Comparison of moped,scooter and motorcycle crash risk and crash severity

The increased popularity of mopeds and motor scooters in Australia and elsewhere in the last decade has contributed substantially to the greater use of powered two-wheelers (PTWs) as a whole. As the exposure of mopeds and scooters has increased, so too has the number of reported crashes involving those PTW types, but there is currently little research comparing the safety of mopeds and, particularly, larger scooters with motorcycles. This study compared the crash risk and crash severity of motorcycles, mopeds and larger scooters in Queensland, Australia. Comprehensive data cleansing was undertaken to separate motorcycles, mopeds and larger scooters in police-reported crash data covering the five years to 30 June 2008. The crash rates of motorcycles (including larger scooters) and mopeds in terms of registered vehicles were similar over this period, although the moped crash rate showed a stronger downward trend. However, the crash rates in terms of distance travelled were nearly four times higher for mopeds than for motorcycles (including larger scooters). More comprehensive distance travelled data is needed to confirm these findings. The overall severity of moped and scooter crashes was significantly lower than motorcycle crashes but an ordered probit regression model showed that crash severity outcomes related to differences in crash characteristics and circumstances, rather than differences between PTW types per se. Greater motorcycle crash severity was associated with higher (>80 km/h) speed zones, horizontal curves, weekend, single vehicle and nighttime crashes. Moped crashes were more severe at night and in speed zones of 90 km/h or more. Larger scooter crashes were more severe in 70 km/h zones (than 60 km/h zones) but not in higher speed zones, and less severe on weekends than on weekdays. The findings can be used to inform potential crash and injury countermeasures tailored to users of different PTW types.     

Errors and violations in relation to motorcyclists' crash risk

This study was conducted to: (a) develop a questionnaire that reliably measures the behaviour of motorcyclists and (b) test which types of behaviour predict motorcyclists' crash risk. A Motorcycle Rider Behaviour Questionnaire (MRBQ), consisting of 43 items to measure the self-reported frequency of specific riding behaviours, was developed and administered to a sample of motorcyclists (N=8666). Principal components analysis revealed a 5-factor solution (traffic errors, control errors, speed violations, performance of stunts and use of safety equipment). Generalised linear modelling showed that, while controlling for the effects of age, experience and annual mileage, traffic errors were the main predictors of crash risk. For crashes in which respondents accepted some degree of blame, control errors and speed violations were also significant predictors of crash risk. Implications of the findings are discussed in relation to deciding which countermeasures may be most effective at reducing motorcycle casualty rates.     

Elderly drivers’ everyday behavior as a predictor of crash involvement—Questionnaire responses by drivers’ family members

When or whether elderly drivers stop driving is concerning not only to the drivers themselves but also to their family members. Therefore, it is important for family members to take the initiative if they wish to obtain information on the likelihood of the drivers’ involvement in crashes. On the basis of the older drivers’ Everyday Behavior Questionnaire (EBQ) developed in this paper, we attempt to predict drivers’ involvement in crashes using the responses given by their family members. The results revealed that this 14-item questionnaire has a sufficient level of internal consistency as well as a significant correlation (r = 0.29) with the experience of involvement in crashes in the last three years (p < 0.01). Although the EBQ is a proxy-reported questionnaire and does not include items directly related to driving behaviors, the correlation between the EBQ and crash involvement is stronger than that of the self-reported Driver Behavior Questionnaire reported in deWinter and Dodou (2010), who conducted a meta-analysis and estimated the overall correlation among samples of earlier studies. In addition, logistic regression analysis showed that the EBQ score and the exposure to driving risks, measured by the frequency of driving, are significant predictors of involvement in crashes.     

