Motorcyclist Injury Research Articles

Factors Affecting Single and Multivehicle Motorcycle Crashes: Insights from Day and Night Analysis Using XGBoost-SHAP Algorithm

This study aimed to identify and compare the risk factors associated with motorcycle crash severity during both daytime and nighttime, for single and multivehicle incidents in Thailand using 2021–2024 data. The research employed the XGBoost (Extreme Gradient Boosting) method for statistical analysis and extensively examined the temporal instability of risk factors. The results highlight the importance of features impacting the injury severity of roadway collisions across various conditions. For single motorcycle crashes, the key risk factors included speeding, early morning incidents, off-road events, and long holidays. In multivehicle crashes, rear-end collisions, interactions with large vehicles, and collisions involving other motorcycles or passenger cars were linked to increased injury severity. The findings indicate that the important factors associated with motorcyclist injury severity in roadway crashes vary depending on the type of crash and time of day. These insights are valuable for policymakers and relevant authorities in developing targeted interventions to enhance road safety and mitigate the incidence of severe and fatal motorcycle crashes.

Seasonal variations and temporal instability of motorcyclist injury severity in Cambodia: Analyses based on a random parameter logit model with heterogeneity in means and variances

Motorcycles are a prevalent mode of transportation in countries like Cambodia that experience distinct rainy and dry seasons. However, the safety concerns associated with motorcycling in this region have not been thoroughly investigated. This study addresses this research gap by examining the severity of motorcyclist injuries in Cambodia, considering the potential variations across seasons and the fluctuations in contributing factors over time. Utilizing a random parameter logit model with heterogeneity in means and variances, the research analyzes motorcycle crash data from 2015 to 2017 to identify heterogeneities in the determinants of injury severity. The study confirms seasonal variations and temporal instabilities in influential factors, highlighting the need for distinct modeling for dry and rainy seasons due to varying contributing factors. Key findings include the consistent increase in fatal injury risk associated with head-on collisions and elderly rider involvement across both seasons. During the rainy season, motorcycle-to-motorcycle crashes significantly heighten the likelihood of severe injuries, with weekend crashes more likely to result in fatalities. Furthermore, more than half of speeding incidents during the rainy season consistently led to fatal injuries across all three years. In contrast, during the dry season, riders faced a greater risk of severe injuries compared to pillion riders, with crashes on national roads more likely to lead to fatal outcomes. Temporal stability tests further reveal that the influence of external variables on motorcyclist injury severity varies across years, stressing the need for tailored, season-specific approaches to effectively mitigate and prevent crashes.

Modeling of motorcyclist injury severities: A comparison between crashes on main-, frontage-, and standard-lane of roadway

This study aims to examine the differentiated impacts of frontage-lane, main-lane, and standard-lane motorcycle collisions on rider injury severity in Thailand. Using comprehensive crash data from 2016 to 2019, a random parameters model accommodating heterogeneity in means and variances is applied. Predictive comparisons between out-of-sample and within-sample predictions uncover differences between collision location sub-models. For frontage-lane collisions, severe or fatal injuries are positively associated with male riders, truck involvement, and speeding. In main-lane collisions, increased severe/fatal injury risk is associated with nighttime, holidays, weekends, single-motorcycle crashes, and collisions with trucks or pickups. In standard-lane collisions, positive severe/fatal injury associations include male riders, nighttime, non-peak hours, speeding, truck/pickup involvement, and pillion presence. Predictive comparisons show reorienting crashes to the frontage lane could reduce fatal injury probability by 0.1199 and 0.2233 versus the main and standard lanes respectively, preventing many fatalities. This research underscores accounting for unobserved heterogeneity and predictive simulation insights. The findings inform effective countermeasures and aid safety professionals, instructors, policymakers, law enforcement, and designers in motorcycle safety efforts.

