Improve Motorcycle Safety Research Articles

Examining Factors Contributing to Motorcycle Collisions with Left-Turning Vehicles at Urban Intersection Locations

Motorcycle crashes account for a significant proportion of traffic-related fatalities on U.S. roadways. Compared with motor vehicles, motorcycles traveling straight ahead are more susceptible to collisions with left-turning vehicles at intersections (note – in a system where traffic travels on the right-hand side of the road). The limited knowledge of the causes and influences of this specific type of crash deters efforts to improve motorcycle safety and is partly influenced by two issues. First, significant variables are unknown; second, motorcycles comprise a small proportion of vehicles in the traffic stream. This study sought to understand the factors that may contribute to the disproportionate crash risk left-turning vehicles pose for motorcyclists while accounting for the imbalance of vehicle proportions. Data containing motorcycle-motor vehicle and motor vehicle-motor vehicle crashes involving left-turning motor vehicles at intersections in South Florida were collected from 2015 to 2017. The study applied logistic regression on a balanced dataset generated using the random oversampling technique. The proposed model improved the predictive accuracy and enabled the identification of factors contributing to motorcycle crashes with left-turning vehicles. A Bayesian network analysis was also applied to the balanced data to analyze the interrelationship of factors associated with motorcycle crashes with left-turning vehicles. Results indicated that the type of intersection and traffic control, time of day, age of drivers, sex of the motorcyclist, roadway type, and weather were significantly associated with motorcyclists’ susceptibility to collisions with left-turning vehicles. Recognizing these attributes could help devise engineering measures and policies for promoting motorcycle safety.

Investigating the risk factors of motorcycle crash injury severity in Pakistan: Insights and policy recommendations

A large number of fatalities and severe injuries are caused by motorcycle crashes worldwide, particularly in developing countries. More than 50% of crashes in Pakistan involve motorcycles. To analyze motorcycle crash severity, various models, including both statistical and machine learning methods, have been applied. Researchers have widely acknowledged that machine learning methods provide superior prediction performance but have weaker interpretability power. However, no study has investigated the consistency of risk factors identified by the two streams of models. The consistency of the findings between these two kinds of methods is vital to improve the interpretability power of machine learning methods for policymaking in an era with more and more applications in the area of traffic safety. This study aims to narrow this research gap by comparing the consistency of crash severity risk factors identified by statistical models and machine learning methods. The study analyzes motorcycle crashes in Rawalpindi city of Pakistan. Multinomial logit model (MNL) and three machine learning models, i.e., the random forest (RF), naive Bayes, and gradient-boosted trees methods, are used to analyze the prediction performance and identify risk factors. The results show that the RF model, with an overall accuracy of 86.7%, outperformed other models. The SHapley Additive exPlanations (SHAP) method was adopted to explore the interpretability of machine learning methods. It was found that the contributing factors to crash injury severity identified by the RF method, such as distracted driving, collisions involving pedestrians, collisions involving a truck, and female riders, are consistent with those determined by the MNL model. These results have clear implications for developing cost-effective safety countermeasures to improve motorcycle safety in Pakistan.

A comparative study of factors associated with motorcycle crash severities under different causal scenarios

This study was carried out to examine the factors associated with motorcycle crash severity in Alabama, under different manner of crash and causal scenarios using mixed logit modeling. Three crash mechanisms were considered in this study: single-vehicle motorcycle crash with motorcyclist at fault, multi-vehicle collision between a motorcycle and another vehicle with motorcyclist being at fault, and motorcyclist not at fault in a collision between a motorcycle and another vehicle. The model estimation results showed that crashes that happened in rural areas were more likely to be severe, irrespective of the causal unit or manner of collision. The results also show that fatigue among motorcyclists was associated with severe injury, whereas driver fatigue was linked to no injury outcome. Further, it was found that risky behaviors such as speeding, driving/riding under the influence of alcohol or drugs, driving/riding with invalid license were significantly associated with severe injury outcome. Developing the injury-severity models based on the segmented crash data has helped to reveal some similarities and differences in crash outcomes based on the crash mechanism and the at-fault road user. It is expected that these findings would provide a data-driven evidence to improve motorcycle safety in the state.