Young drivers--reduced crash risk with passengers in the vehicle

Studies have shown that the effect of passengers on accident propensity among young drivers is ambiguous--in some cases positive and some negative. In Sweden, various kinds of information are compiled in registers, including a national accident database and exposure data collected in a national investigation of the driving habits of license holders. Access to such data offers a good opportunity to study crash risks related to driving with and without passengers. This was done for drivers in three different age groups (18-24, 25-64 and >65 years) accompanied by one, two or three or more passengers. Differences in crash risk were estimated using incidence density ratios (IDRs) and 95% confidence intervals. The results show that passengers had an overall protective effect, that is, the crash risk was higher for those who drove alone, regardless of their age or gender. This protective effect increased with every extra passenger (up to eight), indicating that the more passengers in the vehicle, the safer the driving. The influence of passengers was weakest (albeit still positive) among the youngest drivers (ages 18-24 years), especially the males in that group. The protective impact showed the same pattern on all days of the week, but was most marked from Friday to Sunday for most of the drivers, regardless of age.     

Using the U.S. National Household Travel Survey to estimate the impact of passenger characteristics on young drivers’ relative risk of fatal crash involvement

Motor vehicle crashes are the main cause of morbidity and mortality in teenagers and young adults in the United States. Driving exposure and passenger presence, which can both vary by driver and passenger characteristics, are known to influence crash risk. Some studies have accounted for driving exposure in calculating young driver fatal crash risk in the presence of passengers, but none have estimated crash risk by driver sex and passenger age and sex. One possible reason for this gap is that data collection on driving exposure often precludes appropriate analyses. The purpose of this study was to examine, per 10 million vehicle trips (VT) and vehicle-miles traveled (VMT), the relative risk of fatal crash involvement in 15-20-year-old male and female drivers as a function of their passenger's age and sex, using solo driving as the referent. The Fatality Analysis Reporting System provided fatal motor vehicle crash data from 1999 to 2003 and the 2001 National Household Travel Survey (NHTS) provided VT and VMT. The NHTS collects driving exposure for both household and non-household members (e.g., friends, colleagues), but demographic characteristics only on household members. Missing age and sex of non-household passengers were imputed with hot deck using information from household passengers’ trips with non-household drivers, thereby enabling the calculation of crash rate and relative risk estimates based upon driver and passenger characteristics. Using this approach, the highest risk was found for young male drivers with 16-20-year-old passengers (relative risk [RR] per 10 million VT = 7.99; 95% confidence interval [CI], 7.34-8.69; RR per 10 million VMT = 9.94; 95% CI, 9.13-10.81). Relative risk was also high for 21-34-year-old passengers, again particularly when both drivers and passengers were male. These effects warrant further investigation and underscore the importance of considering driving exposure by passenger characteristics in understanding crash risk. Additionally, as all imputation techniques are imperfect, a more accurate estimation of U.S. fatal crash risk per distance driven would require national surveys to collect data on non-household passenger characteristics.     

Use of Cognitive Work Analysis for exploration of safety management in the operation of motorcycles and scooters

The risk of accident, injury and death is disproportionately higher for motorcycle riders than for motorists. In this paper, we investigate strategies of safety management associated with operation of powered two-wheel vehicles (motorcycles and scooters). Accident prevention is most often driven by an epidemiological approach that investigates the risk factors associated with accidents. By focusing on risk factors, these types of studies fail to examine the strengths of the system in any depth. In this paper we employ an ethnographic approach structured by reference to the framework of Cognitive Work Analysis, to identify how riders of powered two-wheel vehicles manage their own safety and the safety of others. We anticipate that this research will open up a rich, relatively untapped, area for exploration of safety interventions.     

