

te

mixed logit model: An exploratory empirical analysis. Accident Analysis and Prevention

Moore, D. N., Schneider IV, W. H., Savolainen, P. T., and Farzaneh, M., 2011. Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations. Accident Analysis and Prevention 43(3), 621–630. Morgan, A., Mannering, F.L. 2011 The effects of road-surface conditions, age, and gender on driver-injury severities, Accident Analysis and Prevention 43(5), 1852–1863. Muller, A., 1984. Daytime headlight operation and motorcyclist fatalities. Accident Analysis and Prevention 16(1), 1–18.

Olson, P.L., 1989. Motorcycle conspicuity revisited. Human Factors 31(2), 141–146. Olson, P.L., Halstead-Nussloch, R., and Sivak, M, 1979. Development and Testing of Techniques for Increasing the Conspicuity of Motorcycles and Motorcycle Drivers. Report No. DOT HS 805 143. Prepared for National Highway Traffic Safety Administration. Highway Safety Research Institute, University of Michigan, Ann Arbor, Michigan.

24

Page 24 of 35

Olson, P.L., Halstead-Nussloch, R., and Sivak, M., 1981. The effect of improvements in motorcycle/motorcyclist conspicuity on driver behavior. Human Factors: The Journal of the Human Factors and Ergonomics Society 23(2), 237–248. Pai, C. W., Hwang, K. P., and Saleh, W., 2009. A mixed logit analysis of motorists’ right-of-way

ip t

violation in motorcycle accidents at priority T-junctions. Accident Analysis and Prevention 41(3), 565–573.

cr

Patel, R., Mohan D., 1993. An improved motorcycle helmet design for tropical climates. Applied Ergonomics 24(6), 427–431.

lights.

Federation

of

European

Motorcyclists'

Associations,

http://www.fema.rider

an

srights.org/docs/DL3331.PDF

us

Perlot, A., and Prower, S., 2003. Review of the evidence for motorcycle and motorcar daytime

Quddus, M., Noland, R., and Chin, H., 2002. An analysis of motorcycle injury and vehicle damage severity using ordered probit models. Journal of Safety Research 33(4), 445–462.

Analysis and Prevention 49(1), 44–49.

M

Rifaat, S.M., Tay, R., de Barros, A., 2012. Severity of motorcycle crashes in Calgary, Accident

and Prevention 12(2), 151–157.

d

Rumar, K., 1980. Running lights-conspicuity, glare and accident reduction. Accident Analysis

te

Savolainen, P. T., and Mannering, F., 2007. Probabilistic models of motorcyclists’ injury

Ac ce p

severities in single- and multi-vehicle crashes. Accident Analysis and Prevention 39(5), 955– 963.

Savolainen, P., Mannering, F., Lord, D., Quddus, M., 2011. The statistical analysis of crashinjury severities: a review and assessment of methodological alternatives. Accident Analysis and Prevention 43(5), 1666–1676. Schneider, W. H., Savolainen, P. T., Boxel, D. V., and Beverley, R., 2012. Examination of factors determining fault in two-vehicle motorcycle crashes. Accident Analysis and Prevention 45(1), 669–676.

Shaheed, M., Gkritza, K., and Marshall, D., 2012. Motorcycle conspicuity – What factors have they greatest impact. Final report prepared for the Iowa Department of Transportation and the Midwest Transportation Consortium, MTC Project 2011-01.

http://www.intrans.

iastate.edu/research/documents/research-reports/motorcycle_conspicuity_ii_w_cvr.pdf

25

Page 25 of 35

Shankar, V., and Mannering, F., 1996. An exploratory multinomial logit analysis of singlevehicle motorcycle accident severity. Journal of Safety Research 27(3), 183–194. Skalkidou, A., Petridou, E., Papadopoulos, F.C., Dessypris, N., Trichopoulos, D., 1999. Factors affecting motorcycle helmet use in the population of greater Athens, Greece. Injury

ip t

Prevention 5(4), 264–267.

Thomson, G. A., 1980. The role frontal motorcycle conspicuity has in road accidents. Accident

cr

Analysis and Prevention 12(3), 165–178.

Train, K., 2003. Discrete Choice Methods with Simulations. Cambridge University Press,

us

Cambridge, U.K.

