International Journal of Injury Control and Safety Promotion最新文献

This paper investigates the factors influencing the severity of driver injuries in single-vehicle speeding-related crashes, by comparing different driver age groups. This study employed a random threshold random parameter hierarchical ordered probit model and analysed crash data from Thailand between 2012 and 2017. The findings showed that young drivers face a heightened fatality risk when speeding in passenger cars or pickup trucks, hinting at the role of inexperience and risk-taking behaviours. Old drivers exhibit an increased fatality risk when speeding, especially in rainy conditions, on flush median roads, and during evening peak hours, attributed to reduced reaction times and vulnerability to adverse weather. Both young and elderly drivers face escalated fatality risks when speeding on road segments lacking guardrails during adverse weather, with older drivers being particularly vulnerable in rainy conditions. All age groups show an elevated fatality risk when speeding on barrier median roads, underscoring the significant role of speeding, which increases crash impact and limits margins of error and manoeuvrability, thereby highlighting the need for safety measures focusing on driver behaviour. These findings underscore the critical imperative for interventions addressing not only driver conduct but also road infrastructure, collectively striving to curtail the severity of speeding-related crashes.

Cell phone use while walking escalates pedestrian safety risks, particularly among students, given their heavy reliance on these devices. Psychological factors wield significant influence on individual behavior, particularly among adolescents who navigate a blend of rational and social-reactive pathways in decision-making. Therefore, this paper employs the Prototype Willingness Model (PWM) to scrutinize psychological determinants of students' cell phone use while walking, drawing on data from 368 valid samples. Structural equation modeling (SEM) dissects the impact of psychological constructs on this behavior, highlighting the significance of both the social response and reasoning pathways within PWM. The study comprehensively analyzes direct, indirect, and overall effects of exogenous variables on behavioral intention and behavior.

Delivery riders are more vulnerable than other traffic participants, especially in vehicle-involved delivery crashes. This study aims at identifying the unobserved heterogeneities in different factors, based on 4251 vehicle-scooter-style electric bicycle (SSEB) crashes. First, some potential factors are selected from seven perspectives, and the spatiotemporal characteristics are analysed. Second, a latent class clustering method is proposed to clarify the optimal number of clusters by maximizing the heterogeneities across clusters. Third, partial proportional odds (PPO) models for the whole dataset and sub-datasets are developed to explore the heterogeneities across various clusters. Besides, marginal effects are implemented to quantify the heterogeneities. The results evidence that there are remarkable heterogeneities across different clusters, especially in riding behaviours and road conditions. Several factors only significantly affect particular clusters but not the whole dataset. The PPO models for the sub-datasets perform better in identifying the underlying heterogeneities. The results also highlight the greater roles of riding behaviours and road conditions in delivery SSEB-vehicle crashes. The top five influencing factors are running red light, using cell phones, vehicle type, reverse riding and bike lane (their maximum marginal effects exceeding +35%). The findings could support to mitigate the related crash losses.

Road traffic injuries (RTIs) are a significant cause of death and disability among young people worldwide. Programs that use injury prevention strategies have been shown to effectively reduce the number of injuries. This systematic review aims to present the available evidence on the effectiveness of intervention programs in preventing RTIs among adolescents and young adults. Articles were identified and retrieved from databases including PubMed, Embase, Web of Science, Scopus, Science Direct, Magiran, SID, and Iran Medex. Two reviewers independently screened the articles. Articles published from the first year of publication until January 2018 that met the eligibility criteria were included in the review. These articles covered randomized controlled trials, pretest/post-test interventions, and controlled pretest/post-test interventions aimed at reducing RTIs among adolescents and young people aged 12-26 years. The framework, provided by Murphy and Haddon, were used to categorize the interventions. According to this framework, the interventions were categorized into five groups including education/behavior change, incentive, engineering/technology, legislation/enforcement, and multifaceted programs. The methodological quality of the studies was assessed using the Effective Public Health Practice Project. Of the initial 3165 findings, 13 studies met the inclusion criteria. Eleven of these studies used educational/behavioral approaches, while two employed multifaceted programs. In the educational/behavioral approach, participants' behaviors were considered as outcome variables. Only one of these studies was not successful. Multifaceted interventions were successful in changing behaviors. No studies were found on the effectiveness of other interventions. Eight studies were assessed as having moderate quality. The systematic review shows that educational/behavioral strategies and multifaceted programs are effective in reducing RTIs among young adults and adolescents. However, there is a lack of studies on other potential interventions. The quality of the included studies was moderate, suggesting a need for more rigorous research.