Evaluation of the predictability of real-time crash risk models

The primary objective of the present study was to investigate the predictability of crash risk models that were developed using high-resolution real-time traffic data. More specifically the present study sought answers to the following questions: (a) how to evaluate the predictability of a real-time crash risk model; and (b) how to improve the predictability of a real-time crash risk model. The predictability is defined as the crash probability given the crash precursor identified by the crash risk model. An equation was derived based on the Bayes’ theorem for estimating approximately the predictability of crash risk models. The estimated predictability was then used to quantitatively evaluate the effects of the threshold of crash precursors, the matched and unmatched case-control design, and the control-to-case ratio on the predictability of crash risk models. It was found that: (a) the predictability of a crash risk model can be measured as the product of prior crash probability and the ratio between sensitivity and false alarm rate; (b) there is a trade-off between the predictability and sensitivity of a real-time crash risk model; (c) for a given level of sensitivity, the predictability of the crash risk model that is developed using the unmatched case-controlled sample is always better than that of the model developed using the matched case-controlled sample; and (d) when the control-to-case ratio is beyond 4:1, the increase in control-to-case ratio does not lead to clear improvements in predictability.     

The effect of crash experience on changes in risk taking among urban and rural young people

A 20-month prospective study was conducted to investigate the effect of motorcycle crash experience on changes in risk taking among 2514 urban and 2304 rural students in Taiwan. Risk taking was assessed using a 14-item self-administered questionnaire at the beginning and end of the study. A risk-taking score for each student at the initial and the last follow-up assessments was generated from adding up points across all 14 items. For exposure variables, the study documented past motorcycle crash history at the initial assessment and collected detailed information about any motorcycle crash involvement that occurred during the study period. A general linear mixed model was applied to assess the effects of prior and recent crash involvements on the path of risk-taking behavior. The results show that at the initial assessment, students with crash experience had higher risk-taking levels than those without crash experience. However, crash experience, irregardless of whether it was measured in terms of crash history prior to the study, crash frequency, time elapsed since the last crash, or crash severity, did not significantly change the risk-taking path among students, even though its effect differed between urban and rural areas.     

Exploring the safety implications of young drivers’ behavior,attitudes and perceptions

The present study aims at identifying and quantifying significant factors (i.e., demographic, aberrant driving behavior) associated with young drivers’ involvement in at-fault crashes or traffic citations at the ages of 16–17 (while having the Operational License) and 18–24 years old (while having the Full License). A second objective was to investigate the main reason(s) for involvement in risky driving behavior by young drivers.     

The Maze Test: A significant predictor of older driver crash risk

A study sponsored by the National Highway Traffic Safety Administration performed functional assessments on approximately 700 drivers age 70 and older who presented for license renewal in urban, suburban, and rural offices of the Maryland Motor Vehicle Administration. This volunteer sample received a small compensation for study participation, with an assurance that their license status would not be affected by the results. A comparison with all older drivers who visited the same sites on the same days indicated that the study sample was representative of Maryland older drivers with respect to age and prior driving safety indices. Relationships between drivers’ scores on a computer touchscreen version of the Maze Test and prospective crash and serious moving violation experience were analyzed. Results identified specific mazes as highly significant predictors of future safety risk for older drivers, with a particular focus on non-intersection crashes. Study findings indicate that performance on Maze Tests was predictive of prospective crashes and may be useful, as a complement to other, established cognitive screening tools, in identifying at-risk older drivers.     

Teen driver crash risk and associations with smoking and drowsy driving

Motor vehicle crashes are the leading cause of death for young people in the United States. The goal of this study was to identify risk factor profiles of teen and young adult drivers involved in crashes. General demographic and behavioral as well as driving-related factors were considered. Analysis of a nationally representative telephone survey of U.S. young drivers ages 14 to 22 (N = 900) conducted in 2005 was restricted to 506 licensed drivers (learners excluded). Statistically significant univariate associations between factors of interest and the primary outcome, crash involvement (ever) as a driver, were identified and included within a multivariate logistic regression model, controlling for potential demographic confounders. Aside from length of licensure, only driving alone while drowsy and being a current smoker were associated with having been in a crash. Gaining a better understanding of these behaviors could enhance the development of more customized interventions for new drivers.     