Torrez, L.I., 2008. Motorcycle Conspicuity: The Effects of Age and Vehicular Daytime Running

an

Lights. A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Psychology in the College of Sciences at The University of Central Florida, Orlando, Florida.

M

Washington, S. P., Karlaftis, M. G., and Mannering, F. L., 2011. Statistical and Econometric Methods for Transportation Data Analysis. 2nd Edition, Chapman and Hall/CRC.

d

Wells, S., Mullin, B., Norton, R., Langley, J., Connor, J., Lay-Yee, R., and Jackson, R., 2004.

Journal 328, 857–863.

te

Motorcycle rider conspicuity and crash related injury: case-control study. British Medical

Ac ce p

Williams, M. J., Hoffmann, E. R., 1979. Motorcycle conspicuity and traffic accidents. Accident Analysis and Prevention 11(3), 209–224. Winston, C., Maheshri, V., Mannering, F., 2006. An exploration of the offset hypothesis using disaggregate data: The case of airbags and antilock brakes. Journal of Risk and Uncertainty 32(2), 83-99.

Wulf, G., Hancock, P. A. and Rahimi, M., 1989. Motorcycle conspicuity: An evaluation and synthesis of influential factors. Journal of Safety Research 20(4), 153–176. Zador, P. L., 1985. Motorcycle headlight-use laws and fatal motorcycle crashes in the US, 197583. American Journal of Public Health 75(5), 543-546.

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Table 1. Summary statistics for select variables for two-vehicle motorcycle crashes

Mean or Percentage (Standard Deviation)

ip t

Variables Crash-specific variables

3,693

cr

Number of crashes

461.6 (46.1)

Crash severity Fatal/ major injury/minor injury/possible or unknown/ PDO

us

Average number of crashes/year

4.5/18.0/34.1/22.8/20.7

Ac ce p

Driver contributing factor

te

d

M

an

Manner of crash/collision Head-on/rear-end, non-MC hitting MC/rear-end, MC hitting non-MC/angle, non-MC turning left and MC moving straight/ angle, MC turning left and non-MC moving straight/ broadside/sideswipe, same direction/sideswipe, opposite direction/other

Vehicle action

Moving straight/turning left/turning right/other

3.8/11.7/13.1/10.7/2.3/37.8/6.8/ 2.1/11.7

Motorcycle: Lost control/ followed too closed / exceeded authorized speed 3.13/3.2/2.7 Non-motorcycle vehicle: FTYROW*: Making left turn/ FTYROW*: from stop sign 10.7/9.2 Motorcycle: 75.0/4.4/2.4/18.1 Non-motorcycle vehicle: 36.8/35.1/4.1/24.2

Vision obscured Not obscured/moving vehicles/other (trees, hillcrest, buildings, unknown, etc.)

Motorcycle: 87.0/0.2/12.8 Non-motorcycle vehicle:

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78.0/4.0/18.0

Roadway and environmental conditions

ip t

Motorcycle: Speed limit (mph)

2.7/69.2/11.9/16.0/0.2

Non-motorcycle vehicle:

cr

Under 25/ 25-35/40-50/55-65/ over 65

3.5/69.4/11.6/15.3/0.2

us

Weather conditions

72.1/18.4/5.5/4.0

Clear/partly cloudy/cloudy/other

an

Road surface conditions Dry/ wet, snow or slush/other

M

Light conditions

te

Location and time Rural/urban

78.9/3.2/0.5/12.8/3.1/ 0.4/1.1

d

Daylight/ dusk/ dawn/dark-roadway lighted/ dark-roadway not lighted/dark-unknown roadway lighting/other

92.5/1.0/6.5

Ac ce p

Non-intersection/intersection

19.0/81.0 42.4/57.6 11.0/10.7/11.5/12.2/13.7/

Year: 2001 to 2008

Month of year Jan/Feb/Mar/Apr/May/Jun /Jul/Aug/Sep/Oct/Nov/Dec

Day of week Mon/Tue/Wed/Thu/Fri/Sat/Sun

13.4/12.8/14.7 0.6/0.6/2.7/8.8/12.1/17.0/ 16.6/15.8/13.6/8.0/3.5/0.5 13.8/11.5/11.9/12.4/ 12.8/18.5/19.1

Time of day 4.3/4.2/5.6/10.4/25.0/22.7/27.9 0-7/7-9/9-11/11-13/13-16/16-18/18-24 Motorcycle rider and non-motorcycle driver attributes