Addressing endogeneity between hazardous actions and motorcyclist injury severity by integrating generalized propensity score approach and instrumental variable model

Motorcyclist hazardous actions (e.g., particularly speed too fast or failing to stop in assured clear distance (ACD)) are commonly identified as risk factors that significantly impact the motorcyclist injury severity. However, endogenous effects resulting from motorcyclist hazardous actions have seldom been considered, which may cause the biased estimates. Specifically, two important sources of endogeneities (i.e., endogeneity arising from observed confounding factors and endogeneity caused by unobserved confounders) tend to yield a biased relationship between hazardous actions and motorcyclist injury severity. To jointly account for two sources of endogeneities and provide more robust estimates, the study tries to assess the effects of speed-too-fast and failing to stop in ACD on motorcyclist injury severity via a hybrid method by integrating the generalized propensity score approach with instrumental variable model. Specifically, we adopt a generalized propensity score matching method to reduce the endogeneity bias arising from observed confounders. Furthermore, the matched data are used to develop an instrumental variable model with random parameters to handle the endogeneity resulting from unobserved confounders and unobserved heterogeneity, which consists of random parameters binary logit models modelling the motorcyclist hazardous actions in the first stage and a random parameters logit model with heterogeneity in means modelling the motorcyclist injury severity in the second stage. The proposed approach is estimated based on Michigan motorcycle crash data from 2015 to 2018. Results suggest that alcohol use leads motorcyclists to engage in speed-too-fast, while alcohol use and signal control cause motorcyclists to be involved in failing to stop in ACD. Middle-aged and elderly motorcyclists, alcohol use, speed too fast, speed limit ≥50 mph, wet surface, and head-on/angle crashes significantly increase the injury severity of motorcyclists. Moreover, failing to stop in ACD produces a random parameter with heterogeneity in means, while intersection increases the mean effects of failing to stop in ACD on motorcyclist minor injury. These findings further provide insights for a better understanding of hazardous actions and motorcyclist injury severity via the impact analysis of various explanatory variables.

The Effect of Age on Motorcyclist Injury Severities in Multi-Vehicle Motorcycle Crashes: Accounting for Unobserved Heterogeneity and Insights From Out-of-Sample Prediction

The Effect of Age on Motorcyclist Injury Severities in Multi-Vehicle Motorcycle Crashes: Accounting for Unobserved Heterogeneity and Insights From Out-of-Sample Prediction

Exploring senior motorcyclist injury severity crashes: Random parameter model with heterogeneity in mean and variance

The fatality rates of motorcyclists are substantially higher than for passenger car occupants per registered vehicle and vehicle miles traveled. There is limited information regarding the contributing factors associated with motorcycle injury severity among senior riders (≥65 years). Therefore, random parameter logit models with heterogeneity in mean and variance were developed based on seven years (2012–2018) crashes database at two different locations, intersection and non-intersection roadways in Florida. About 30 indicator variables were significant for both intersection and non-intersection crashes and were categorized under the driver, temporal, spatial, roadway, and other characteristics, driver action, and crash type. Several factors were found to impact the fatal injuries at the intersection directly, and no intersection crashes. However, speeding, dark, and morning crashes are shared by both locations. This paper highlights the importance of considering a specific crash location during the analysis to minimize any possible erroneous and unreliable countermeasures. Various countermeasures were proposed to assist the safety authorities and policymakers to mitigate the senior motorcyclist injury severities related to crashes.

Association of Illegal Motorcyclist Behaviors and Injury Severity in Urban Motorcycle Crashes

Motorcycle crashes have been a significant cause of death and serious injury in urban regions, which has a negative effect on the development of sustainable urban transportation. In this study, two logit models, one model for illegal motorcyclist behaviors and the other for crash injury severity, are developed using urban motorcycle crashes in Hunan Province, China. The two developed models could allow us to simultaneously identify significant factors related to specific illegal behaviors and the severity level of crashes. The results show that illegal motorcyclist behaviors, including unlicensed riding, drunk riding, and improper overtaking/lane changes, are more likely to cause serious injuries or death. Young motorcyclists are associated with a higher likelihood of illegal behaviors, while old motorcyclists are associated with a higher likelihood of serious injuries or death. Night conditions without street lights are significantly associated with a higher probability of illegal motorcyclist behaviors as well as higher injury severity. Crashes caused by drunk riding have the highest likelihood of fatal/severe injury. Based on these findings, efficient interventions are recommended to curb illegal motorcyclist behaviors and alleviate crash injury severity.