An analysis of motorcyclists' injury severities in work-zone crashes with unobserved heterogeneity

Work-zone related crashes in Florida are increasing steadily in recent years. With such growing concern, work-zone is recognized in Florida Strategic Highway Safety Plan. The severity and complexity of motorcycle crashes in work-zones is critically important and worth investigating. However, the resulting effect of work-zone on motorcyclists' injuries in work-zone crashes is not fully understood. The purpose of study is to identify the contributing factors of motorcyclists' injury severity sustained in the work-zone crashes in Florida. Recognizing the relatively higher risk of motorcyclists in work-zones with respective to non-work-zones, this study further uncovers the contributing factors for single- and multi-vehicle motorcycle crashes in Florida work-zones. This study investigated motorcyclists' injury severity applying random parameter multinomial logit with possible heterogeneity in means and variances of the random parameters for single-motorcycle and multi-vehicle motorcycle crashes. This study utilizes the Crash Analysis Reporting (CAR) system in Florida over a period of five years from 2012 to 2016 (inclusive). The model result indicates a complex relationship between dark condition, old-aged motorcyclist (50–65), requirement and absence of endorsement, partial ejection, straight roadway segment, shoulder width (up to 1.22 m (4 ft), and 2.74–3.66 m (9–12 ft), urban interstate, activity area, and lane closure and work on shoulder-median work-zone types. The effect of work-zone on single-motorcycle crashes tends to have much more in resulting injury severities relative to multi-vehicle motorcycle crashes. It is more important to investigate the injury severity by single- and multi-vehicle crashes involving motorcycles in work-zones. These risk factors identified in the study are expected to provide more insights for the countermeasures specific to engineering (roadway design) and policy (motorcycle training), which can be considered to improve motorcycle safety in Florida.

Factors affecting motorcycle crash casualty severity at signalized and non-signalized intersections in Ghana: Insights from a data mining and binary logit regression approach

Despite the countless benefits derived from motorcycle usage, it has become a significant public health concern, particularly in developing countries, due to the plateauing number of fatal/serious injuries associated with them. Although it has been well documented that the frequency and fatality rates of intersection-related motorcycle crashes are high, little research efforts have been made to explore the contributory factors influencing motorcycle-involved crashes at these locations. Interestingly, no study has investigated the latent patterns and chains of factors that simultaneously contribute to the injury severity sustained by motorcycle crash casualties at intersections under different traffic control conditions in developing countries. Since motorcycles are mostly used as taxis in developing countries, it is imperative to consider the injury severity sustained by all crash casualties in the motorcycle safety analysis. This study bridges the research gap by employing a plausible data mining tool to explore hidden rules associated with motorcycle crash casualty injury severity outcomes at both signalized and non-signalized intersections in Ghana's most densely populated region, Accra, using three-year crash data spanning 2016–2018. Besides, a binary logit regression model was also employed to explore the impact of crash factors on casualty severity outcomes using the same dataset. The results from both analysis techniques were consistent; however, the data mining technique provided chains of factors which provided additional insights into the groups of factors that collectively influence the casualty injury severity outcomes. From the rule discovery results, while full license status, daytime/daylight, and shoulder presence increased the risk of fatal injuries at signalized intersections, factors such as inattentiveness, good road surface, nighttime, shoulder absence, and young rider were highly likely to increase casualty fatalities at non-signalized intersections. By controlling all or some of these risk factors, the level of injury severity on the roadways could be reduced. Based on the findings, we provide enforcement, education, and engineering-based recommendations to help improve motorcycle safety.