28

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Gender of the motorcycle rider Male/Female

92.0/8.0

Gender of the driver of the non-motorcycle vehicle Male/Female

53.0/47.0

Age of the motorcycle rider

ip t

39.8 (17.3)

under 20/ 21 to 30/31 to 40/ 41 to 50/ 51 to 60/

11.2/23.1/19.1/23.7/ 14.7/8.2

Under the influence of alcohol, drug or medication

43.3 (24.0) 19.9/20.3/13.9/12.9/10.3/22.7

us

Age of the driver of the non-motorcycle vehicle under 20/ 21 to 30/31 to 40/ 41 to 50/ 51 to 60/ over 60 years old

cr

over 60 years old

0.8/2.5

Helmet use

85.0/15.0

M

Rider without helmet/ rider with helmet

an

Motorcycle rider/non-motorcycle vehicle driver

Motorcycle and non-motorcycle vehicle attributes

Ac ce p

Vehicle model year

57.0 /13.7/8.7/8.1/1.2/11.3

te

d

Vehicle type If the non-motorcycle vehicle was a car /light truck / SUV/ van or minivan / single-unit truck or trailer / other

If the non-motorcycle vehicle model year was above year 2000/below year 2000

25.0/75.0

Motorcycle model year

If the motorcycle model year was above year 2000/below year 2000 Vehicle type* vehicle action Non-motorcycle vehicle was a light truck, SUV, van or minivan and was turning left when collided with a MC

46.0/54.0

10.2

* Failed to yield right of way

29

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-2.745

Indicator variable for angle crash with non-MC turning left

0.357

Indicator variable for non-MC drivers’ vision not obscured

us

and MC moving straight, fixed parameter

-6.716

cr

Constant, fixed parameter

ip t

Table 2. Mixed logit model estimation results for two-vehicle motorcycle (MC) crash severity outcomes (Variables are defined for four injury outcomes: fatal or major, minor, possible or unknown, and property damage only) Parameter Variable t-statistic estimate Fatal or major injury crash 2.579

0.456

3.619

2.493

6.987

Indicator variable for dry surface, fixed parameter

2.066

5.481

Indicator variable for daylight condition, fixed parameter

-0.439

-3.745

Indicator variable for helmet use, fixed parameter

-0.293

-2.346

Indicator variable for passenger car, fixed parameter

-0.276

-2.862

Indicator variable for non-MC vehicle model year greater

-0.242

-2.262

-2.739

-6.650

0.408 (1.540)

2.749 (2.285)

0.698

4.639

1.456

4.064

Indicator variable for dry surface, fixed parameter

1.486

4.297

Indicator variable for urban location, fixed parameter

0.244

1.590

Indicator variable for riding month of June, July or

0.150

1.641

an

Indicator variable for MC speed limit greater or equal to

or equal to 2000

Ac ce p

Minor injury crash

te

d

M

55 mph, fixed parameter

Constant, fixed parameter

Indicator variable for rear end collision (standard error of parameter distribution)

Indicator variable for motorcyclists’ vision not obscured, fixed parameter

Indicator variable for MC speed limit greater or equal to 55 mph, fixed parameter

August, fixed parameter Possible or unknown injury crash

30

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Constant, fixed parameter

-0.787

-2.011

55 mph (standard error of parameter distribution)

-4.849 (7.270)

-1.461 (2.248)

Indicator variable for urban location (Standard error of

-6.58 (12.910)

-1.791 (2.265)

Indicator variable for MC speed limit greater or equal to

ip t

parameter distribution) Indicator variable for riding month of June, July or August

-1.824 (2.719)

-1.836 (1.824)

cr

(Standard error of parameter distribution)

-2.004

-2.861

us

Indicator variable for collision with pick-up, fixed parameter

an

Property damage only (PDO) crash

1.763

5.413

Indicator variable for non-MC driver failed to yield at stop

-0.654

-2.010

sign, fixed parameter

-1.083

-2.933

-2.306 (2.907)

-4.921 (4.281)

-0.594

-1.806

-0.242

2.262

M

Indicator variable for rear end collision

Indicator variable for non-MC driver failed to yield to MC

d

while turning left, fixed parameter

Ac ce p

parameter distribution)

te

Indicator variable for male motorcyclist (Standard error of

Indicator variable for pick-up, van or min-van, SUV turning left, fixed parameter