Modeling endogeneity between motorcyclist injury severity and at-fault status by applying a Bayesian simultaneous random-parameters model with a recursive structure

Motorcyclists’ at-fault status is an important factor influencing crash injury severity in that intrinsically unsafe riders tend to be at fault and are the ones likely to be involved in severe crashes. However, this endogeneity issue and its influence on model estimations have seldom been investigated with regard to motorcyclist crash severity analysis. This study proposes a simultaneous model system to account for the endogenous effects of at-fault status in the motorcyclists’ injury severity analysis. Four Bayesian simultaneous models were developed using motorcyclist crash injury data from Queensland, Australia, from the year 2017 through 2018, including an independent binary and independent ordered Probit model, a simultaneous binary-ordered Probit model without recursive structure, a simultaneous binary-ordered Probit model with a recursive structure, and a simultaneous random-parameters binary-ordered Probit model with a recursive structure. The results of all simultaneous models indicate the existence of endogeneity associated with at-fault status in the injury outcome analysis. In particular, the endogenous relationship is detected by the significant cross-equation correlations in the simultaneous models. The model comparison by Deviance Information Criteria highlights the superiority of the simultaneous random-parameters model with a recursive structure. It was found that exogenous variables such as traffic sign-controlled measures, posted speed limits of 100–110 km/h, the presence of vertical grades, rider age 16–24 years, and unlicensed influenced injury severity indirectly through at-fault status, and ignoring these indirect influences could result in biased estimates. The presence of random parameters, such as collisions with heavy vehicles and riders over 59 years, highlights the importance of considering heterogeneity. The simultaneous random-parameters model with a recursive structure model revealed that the critical factors contributing to riders’ at-fault status included unlicensed riders and posted speed limits of 100–110 km/h, and the crucial factors influencing riders’ injury levels included head-on crashes, collisions with heavy vehicles, darkness-unlighted, and riders over 59 years old. The proposed model system demonstrates the importance of considering both endogeneity and heterogeneity for modeling the injury severity of motorcyclists.

Temporal Instability of Factors Affecting Injury Severity in Helmet-Wearing and Non-Helmet-Wearing Motorcycle Crashes: A Random Parameter Approach with Heterogeneity in Means and Variances.

Not wearing a helmet, not properly strapping the helmet on, or wearing a substandard helmet increases the risk of fatalities and injuries in motorcycle crashes. This research examines the differences in motorcycle crash injury severity considering crashes involving the compliance with and defiance of helmet use by motorcycle riders and highlights the temporal variation in their impact. Three-year (2017–2019) motorcycle crash data were collected from RESCUE 1122, a provincial emergency response service for Rawalpindi, Pakistan. The available crash data include crash-specific information, vehicle, driver, spatial and temporal characteristics, roadway features, and traffic volume, which influence the motorcyclist’s injury severity. A random parameters logit model with heterogeneity in means and variances was evaluated to predict critical contributory factors in helmet-wearing and non-helmet-wearing motorcyclist crashes. Model estimates suggest significant variations in the impact of explanatory variables on motorcyclists’ injury severity in the case of compliance with and defiance of helmet use. For helmet-wearing motorcyclists, key factors significantly associated with increasingly severe injury and fatal injuries include young riders (below 20 years of age), female pillion riders, collisions with another motorcycle, large trucks, passenger car, drivers aged 50 years and above, and drivers being distracted while driving. In contrast, for non-helmet-wearing motorcyclists, the significant factors responsible for severe injuries and fatalities were distracted driving, the collision of two motorcycles, crashes at U-turns, weekday crashes, and drivers above 50 years of age. The impact of parameters that predict motorcyclist injury severity was found to vary dramatically over time, exhibiting statistically significant temporal instability. The results of this study can serve as potential motorcycle safety guidelines for all relevant stakeholders to improve the state of motorcycle safety in the country.

