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The findings show that vessels that experienced casualties within a preceding 10-year period were at increased odds of disaster. SB216763 manufacturer Other significant predictors included safety decal status and hull material. Practical Applications The results of this analysis emphasize the importance of implementing vessel-specific preventive maintenance plans. At an industry level, specific prevention policies should be developed focusing on high-risk fleets to identify and correct a wide range of safety deficits before they have catastrophic and fatal consequences.
Many U.S. cities have adopted the Vision Zero strategy with the specific goal of eliminating traffic-related deaths and injuries. To achieve this ambitious goal, safety professionals have increasingly called for the development of a safe systems approach to traffic safety. This approach calls for examining the macrolevel risk factors that may lead road users to engage in errors that result in crashes. This study explores the relationship between built environment variables and crash frequency, paying specific attention to the environmental mediating factors, such as traffic exposure, traffic conflicts, and network-level speed characteristics.
Three years (2011-2013) of crash data from Mecklenburg County, North Carolina, were used to model crash frequency on surface streets as a function of built environment variables at the census block group level. Separate models were developed for total and KAB crashes (i.e., crashes resulting in fatalities (K), incapacitating injuries (A), or non-incapacitating injurivironment influences traffic safety. The variables found to be significant are all policy-relevant variables that can be manipulated to improve traffic safety. link2 Practical Applications The study findings will shape transportation planning and policy level decisions in designing the built environment for safer travels.
School-based first aid training has benefits for adolescents with an opportunity to increase health and safety knowledge relating to injury and cardiac arrest.
Using a quasi-experimental design we evaluated differences among students (M
= 13.46 years, 55% female) taught first aid through the Skills for Preventing Injury in Youth (SPIY) program (n = 1942), treatment-as-usual school-based first aid training (n = 675), and students who did not receive first aid training (n = 489).
Results showed significant differences in self-reported knowledge scores at twelve-month follow-up (controlling for baseline knowledge). Students in the SPIY group and the treatment-as-usual first aid group had higher total scores than the control group. Teachers and students reported positive perceptions to first aid study, in particular the interactive delivery and scenarios for contextualizing information. Practical Applications The study provides support for the retention of first aid knowledge up to 12-months and thus the inclusion and feasibility of first aid training in secondary school curriculum.
Results showed significant differences in self-reported knowledge scores at twelve-month follow-up (controlling for baseline knowledge). Students in the SPIY group and the treatment-as-usual first aid group had higher total scores than the control group. link3 Teachers and students reported positive perceptions to first aid study, in particular the interactive delivery and scenarios for contextualizing information. Practical Applications The study provides support for the retention of first aid knowledge up to 12-months and thus the inclusion and feasibility of first aid training in secondary school curriculum.
The final failure in the causal chain of events in 94% of crashes is driver error. It is assumed most crashes will be prevented by autonomous vehicles (AVs), but AVs will still crash if they make the same mistakes as humans. By identifying the distribution of crashes among various contributing factors, this study provides guidance on the roles AVs must perform and errors they must avoid to realize their safety potential.
Using the NMVCCS database, five categories of driver-related contributing factors were assigned to crashes (1) sensing/perceiving (i.e., not recognizing hazards); (2) predicting (i.e., misjudging behavior of other vehicles); (3) planning/deciding (i.e., poor decision-making behind traffic law adherence and defensive driving); (4) execution/performance (i.e., inappropriate vehicle control); and (5) incapacitation (i.e., alcohol-impaired or otherwise incapacitated driver). Assuming AVs would have superior perception and be incapable of incapacitation, we determined how many crashes would peigners and regulators must address if AVs will realize their potential to eliminate most crashes.
Errors in choosing evasive maneuvers, predicting actions of other road users, and traveling at speeds suitable for conditions will persist if designers program AVs to make errors similar to those of today's human drivers. Planning/deciding factors, such as speeding and disobeying traffic laws, reflect driver preferences, and AV design philosophies will need to be consistent with safety rather than occupant preferences when they conflict. Practical applications This study illustrates the complex roles AVs will have to perform and the risks arising from occupant preferences that AV designers and regulators must address if AVs will realize their potential to eliminate most crashes.
Analyzing key factors of motorcycle accidents is an effective method to reduce fatalities and improve road safety. Association Rule Mining (ARM) is an efficient data mining method to identify critical factors associated with injury severity. However, the existing studies have some limitations in applying ARM (a) Most studies determined parameter thresholds of ARM subjectively, which lacks objectiveness and efficiency; (b) Most studies only listed rules with high parameter thresholds, while lacking in-depth analysis of multiple-item rules. Besides, the existing studies seldom conducted a spatial analysis of motorcycle accidents, which can provide intuitive suggestions for policymakers.