Indicator variable for non-MC vehicle model year greater or equal to 2000, fixed parameter

Number of observations

3,693

Initial log likelihood

-5,119.58

Log likelihood at convergence

-4,760.95

31

Page 31 of 35

ip t

Table 3. Estimated elasticity values of the mixed logit model for two-vehicle motorcycle (MC) crash injury severity outcomes, in percent (Variables are defined for four injury outcomes: fatal or major, minor, possible or unknown, and property damage only) Fatal or Possible or Property Minor major unknown damage Variable injury injury injury only injury Fatal or major injury crash Indicator variable for angle crash with 2.5% -1.6% -0.2% -0.9%

cr

non-MC turning left

parameter Indicator variable for non-MC

22.2%

Indicator variable for dry surface,

-21.6%

-4.0%

-13.3%

-59.2%

-8.6%

-31.7%

-23.4%

10.1%

1.5%

5.3%

condition, fixed parameter

-3.5%

1.5%

0.2%

0.8%

-10.7%

4.3%

0.5%

2.2%

-4.0%

1.7%

0.2%

0.9%

-2.4%

7.1%

-1.6%

-4.0%

-29.0%

27.4%

-3.6%

-13.9%

te

Indicator variable for daylight

123.6%

d

fixed parameter

Ac ce p

Indicator variable for helmet use,

-5.9%

18.9%

greater or equal to 55 mph, fixed parameter

-1.7%

M

Indicator variable for MC speed limit

-11.0%

an

drivers’ vision not obscured

us

and MC moving straight, fixed

fixed parameter

Indicator variable for passenger car, fixed parameter

Indicator variable for non-MC vehicle model year greater

or equal to 2000, fixed parameter Minor injury crash Indicator variable for rear end collision (random parameter with normal distribution)

Indicator variable for motorcyclists’

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vision not obscured, fixed parameter Indicator variable for MC speed limit

-8.4%

15.0%

-1.5%

-64.3%

64.7%

-8.0%

-10.2%

9.4%

-3.5%

3.4%

-4.8%

Indicator variable for dry surface, fixed parameter Indicator variable for urban location,

Indicator variable for riding month of

-0.7%

6.3%

-0.8%

-4.9%

-5.5%

21.6%

-7.3%

1.8%

1.7%

-6.0%

1.4%

0.8%

0.8%

-4.7%

0.6%

-10.8%

-7.9%

-2.2%

15.3%

Ac ce p

(random parameter

M

te

Indicator variable for urban location

d

normal distribution)

-0.7%

an

Possible or unknown injury crash

mph (random parameter with

-4.8%

-1.7%

fixed parameter

greater or equal to 55

-31.2%

-0.4%

June, July or August,

Indicator variable for MC speed limit

-0.9%

us

fixed parameter

cr

mph, fixed parameter

ip t

greater or equal to 55

with normal distribution)

Indicator variable for riding month of June, July or August

(random parameter with normal distribution)

Indicator variable for collision with pick-up, fixed parameter

Property damage only (PDO) crash Indicator variable for rear end collision, fixed parameter

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Indicator variable for non-MC driver

0.6%

0.6%

0.1%

-2.8%

0.9%

0.9%

0.2%

-6.3%

9.9%

5.5%

-0.9%

0.5%

0.5%

failed to yield at stop

Indicator variable for non-MC driver failed to yield to MC while turning left, fixed parameter

motorcyclist (random

Indicator variable for pick-up, van or

us

parameter with normal distribution)

turning left, fixed parameter 0.8%

Indicator variable for non-MC vehicle

an

min-van, SUV

0.7%

0.1%

-3.0%

0.2%

-2.6%

M

model year greater

-3.2%

cr

Indicator variable for male

ip t

sign, fixed parameter

Ac ce p

te

d

or equal to 2000, fixed parameter

34

Page 34 of 35

Highlights The factors affecting two-vehicle motorcycle crash severity outcomes are examined

A wide range of factors significantly influence crash severity outcomes

ip t

A random parameter model is used to account for the limited police-reported information on motorcycle conspicuity

cr

Roadway surface condition, clear vision, speed limit, light conditions and helmet use are among the key factors influencing severe injury crashes

Ac ce p

te

d

M

an

us

Five factors were found with non-uniform effects on minor and lower injury-severity outcomes

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