The impact of weekday, weekend, and holiday crashes on motorcyclist injury severities: Accounting for temporal influence with unobserved effect and insights from out-of-sample prediction

This paper examines the differences between weekday, weekend, and holiday crashes on the severity of motorcyclist injury using four-year motorcycle crash data in Thailand from 2016 to 2019. While also considering the temporal stability assessment of significant factors, this study adopted a random parameters logit model with possible heterogeneity in means and variances to account for unobserved heterogeneity. Three levels of motorcyclist injury severity were considered including minor injury, severe injury, and fatal injury. Two series of likelihood ratio tests clearly indicated nontransferability between weekday, weekend, and holiday crashes and substantial temporal instability over the four-year study period. Findings also revealed many statistically significant factors that affect motorcyclist injury severity probabilities in various time-of-year and yearly models. In addition, the prediction comparison results (using out-of-sample prediction simulation) clearly illustrated substantial differences between weekday, weekend, and holiday motorcyclist injury severity probabilities, and substantial changes in each injury predicted probabilities over time. This paper highlights the importance of accounting for day-of-week and holiday transferability and temporal instability with unobserved effects in the determinants that affect motorcyclist injury severity. Through the observed nontransferability and temporal instability, the findings provide valuable knowledge for practitioners, researchers, institutions, and decision-makers to enhance highway safety, specifically motorcyclist safety, and facilitate the development of more effective motorcycle crash injury mitigation policies.

Motorcyclist injury severity analysis: a comparison of Artificial Neural Networks and random parameter model with heterogeneity in means and variances

In Thailand, the motorcyclist mortality rate is steadily on the rise and remains a serious concern for highway administrators and burden on both economic and local people. Using motorcycle-crash data in Thailand from 2016 to 2019, this study empirically employed and compared the Artificial Neural Networks (ANN) model and random parameters binary probit model with heterogeneity in means and variances (RPBPHM) to explore the effects of a wide range of associated risk characteristics on the severity outcomes of the motorcyclist. Study results revealed that probabilities of injury or fatal crash increase for crashes that involve male riders, riding with pillion, speeding, improper overtaking, riders under influence of alcohol, fatigue riders, undivided road and so on. The probability of non-injury crash increases for crashes on main or frontage traffic lane, four-lane road, concrete road, during rain, involving collision with other motorcycles, rear-end crashes, sideswipe crashes, single-motorcycle crashes and crashes within urban areas. The RPBPHM models were found to outperform the ANN model (quadratic support vector machine) in all performance metrics. The findings could potentially assist policymaker, safety professionals, practitioners, trainers, government agencies or highway designers in future planning and serve as guidance for mitigation policies directed at safety improvement for motorcyclists.

Empirical comparison of the effects of urban and rural crashes on motorcyclist injury severities: A correlated random parameters ordered probit approach with heterogeneity in means

In Thailand in 2016, more than 70% of all deaths due to road accidents were motorcyclist deaths. This study uses a correlated random parameters ordered probit model with heterogeneity in means (CRPOPHM) to obtain insight into differences in the significant factors determining the severity of motorcyclist injury between motorcycle crashes in urban and rural roadways, using data on motorcycle crashes in Thailand from 2016 to 2019. Using a rating system for injury severity level from minor injury to severe injury and to fatal injury, a wide range of potential risk factors are considered, including rider characteristics and actions, roadway characteristics, environmental and temporal characteristics, and crash characteristics. The findings indicate that, although some factors are significant in both urban and rural models, factors such as male rider, illegally overtaking, drowsiness, four-lane or wider highway, flush or depressed median, road on slope, weekend, nighttime with light, crash with van or minibus, and rear-ending or sideswiping crash, are significant only in the rural model, whereas the factors barrier median, occurring between 18:00 and 23:59, and striking a passenger car are statistically significant in only the urban model. These findings further suggests that difference in effect of unobserved characteristics could be seen in different crash locations, and splitting the model estimation between both location types could be done to develop effective guidance for policies to mitigate the severity of motorcyclist injuries. In addition, practical policy-related recommendations drawn from the results of the analysis are provided. With respect to methodology, the proposed CRPOPHM method outperforms lower-ordered models in terms of statistical fit and captures unobserved heterogeneity to a greater extent.