MOTORCYCLE HELMET USE AND FATALITIES IN THE SOUTHEAST REGION OF THE USA

Introduction: The federal government estimates that per mile traveled in 2018, the number of deaths on motorcycles was nearly 27 times the number in cars. In the United States there is no universal helmet law. In twenty-two states, motorcycle helmets are entirely optional, while in nineteen states and the District of Columbia universal motorcycle helmets laws requiring helmets for all riders regardless of age are implemented and nine states only require younger motorcycle riders to wear a helmet, with varying age limits. Objectives: The overall objectives of this study were to evaluate the following: (1) number of fatalities (with and without helmet use), (2) fatality rate per motorcycle registration, (3) fatality percentage with age, (4) percent fatality due to alcohol impairment, and (5) location of collision impact to the rider in two southern states (Mississippi and Alabama) where helmet laws are established compared with a southern state (Florida) that only requires riders less than 20 years of age to be helmeted. Methods: Data from 2015-2018 were obtained from the National Highway Transportation Safety Administration Reporting System (FARS) and supplemented with state related and CDC data. Results: In all three states, the most common collision was a front-end impact. Mississippi had the highest percentage of motorcycle fatalities even with >80% of riders helmeted when the fatal accident occurred, followed by Florida motorcyclist who are only 50% of the time helmeted. In all three southern states similar percentage of fatalities were seen in each age group with higher fatalities associated with age range of 30-39 years. Conclusions: Variables such as helmet type, distance from a level 1 trauma center, poor roads, weather conditions, and visibility of the rider may also be factors that contribute to a higher incidence of fatality and need to be further investigated to improve motorcycle safety.

Identifying Factors Contributing to the Motorcycle Crash Severity in Pakistan

Motorcycle is a popular mode of transportation in many developing countries, including Pakistan. Since the last decade, the registered number of motorcycles in Pakistan has increased by six times, constituting 74% of the total registered vehicles. However, limited research efforts have been made to investigate motorcycle-related safety issues in Pakistan. Thus, the relationship between potential risk factors and injury outcomes of motorcycle crashes is still unclear in the country. This study, therefore, established a random parameter logit model to examine the factors associated with the motorcycle injury severity. The analysis is based on two years (2014–2015) of data collected through the road traffic injuries surveillance system from Karachi city, Pakistan. The results indicate that the summer season, weekends, nighttime, elderly riders, heavy vehicle, and single-vehicle collisions are positively associated with fatalities, while the presence of pillion passengers and motorcycle-to-motorcycle crashes are negatively associated with fatalities. More importantly, in the specific context of Pakistan, morning hours, young riders, and female pillion passengers whose clothes stuck in the wheel significantly increase the fatal injury outcomes. Based on the findings, potential countermeasures to improve motorcycle safety are discussed, such as strict enforcement to control motorcyclists' risky behavior and speeding, provision of exclusive motorcycles lanes, and education of female pillion passengers. The findings from this study would increase awareness of motorcycle safety and can be used by the policymakers to enhance road safety in Pakistan, as well as in other developing countries with similar situations.

Hazard-based duration analysis of the time between motorcyclists’ initial training and their first crash

The high fatality rates of motorcyclists relative to other motorized forms of highway transportation has been a focus of engineers and safety policies for many years. One of the most popular approaches to improve motorcycle safety has been to design training courses to improve riding competence. However, the effectiveness of these courses and the initial crash risk of motorcyclists is not well understood. The current study seeks to provide insight into motorcyclists’ initial crash risk by following the crash history of newly trained motorcyclists for up to 5 years after they complete their required motorcycle training. Specifically, attention is focused on the time until motorcyclists’ first crash. Using data from over 40,000 Florida motorcyclists, Weibull hazard-based duration models are estimated with gamma heterogeneity, and full random parameters, to determine the factors that affect the duration until motorcyclists’ first crash. The estimation results show that age and gender are driving factors in determining this duration and ultimately crash risk. In addition, estimation results show that most gender-age groups have an initial period of increasing crash risk, ranging from 30 to 96 days, where the likelihood of a crash increases the longer motorcyclists go without having a crash. After this initial period, crash risk declines over time as increasing riding experience reduces overall risk. This particular finding points to a critical motorcycle-safety opportunity in that policies and/or training can be modified to attempt to eliminate this initial high-risk period to arrive at a desirable lower initial risk and a declining risk over time as experience makes for safer and more cautious riding.