Developing crash modification functions to assess safety effects of adding bike lanes for urban arterials with different roadway and socio-economic characteristics

Although many researchers have estimated crash modification factors (CMFs) for specific treatments (or countermeasures), there is a lack of studies that explored the heterogeneous effects of roadway characteristics on crash frequency among treated sites. Generally, the CMF estimated by before–after studies represents overall safety effects of the treatment in a fixed value. However, as each treated site has different roadway characteristics, there is a need to assess the variation of CMFs among the treated sites with different roadway characteristics through crash modification functions (CMFunctions). The main objective of this research is to determine relationships between the safety effects of adding a bike lane and the roadway characteristics through (1) evaluation of CMFs for adding a bike lane using observational before–after with empirical Bayes (EB) and cross-sectional methods, and (2) development of simple and full CMFunctions which are describe the CMF in a function of roadway characteristics of the sites. Data was collected for urban arterials in Florida, and the Florida-specific full SPFs were developed. Moreover, socio-economic parameters were collected and included in CMFunctions and SPFs (1) to capture the effects of the variables that represent volume of bicyclists and (2) to identify general relationship between the CMFs and these characteristics. In order to achieve better performance of CMFunctions, data mining techniques were used.     

In-depth analysis of drivers’ merging behavior and rear-end crash risks in work zone merging areas

This study investigates the drivers’ merging behavior and the rear-end crash risk in work zone merging areas during the entire merging implementation period from the time of starting a merging maneuver to that of completing the maneuver. With the merging traffic data from a work zone site in Singapore, a mixed probit model is developed to describe the merging behavior, and two surrogate safety measures including the time to collision (TTC) and deceleration rate to avoid the crash (DRAC) are adopted to compute the rear-end crash risk between the merging vehicle and its neighboring vehicles. Results show that the merging vehicle has a bigger probability of completing a merging maneuver quickly under one of the following situations: (i) the merging vehicle moves relatively fast; (ii) the merging lead vehicle is a heavy vehicle; and (iii) there is a sizable gap in the adjacent through lane. Results indicate that the rear-end crash risk does not monotonically increase as the merging vehicle speed increases. The merging vehicle's rear-end crash risk is also affected by the vehicle type. There is a biggest increment of rear-end crash risk if the merging lead vehicle belongs to a heavy vehicle. Although the reduced remaining distance to work zone could urge the merging vehicle to complete a merging maneuver quickly, it might lead to an increased rear-end crash risk. Interestingly, it is found that the rear-end crash risk could be generally increased over the elapsed time after the merging maneuver being triggered.     

Findings from the Candrive/Ozcandrive study: Low mileage older drivers,crash risk and reduced fitness to drive

Previous research has found that only older drivers with low annual driving mileages had a heightened crash risk relative to other age groups. These drivers tend to drive mainly in urban areas, where the prevalence of complex traffic situations increases crash risk. However it might also be that some drivers may have reduced their driving due to perceived or actual declines in driving fitness.     

Analysis of crash severities using nested logit model—Accounting for the underreporting of crashes

Recent studies in the area of highway safety have demonstrated the usefulness of logit models for modeling crash injury severities. Use of these models enables one to identify and quantify the effects of factors that contribute to certain levels of severity. Most often, these models are estimated assuming equal probability of the occurrence for each injury severity level in the data. However, traffic crash data are generally characterized by underreporting, especially when crashes result in lower injury severity. Thus, the sample used for an analysis is often outcome-based, which can result in a biased estimation of model parameters. This is more of a problem when a nested logit model specification is used instead of a multinomial logit model and when true shares of the outcomes-injury severity levels in the population are not known (which is almost always the case). This study demonstrates an application of a recently proposed weighted conditional maximum likelihood estimator in tackling the problem of underreporting of crashes when using a nested logit model for crash severity analyses.     