Investigation of factors influencing motorcyclist injury severity using random parameters logit model with heterogeneity in means and variances

Motorcyclists are an integral component of the traffic stream especially, in low-income developing countries like Pakistan. However, motorcyclists lay in the group of vulnerable road users i.e., road users with the least protection, along with pedestrians and cyclists. Therefore, the current study employed the random parameters logit model to identify key risk factors associated with motorcyclist injury severity using three years crash data (2017-2019) for city of Rawalpindi, Pakistan. To calibrate the model, motorcyclist injury severity thresholds are classified as no injury, minor injury, severe injury, and fatal injury. For motorcyclist injury severity analysis, the effects of vehicle crash characteristics, weather conditions, rider attributes and other socio-demographic considerations were primarily considered. The study results showed that severe and fatal injury risk is increased for crashes occurred during weekdays, involving riders aged above 50 years, involving the collision of motorcycles with passenger car and heavy vehicles, involving a female as a pillion rider, and those that occurred due to over speeding. Based on the results obtained from the model, the study suggests several policy implication as strict implementation of traffic regulations such as heavy fines or cancellation of driving license on over speeding, wrong turns, inappropriate passing, running a red light, not wearing helmet etc., instructing the females not to wear loose clothes or trailing scarves while riding the bike as pillion riders. The outcomes are expected to stimulate more interest and discussion regarding motorcycle safety in the country and can be used by city traffic police and highway authorities to boost the road safety.

A spatiotemporal analysis of motorcyclist injury severity: Findings from 20 years of crash data from Pennsylvania

Motorcyclists face higher risks of severe injuries in crashes compared to motor vehicle drivers who are often protected by seatbelts and airbags during collisions. A report by the National Highway Traffic Safety Administration reveals that motorcyclists have 27 times the risk of fatality in traffic crashes as much as motor vehicle drivers. Previous studies have identified a list of risk factors associated with motorcyclist injury severity and generated valuable insights for countermeasures to protect motorcyclists in crashes. These studies have shown that wearing helmets and/or motorcycle-specific reflective clothing and boots, driving alcohol/drug-free, and obeying traffic regulations are good practices for safe motorcycling. However, these practices and other risk factors are likely to interact with local geographic, socio-economic, and cultural contexts, leading to diversified correlations with motorcyclist injury severity, which remains under-explored. Such correlations may exhibit variations across space and time. The objective of this study is to revisit the correlates of motorcyclist injury severity with a focus on the spatial and temporal variations of correlations between risk factors and injury severity. This study employed an integrated spatiotemporal analytical approach to mine comprehensive statewide 20 years’ motorcycle-involved traffic crashes (N = 50,823) in Pennsylvania. Non-stationarity tests were performed to examine the significance of variations in spatially and temporally local correlations. The results show that most factors, such as helmet, engine size, vehicle age, pillion passenger, at-fault striking, and speeding, hold significant non-stationary relationships with motorcyclist injury severity. Furthermore, cluster analysis of estimations reveals the regional similarities of correlates, which may help practitioners develop regional motorcyclist safety countermeasures.

Evaluating the combined effect of alcohol-involved and un-helmeted riding on motorcyclist fatalities in Taiwan