Safety of child passengers who ride to school on a motorcycle: An observational study in two Argentine cities

In low- and middle-income countries, the motorcycle has increasingly become a means of family transportation due to its low cost and the poor quality of public transportation. Consequently, many child motorcycle passengers are at an elevated risk of serious injury in the event of a traffic crash. The available research on child safety devices is scarce and focused mainly on helmet use, while other devices (e.g. high-visibility clothing) have received little attention. This observational study documented the safety conditions and related factors of primary schoolchildren who rode to school as motorcycle passengers in two cities of Argentina.Data was obtained in the vicinity of 27 public primary schools in two cities of Argentina (one mid-sized city and the other a small city by the country's standards) between May 2018 and September 2019. A total of 1440 motorcycle occupants (656 drivers and 784 child passengers) were observed.The use of an adequate helmet was lower among children (vs. drivers). Most of the children were unable to reach the motorcycle's footrests, did not use high-visibility clothing or a fastening device, carried a backpack, and mounted and dismounted the motorcycle in an unsafe place. Some children rode in front of the driver or in a motorcycle with three or four occupants. Use of helmet by drivers and driver sex (woman) were related with an increase in safety in children. The indicators were worse in the larger city.The results revealed a low level of adoption of safety measures in children as well as in adults in both cities. This suggests that motorcycle travel is unsafe. Greater effort is needed to improve motorcycle safety, especially for children. It is important to debate regulatory details such as a minimum age for passengers, a limit on the number of passengers, and other safety criteria. It is also important to conduct educational campaigns that include the distribution of safety devices.

Motorcycle crashes potentially preventable by three crash avoidance technologies on passenger vehicles

ABSTRACTObjective: The objective of this study was to identify and quantify the motorcycle crash population that would be potential beneficiaries of 3 crash avoidance technologies recently available on passenger vehicles.Methods: Two-vehicle crashes between a motorcycle and a passenger vehicle that occurred in the United States during 2011–2015 were classified by type, with consideration of the functionality of 3 classes of passenger vehicle crash avoidance technologies: frontal crash prevention, lane maintenance, and blind spot detection. Results were expressed as the percentage of crashes potentially preventable by each type of technology, based on all known types of 2-vehicle crashes and based on all crashes involving motorcycles.Results: Frontal crash prevention had the largest potential to prevent 2-vehicle motorcycle crashes with passenger vehicles. The 3 technologies in sum had the potential to prevent 10% of fatal 2-vehicle crashes and 23% of police-reported crashes. However, because 2-vehicle crashes with a passenger vehicle represent fewer than half of all motorcycle crashes, these technologies represent a potential to avoid 4% of all fatal motorcycle crashes and 10% of all police-reported motorcycle crashes.Discussion: Refining the ability of passenger vehicle crash avoidance systems to detect motorcycles represents an opportunity to improve motorcycle safety. Expanding the capabilities of these technologies represents an even greater opportunity. However, even fully realizing these opportunities can affect only a minority of motorcycle crashes and does not change the need for other motorcycle safety countermeasures such as helmets, universal helmet laws, and antilock braking systems.