Construction workers face a high risk of various occupational accidents, many of which can result in fatalities. This study aims to develop a prediction model for nine prevalent types of construction accidents, utilizing construction tasks, activities, and tools/materials as input features, through the application of machine learning-based multi-class classification algorithms. 152,867 construction accident summary reports, composed of both structured (construction task, construction activity, accident type) and unstructured data (tools/materials) were used for the study. The study employed several data processing techniques, including keyword extraction through text mining, Boruta feature selection, and SMOTE data resampling enhance model accuracy. Three performance metrics (Multi-class area under the receiver operating characteristic curve (MAUC), Multi-class Matthews Correlation Coefficient (MMCC), Geometric-mean (G-mean)) were used to compare the predictive performance of four machine learning algorithms, including Decision tree, Random forest, Naïve bayes, and XGBoost. Of the four algorithms, XGBoost showed the highest performance in predicting accident type (MAUC: 0.8603, MMCC: 0.3523, G-mean: 0.5009). Furthermore, a Shapley additive explanation (SHAP) analysis was conducted to visualize feature importance. The findings of this study make a valuable contribution to improving construction safety by presenting a prediction model for accident types derived from real-world big data.

This paper studies the main factors affecting road traffic accidents (RTAs) using a systematic review. The primary focus is on factors related to road characteristics and driver behaviours. This review also addresses the socioeconomic and demographic factors to provide a clear overview of which groups suffer the most from RTAs. Several factors were found to affect RTAs, notably road characteristics: highways, high-speed roads, unplanned intersections and two-way roads without dividers; driver behaviours: reckless/aggressive driving and riding, excessive speeding, unawareness of traffic laws, and not using safety equipment; and vehicle types: four and two-wheeled. This review found that male and economically productive people with less education were mostly associated with RTAs. In addition, for most of the low and middle-income countries analyzed, there is a lack of quality data relating to RTAs. Nevertheless, this review provides researchers and policy makers with a better understanding of road accidents for improving road safety.

Over the past decade, rural crashes have been responsible for an average of 65% of crash-induced casualties in Iran. Evidence from prior studies reveals that a significant number of these rural crashes occur at marginal areas around cities. Thus, Exclusive crash severity models should be developed to identify the factors associated with higher injury and fatality probabilities in these areas. In this study, a partial proportional odds (PPO) model was formulated using the rural crash data collected from roads leading to the city of Isfahan. The PPO model holds the ordinal nature of crash observations and allows for different influences of independent variables on various crash severity levels. Insights derived from the results reveal that factors such as vehicle traffic maintaining an average speed exceeding 95 km/h, the occurrence of multi-vehicle crashes, the incidence of overturn-type crashes, the at-fault vehicle being a truck/trailer and at-fault or not-at-fault vehicle being a motorcycle, increase the likelihood of more severe rural crashes. Conversely, a foreign vehicle being at-fault, and the driver of the at-fault vehicle aged between 30 and 40 years, tend to diminish the occurrence of severe crashes at marginal areas around cities.

Drunk-driving events often escalate into drunk-driving crashes, however, the contributing factors of this progression remain elusive. To mitigate the likelihood of crashes stemming from drunk-driving events, this paper introduces the notion of 'the severity of drunk-driving event' and examines the complex relationship between the severity and its contributing factors, considering spatiotemporal heterogeneity. The study utilizes a Geographically and Temporally Weighted Binary Logistic Regression (GTWBLR) model to conduct spatiotemporal analysis based on police-reported drunk-driving events in Beijing, China. The results show that most factors passed the non-stationary test, indicating their effects on the severity of drunk-driving event vary significantly across different spatial and temporal domains. Notably, during non-workday, drunk-driving events in northeast of Beijing are more likely to escalate into crashes. Furthermore, severe weather during winter in the northwest of Beijing is associated with high risk of drunk-driving crashes. Based on these insights, the authorities can strengthen drunk-driving checks in the northeast region of Beijing, particularly during non-workdays. And it is crucial to promptly clear accumulated snow on the roads during severe winter weather to improve road safety. These insights and recommendations are highly valuable for reducing the risk of drunk-driving crashes.