To address these limitations, this study proposes an ARM-based framework to identify critical factors related to motorcycle injury severity. A method for parameter optimization is proposed to objectively determine parameter thresholds in ARM. A method of factor extraction is proposed to identify individual key factors from 2rity analysis.
The framework is applied to a case study of motorcycle accidents in Victoria, Australia. Fifteen attributes are selected after data preprocessing. 0.03 and 0.7 are determined as the best thresholds of support and confidence in ARM. Five individual key factors and four boosting factors are identified to be related to fatal injury. Spatial analysis is conducted by GIS to present hot spots of motorcycle accidents. The proposed framework has been validated to have better performance on parameter optimization and rule analysis in ARM. Practical applications The hot spots of motorcycle accidents related to fatal factors are presented in GIS maps. Policymakers can refer to those maps straightforwardly when decision making. This framework can be applied to various kinds of traffic accidents to improve the performance of severity analysis.
Attitudes toward risky driving behaviors are commonly evaluated through direct self-report measures. Nevertheless, these instruments have limitations, such as socially-desirable responding. This study examines the validity of the Implicit Association Test (IAT) as an indirect measure of attitudes towards risky driving. An IAT with "risky" vs. "safe" driving behaviors categories was evaluated.
A sample of 100 participants (ranging from 18 to 70 years of age) completed the IAT and measures of attitudes, driving styles, personality traits, risk-taking (IOWA Gambling Task), and social desirability (Driver Social Desirability Scale).
A high level of internal consistency was found for IAT scores. The IAT was correlated with driving styles (risky, dissociative, and careful dimensions), risk-related personality traits (impulsive/sensation seeking and aggression/hostility) and risk-taking measures. IAT scores were also associated with self-reported risky driving behaviors (r = 0.33). As expected, a higher level of negative implicit attitudes was found among young drivers. The driver social desirability scale was correlated with most self-report measures, but not with the IAT.
The present study provides reliability and validity evidence for the IAT as an indirect measure of attitudes towards risky driving. The IAT can serve as an important complement to conventional self-report measures of driving attitudes. Practical Applications Potential use of global measure of implicit attitudes toward risky driving behaviors in the evaluation, education, and training of drivers are discussed.
The present study provides reliability and validity evidence for the IAT as an indirect measure of attitudes towards risky driving. The IAT can serve as an important complement to conventional self-report measures of driving attitudes. Practical Applications Potential use of global measure of implicit attitudes toward risky driving behaviors in the evaluation, education, and training of drivers are discussed.
Though previous research has linked personality and workplace safety, results have been inconsistent. Aims of the present study were to understand when and how personality factors predict safety performance.
With 492 working adults, a moderated mediation model was tested whereby the relationship between personality and safety behavior was mediated by safety motivation and moderated by situation strength (i.e., safety climate perceptions).
Findings indicate that, aside from extraversion, safety motivation mediated all relationships between FFM personality traits and safety behavior. The mediated relationship between conscientiousness and safety motivation was attenuated by safety climate perceptions. However, relationships between all other personality traits and safety motivation, and ultimately safety behavior, remained consistent or, in the case of extraversion, was augmented at higher levels of safety climate perceptions.
Results demonstrate an empirical basis for how and when personality translates into safety behavior at work. Additionally, findings provide a theoretical explanation for the mixed results among previous studies of personality's relationship with safety outcomes. Implications are discussed for employee selection and training practices in safety-intensive industries.
Results demonstrate an empirical basis for how and when personality translates into safety behavior at work. Additionally, findings provide a theoretical explanation for the mixed results among previous studies of personality's relationship with safety outcomes. Implications are discussed for employee selection and training practices in safety-intensive industries.
Connected automated vehicles (CAVs) technology has deeply integrated advanced technologies in various fields, providing an effective way to improve traffic safety. However, it would take time for vehicles on the road to vehicles from human-driven vehicles (HDVs) progress to CAVs. Moreover, the Cooperative Adaptive Cruise Control (CACC) vehicle would degrade into the Adaptive Cruise Control (ACC) vehicle due to communication failure.
First, the different car-following models are used to capture characteristics of different types of vehicles (e.g., HDVs, CACC, and ACC). Second, the stability of mixed traffic flow is analyzed under different penetration rates of CAVs. Then, multiple safety measures, such as standard deviation of vehicle speed (SD), time exposed rear-end crash risk (TER), time exposed time-to-collision (TET), and time-integrated time-to-collision (TIT) are used to evaluate the safety of mixed traffic flow on expressways. Finally, the sensitivity of traffic demand, the threshold of time-to-collision (TTC), and the parameters of car-following models are analyzed based on a numerical simulation.