Psychological models for development of motorcycle helmet use among students in Vietnam

A helmet can reduce head accident severity. The aim of this research study was to study the intention for helmet use of students who ride motorcycles in Vietnam, by Structural Equation Modeling (SEM). Questionnaires developed by several traffic psychology modules, including the Theory of Planned Behaviour (TPB), Traffic Locus of Control (T-LOC), and Health Belief Model (HBM), were distributed to students at Ton Thang University and University of Architecture, Ho Chi Minh City. SEM was used to explain helmet use behaviour. The results indicate that TPB, T-LOC and HBM could explain the variance in helmet use behaviour. However, TPB can explain behaviour (helmet use intention) better than T-LOC and HBM. The outcome of this study is useful for the agencies responsible to improve motorcycle safety.

Direct medical costs of motorcycle crashes in Ontario.

There is no reliable estimate of costs incurred by motorcycle crashes. Our objective was to calculate the direct costs of all publicly funded medical care provided to individuals after motorcycle crashes compared with automobile crashes. We conducted a population-based, matched cohort study of adults in Ontario who presented to hospital because of a motorcycle or automobile crash from 2007 through 2013. For each case, we identified 1 control absent a motor vehicle crash during the study period. Direct costs for each case and control were estimated in 2013 Canadian dollars from the payer perspective using methodology that links health care use to individuals over time. We calculated costs attributable to motorcycle and automobile crashes within 2 years using a difference-in-differences approach. We identified 26 831 patients injured in motorcycle crashes and 281 826 injured in automobile crashes. Mean costs attributable to motorcycle and automobile crashes were $5825 and $2995, respectively (p < 0.001). The rate of injury was triple for motorcycle crashes compared with automobile crashes (2194 injured annually/100 000 registered motorcycles v. 718 injured annually/100 000 registered automobiles; incidence rate ratio [IRR] 3.1, 95% confidence interval [CI] 2.8 to 3.3, p < 0.001). Severe injuries, defined as those with an Abbreviated Injury Scale ≥ 3, were 10 times greater (125 severe injuries annually/100 000 registered motorcycles v. 12 severe injuries annually/100 000 registered automobiles; IRR 10.4, 95% CI 8.3 to 13.1, p < 0.001). Considering both the attributable cost and higher rate of injury, we found that each registered motorcycle in Ontario costs the public health care system 6 times the amount of each registered automobile. Medical costs may provide an additional incentive to improve motorcycle safety.

Are automatic systems the future of motorcycle safety? A novel methodology to prioritize potential safety solutions based on their projected effectiveness

ABSTRACTObjective: Motorcycle riders are involved in significantly more crashes per kilometer driven than passenger car drivers. Nonetheless, the development and implementation of motorcycle safety systems lags far behind that of passenger cars. This research addresses the identification of the most effective motorcycle safety solutions in the context of different countries.Methods: A knowledge-based system of motorcycle safety (KBMS) was developed to assess the potential for various safety solutions to mitigate or avoid motorcycle crashes. First, a set of 26 common crash scenarios was identified from the analysis of multiple crash databases. Second, the relative effectiveness of 10 safety solutions was assessed for the 26 crash scenarios by a panel of experts. Third, relevant information about crashes was used to weigh the importance of each crash scenario in the region studied. The KBMS method was applied with an Italian database, with a total of more than 1 million motorcycle crashes in the period 2000–2012.Results: When applied to the Italian context, the KBMS suggested that automatic systems designed to compensate for riders' or drivers' errors of commission or omission are the potentially most effective safety solution. The KBMS method showed an effective way to compare the potential of various safety solutions, through a scored list with the expected effectiveness of each safety solution for the region to which the crash data belong. A comparison of our results with a previous study that attempted a systematic prioritization of safety systems for motorcycles (PISa project) showed an encouraging agreement.Conclusions: Current results revealed that automatic systems have the greatest potential to improve motorcycle safety. Accumulating and encoding expertise in crash analysis from a range of disciplines into a scalable and reusable analytical tool, as proposed with the use of KBMS, has the potential to guide research and development of effective safety systems. As the expert assessment of the crash scenarios is decoupled from the regional crash database, the expert assessment may be reutilized, thereby allowing rapid reanalysis when new crash data become available. In addition, the KBMS methodology has potential application to injury forecasting, driver/rider training strategies, and redesign of existing road infrastructure.