BackgroundAlcohol-involved riders tend to engage in other risk-taking behaviours such as un-helmeted riding which could further increases injury severity. The combined effect of alcohol-involved and un-helmeted riding on fatal injuries is rarely investigated. This study investigated the interaction effect between blood alcohol concentration and helmet use on fatal injuries. MethodsThis study used the National Taiwan Traffic Crash Dataset for the period from 2011 to 2015. Data on road crashes involving a motorcycle and an automobile were extracted and analysed. Multiple logistic regression models were used to calculate the adjusted odds ratio (AOR). We calculated an interaction effect for blood alcohol concentration and helmet use based on STROBE guidelines. ResultsThere were a total of 669,292 motorcyclist casualties; among these casualties, 3459 (0.5 %) motorcyclists sustained fatal injuries. Alcohol-involved riders were 9.47 times (AOR = 9.47; 95 % CI = 8.75–10.25) more likely than sober ones to sustain fatal injuries. Alcohol-involved and un-helmeted riders were approximately 18 times (AOR = 18.1; CI: 15.9–20.4) more likely to sustain fatal injuries than sober and helmeted riders. Riders involved in head-on crashes and approach-turn motorcycle crashes had an increased probability of sustaining fatal injuries by 240 % (AOR = 3.4; 95 % CI = 2.91–4.09) and 132 % (AOR = 2.3; 95 % CI = 2.016–2.67), respectively. ConclusionsThis study found that alcohol-involved riding acts synergistically with un-helmeted riding to increase motorcyclist injury severity.

A statistical assessment of temporal instability in the factors determining motorcyclist injury severities

This study explores the temporal instability of factors affecting motorcyclist-injury severities in single-vehicle motorcycle crashes in Florida. Two data sources are used; one covers the 2012 to 2016 crash histories of Florida motorcyclists who were newly licensed in 2012, and the second covers motorcycle crashes that occur on horizontal curves in Florida from 2005 to 2015. In the first dataset (2012 new riders), temporal changes may result from riders gaining experience as well as general temporal shifts. In the second dataset, rider experience is unknown (thus becoming a source of potential unobserved heterogeneity) but the temporal changes will be largely from general temporal shifts. With three possible motorcyclist injury severity outcomes (no visible injury, minor injury, and severe injury), random parameters multinomial logit models, that allow for heterogeneity in means and variances, were estimated for all possible annual time periods in each dataset. Likelihood ratio tests were conducted to examine the overall stability of model estimates across time periods, and marginal effects of each explanatory variable were also considered to investigate the temporal instability of the effect of individual parameter estimates on motorcyclist injury-severity probabilities. A wide range of variables was considered including motorcyclists’ attributes (such as ethnicity and age), roadway and environmental conditions (such as light and road surface conditions), motorcycle characteristics (such as motorcycle make and type of motorcycle), rider actions (such as speeding and improper driving actions), and roadway conditions (such as obstacles on the road and speed limits). The results show significant temporal instability in motorcyclist-injury severity models, which likely result from changes in motorcycle technology and performance, changes in macroeconomic conditions, changes induced by how riders respond to the changing behavior of other road users (whose behavior may be changing as a result of technology changes in their vehicles, evolving use of personal technologies in their vehicle, such as cell phones, etc.), and the changes in riders’ behavior and skills over time.

Motorcyclist injury risk as a function of real-life crash speed and other contributing factors

The Vision Zero approach advocates for a road transport system designed with human injury tolerance and human fallibility as its basis. While biomechanical limits and the relationship between speed and injury outcome has been extensively investigated for car occupants and pedestrians, research analyzing this relationship for motorcyclists remains limited. The aim of this study was to address this issue by developing multivariate injury risk models for motorcyclists that estimate the relationship between speed and injury severity. For that purpose, motorcycle injury crashes from the German In-Depth Accident Study (GIDAS) database for the period 1999–2017 (n = 1037) were extracted. Different models were tested using logistic regression and backwards elimination of non-significant variables. The best fitting model in the current study included relative speed, type of crash opponent, impact location on the motorcycle and impact mechanism of the rider during the crash. A strong and significant relationship between relative speed and injury severity in motorcycle crashes was demonstrated. At 70 km/h, the risk for at least serious injuries in collisions with wide objects, crash barriers and narrow objects was 20%, 51%, and 64%, respectively. Further, it was found that head-on collisions between motorcycles and passenger cars, with both vehicles traveling at 60 km/h (a relative speed at 120 km/h), present 55% risk of at least serious injury to the motorcycle rider.More research is needed to fully understand the boundary conditions needed to design a safe road transport system for motorcyclists. However, this study provides important insights into the relationship between speed and injury severity for riders in various crash situations. The results may be useful in the discussion of appropriate speed limits and in determining the benefits of countermeasures which aim to reduce crash speed.