In defence of the ‘low-mileage bias’

There has been a long-recognised association between extent of driving and crash involvement: the lower the annual mileage driven, the higher the per-distance crash rate. Because older drivers generally drive less distance per year than others, this association has been used to explain much of their apparent over-involvement in crashes. Several studies from different countries around the world have demonstrated this ‘low-mileage bias’ and the relative safety of older drivers. However all studies have relied upon self-reported crash involvement and driving activity. Staplin et al. [Staplin, L., Gish, K., Joyce, J., 2008. ‘Low mileage bias’ and related policy implications—a cautionary note. Accident Analysis and Prevention 40, 1249–1252] have drawn attention to the discrepancy between self-reported and odometer-based driving distances and have argued against the credibility of the low-mileage bias. This paper has re-worked initial data from an early study which supported low-mileage bias, this time using odometer-based readings rather than self-reported mileage. Accepting the odometer readings at face value, the low-mileage bias remains evident, albeit at a reduced level.     

Fatality risk in motorcycle collisions with roadside objects in the United States

Motorcycle crashes with roadside objects often involve more than one impact event: typically involving a collision with the ground and another object. The objective of this study was to determine the fatality risk in these roadside object collisions when compared with crashes only involving a collision with the ground. The roadside objects analyzed included guardrails, concrete barriers, signs, utility poles, and trees. The Fatality Analysis Reporting System (FARS) database was used in conjunction with the General Estimates System (GES) to analyze fatality risk for motorcycle crashes from 2004 to 2008. The analysis was based upon over 3600 fatal motorcycle crashes with roadside objects. Collisions with roadside objects were found to have a higher fatality risk than collisions with either the ground or another motor vehicle. Based on the most harmful event reported in the crash, motorcycle collisions with guardrail were 7 times more likely to be fatal than collisions with the ground, and collisions with trees were almost 15 times more likely to be fatal than collisions with the ground. Additionally, the roadside object was reported as the most harmful event in the majority of the crashes in fatal two-event crashes involving a roadside object and a collision with the ground, with the exception of collisions with signage. From these analyses it was concluded that collisions with fixed objects are more harmful to motorcyclists than collisions with the ground.     

Evaluation of the impacts of traffic states on crash risks on freeways

The primary objective of this study is to divide freeway traffic flow into different states, and to evaluate the safety performance associated with each state. Using traffic flow data and crash data collected from a northbound segment of the I-880 freeway in the state of California, United States, K-means clustering analysis was conducted to classify traffic flow into five different states. Conditional logistic regression models using case-controlled data were then developed to study the relationship between crash risks and traffic states. Traffic flow characteristics in each traffic state were compared to identify the underlying phenomena that made certain traffic states more hazardous than others. Crash risk models were also developed for different traffic states to identify how traffic flow characteristics such as speed and speed variance affected crash risks in different traffic states. The findings of this study demonstrate that the operations of freeway traffic can be divided into different states using traffic occupancy measured from nearby loop detector stations, and each traffic state can be assigned with a certain safety level. The impacts of traffic flow parameters on crash risks are different across different traffic flow states. A method based on discriminant analysis was further developed to identify traffic states given real-time freeway traffic flow data. Validation results showed that the method was of reasonably high accuracy for identifying freeway traffic states.     

Pedestrian- and driver-related factors associated with the risk of causing collisions involving pedestrians in Spain

This study aimed to quantify the association between pedestrian- and driver-related factors and the risk of causing road crashes involving pedestrians in urban areas in Spain between 1993 and 2011. From the nationwide police-based registry of road crashes with victims in Spain, we analyzed all 63,205 pairs of pedestrians and drivers involved in crashes in urban areas in which only the pedestrian or only the driver was at fault. Logistic regression models were used to obtain adjusted odds ratios to assess the strength of association between each individual-related variable and the pedestrian’s odds of being at fault for the crash (and conversely, the driver’s odds of not being at fault).The subgroups of road users at high risk of causing a road crash with a pedestrian in urban areas were young and male pedestrians, pedestrians with psychophysical conditions or health problems, the youngest and the oldest drivers, and drivers with markers of high-risk behaviors (alcohol use, nonuse of safety devices, and driving without a valid license). These subgroups should be targeted by preventive strategies intended to decrease the rate of urban road crashes involving pedestrians in Spain.