The Association of Gasoline Prices With Hospital Utilization and Costs for Motorcycle and Nonmotorcycle Motor Vehicle Injuries in the United States.

This study examined the association between gasoline prices and hospitalizations for motorcycle and nonmotorcycle motor vehicle crash (MVC) injuries. Data on inpatient hospitalizations were obtained from the 2001 to 2010 Nationwide Inpatient Sample. Panel feasible generalized least squares models were used to estimate the effects of monthly inflation-adjusted gasoline prices on hospitalization rates for MVC injuries and to predict the impact of increasing gasoline taxes. On the basis of the available data, a $1.00 increase in the gasoline tax was associated with an estimated 8348 fewer annual hospitalizations for nonmotorcycle MVC injuries, and reduced hospital costs by $143 million. However, the increase in the gasoline tax was also associated with an estimated 3574 more annual hospitalizations for motorcycle crash injuries, and extended hospital costs by $73 million. This analysis of some existing data suggest that the increased utilization and costs of hospitalization from motorcycle crash injuries associated with an increase in the price of gasoline are likely to substantially offset reductions in nonmotorcycle MVC injuries. A policy decision to increase the gasoline tax could improve traffic safety if the increased tax is paired with public health interventions to improve motorcycle safety.

Correlation between crash avoidance maneuvers and injury severity sustained by motorcyclists in single-vehicle crashes

ABSTRACTObjectives: In order to improve motorcycle safety, this article examines the correlation between crash avoidance maneuvers and injury severity sustained by motorcyclists, under multiple precrash conditions. Method: Ten-year crash data for single-vehicle motorcycle crashes from the General Estimates Systems (GES) were analyzed, using partial proportional odds models (i.e., generalized ordered logit models). Results: The modeling results show that “braking (no lock-up)” is associated with a higher probability of increased severity, whereas “braking (lock-up)” is associated with a higher probability of decreased severity, under all precrash conditions. “Steering” is associated with a higher probability of reduced injury severity when other vehicles are encroaching, whereas it is correlated with high injury severity under other conditions. “Braking and steering” is significantly associated with a higher probability of low severity under “animal encounter and object presence,” whereas it is surprisingly correlated with high injury severity when motorcycles are traveling off the edge of the road. The results also show that a large number of motorcyclists did not perform any crash avoidance maneuvers or conducted crash avoidance maneuvers that are significantly associated with high injury severity. Conclusions: In general, this study suggests that precrash maneuvers are an important factor associated with motorcyclists' injury severity. To improve motorcycle safety, training/educational programs should be considered to improve safety awareness and adjust driving habits of motorcyclists. Antilock brakes and such systems are also promising, because they could effectively prevent brake lock-up and assist motorcyclists in maneuvering during critical conditions. This study also provides valuable information for the design of motorcycle training curriculum.

Motorcycle safety among motorcycle taxi drivers and nonoccupational motorcyclists in developing countries: A case study of Maoming, South China

ABSTRACTObjective: An increasing number of motorcycle taxis have been involved in traffic crashes in many developing countries. This study examines the characteristics of both motorcycle taxi drivers and nonoccupational motorcyclists, investigates the risks they pose to road safety, and provides recommendations to minimize their risks.Methods: Based on the data collected from a questionnaire survey of 867 motorcycle taxi drivers and 2,029 nonoccupational motorcyclists in Maoming, South China, comparisons were made to analyze differences of personal attributes, attitudes toward road safety, and self-reported behavior of the 2 groups.Results: Results of the chi-square tests show that not only motorcycle taxi drivers but also nonoccupational motorcyclists in Maoming held poor attitudes toward road safety and both groups reported unsafe driving behavior. There is much room for improving local road safety education among all motorcyclists in Maoming. Yet, motorcycle taxi drivers were more likely to pose road safety risks than nonoccupational motorcyclists under some circumstances, such as speeding late at night or early in the morning, not requiring passengers to wear helmets, and running a red light. The results of the binary logistic regression model show that possessing a vehicle license for a motorcycle or not was the common significant predictor for unsafe driving behavior of motorcycle taxi drivers and nonoccupational motorcyclists. Therefore, enforcement against all motorcyclists not showing vehicle licenses for their motorcycles should be stepped up.Conclusion: Motorcycle safety is largely poor in Maoming. Therefore, efforts to improve motorcycle safety should be strengthened by targeting not only motorcycle taxi drivers but also nonoccupational motorcyclists.