Factors affecting motorcyclists’ injury severities: An empirical assessment using random parameters logit model with heterogeneity in means and variances

Motorcycles constitute 61% of the total registered vehicles in Pakistan and there has been a 371% growth in motorcycles in the country from year 2005-2015. Motorcycle is an essential and popular mode of transportation in Pakistan, therefore, the present study estimated a random parameters logit model to investigate the factors influencing the motorcycle injury severity using motorcycle crash data of Rawalpindi city collected by the Provincial Emergency Response Service. No injury, minor injury, severe injury and fatal injury are used as four categories of motorcyclist injury severity levels to calibrate the model. Mainly the effects of speed limits, crash-specific factors, rider attributes, roadway characteristics, weather and socio-demographics factors are considered for motorcycle-injury severity analysis. It was revealed that probability of fatal/severe injury increases for crashes: involving middle-aged riders (25–50 years) and riders with no education, occurring on roads with posted speed limit of 70 kms per hour or higher, crashes involving a motorcycle and a heavy vehicle, involving collision of a motorcycle with a fixed object and occurring during dry weather conditions. Also, the probability of minor injury increases for crashes: occurring on divided streets and road segments with a posted speed limit of less than 50 kms per hour, involving Chinese brand motorcycles, involving registered motorcycles, and where at least one motorcycle and auto rickshaw is involved. The research findings suggest that besides measures to control/ reduce the risky motorcyclists behavior there is a need to lower speed limits on roads with a higher motorcycle proportion, separate motorcycles from heavy vehicles and removal of fixed objects from the roadside. Besides data limitations, results are expected to generate more discussion and interest in motorcycle safety in the country and can be used by the enforcement agencies to improve/ enhance the current state of motorcycle safety in the country.

CORRELATIONS AMONG MOTORCYCLE-RELATED DEATHS, HELMET LAW ENFORCEMENT AND HELMET USAGE FOR ASEAN COUNTRIES

The ASEAN Community is recorded to have a high rate of motorcyclist injury and death. This study aims to determine the statistical correlations among the deaths related to motorcycle accidents, helmet law enforcement and helmet use among ASEAN countries. Linear regression analysis was applied to develop models exploring the correlations at the 95% confidence level. The results indicate that those countries with higher efficiency of helmet law enforcement and higher helmet usage have achieved lower motorcycle-related death. The findings are useful for responsible organizations to determine road safety policies to reduce the deaths of motorcyclists in ASEAN countries.

The older motorcyclist

BackgroundMotorcyclists are vulnerable road users and are over represented in road fatality and injury statistics. We aimed to explore the trend of ageing motorcyclists, factors in older motorcyclist collisions and injury severity and fatality rates in older motorcyclists. MethodsWe performed a literature review of the subject using TRID, Cochrane Injuries, Medline and CINAHL. ResultsInternationally, there has been a disproportionate rise in injuries and fatalities involving older motorcyclists. Older motorcyclists are more likely to be admitted to hospital, have more severe injuries, require intensive care, have a longer length of stay and suffer more complications. Head and thoracic injuries are more common and injuries at all sites are more severe in older adults. Comorbidities and reduced physiologic reserve predispose older motorcyclists to higher mortality and more severe injuries. Older motorcyclists are more likely to ride motorcycles with larger capacity engines. They are more likely to crash on higher speed roadways and in rural areas. An increasing proportion of older motorcyclists are returning riders whose riding skill has likely depreciated over time but are riding on powerful machines. ConclusionsMotorcyclists are getting older. The nature of motorcycling is changing and for many, especially older riders, it is primarily a leisure pursuit. In general, older motorcyclists are safer riders but in recent years, there has been an inordinate rise in fatalities and injuries affecting older motorcyclists. Training and safety initiatives for older motorcyclists and returning riders may be effective in reducing injuries and fatalities.