A96 Rising gasoline prices increase new motorcycle sales and fatalities in the United States

Abstract Background Motorcycle safety has received much attention from both researchers and policymakers in the United States (US), especially because motorcycle fatalities and injuries are increasing at the same time that overall motor vehicle fatalities have been decreasing. To our knowledge, no study has linked trends in gasoline prices with new motorcycle sales in the US. New motorcycle sales data are more likely to be associated with less experienced riders who may be entering motorcycling in response to escalating gasoline prices. We compare trends in gasoline prices with industry-provided data on new motorcycle sales, and then estimate the impact of new motorcycle sales on crash fatalities. Methods Data come from the Motorcycle Industry Council and Fatality Analysis Reporting System for 1984-2009. Autoregressive integrated moving average (ARIMA) regression is used to model the relationship of inflation-adjusted gasoline prices with sales. The impact of these new sales on fatalities is estimated. Results New motorcycles had their lowest sales with 186,000 units in 1992 and, afterward, grew every year until 2008, reaching 888,000 units—a 400% increase. Sales and gasoline prices are highly correlated (r=.78). ARIMA regression modeling predicts that for every $1 increase in gasoline prices, there are an additional 286,000 new motorcycle sales in the US (p Conclusions Although prior research shows a negative relationship between gasoline prices and driving, we present evidence that gasoline prices may be increasing incentives to purchase motorcycles, leading to an increase in fatalities from motorcycle crashes. These findings lend support to traffic safety initiatives to improve motorcycle safety gear and training requirements for new motorcycle riders.

Modeling fault among motorcyclists involved in crashes

Singapore crash statistics from 2001 to 2006 show that the motorcyclist fatality and injury rates per registered vehicle are higher than those of other motor vehicles by 13 and 7 times, respectively. The crash involvement rate of motorcyclists as victims of other road users is also about 43%. The objective of this study is to identify the factors that contribute to the fault of motorcyclists involved in crashes. This is done by using the binary logit model to differentiate between at-fault and not-at-fault cases and the analysis is further categorized by the location of the crashes, i.e., at intersections, on expressways and at non-intersections. A number of explanatory variables representing roadway characteristics, environmental factors, motorcycle descriptions, and rider demographics have been evaluated. Time trend effect shows that not-at-fault crash involvement of motorcyclists has increased with time. The likelihood of night time crashes has also increased for not-at-fault crashes at intersections and expressways. The presence of surveillance cameras is effective in reducing not-at-fault crashes at intersections. Wet-road surfaces increase at-fault crash involvement at non-intersections. At intersections, not-at-fault crash involvement is more likely on single-lane roads or on median lane of multi-lane roads, while on expressways at-fault crash involvement is more likely on the median lane. Roads with higher speed limit have higher at-fault crash involvement and this is also true on expressways. Motorcycles with pillion passengers or with higher engine capacity have higher likelihood of being at-fault in crashes on expressways. Motorcyclists are more likely to be at-fault in collisions involving pedestrians and this effect is higher at night. In multi-vehicle crashes, motorcyclists are more likely to be victims than at-fault. Young and older riders are more likely to be at-fault in crashes than middle-aged group of riders. The findings of this study will help to develop more targeted countermeasures to improve motorcycle safety and more cost-effective safety awareness program in motorcyclist training.