International Journal of Injury Control and Safety Promotion最新文献

The study determined the prevalence and factors associated with unintentional injuries among under- fives in Kgalagadi South. A descriptive cross- sectional study was conducted between November 2020 and February 2021 using a multi- stage sampling technique to recruit 389 guardians with children aged five years and below. Self-administered questionnaires were used for the literate population, while interviewer-administered questionnaires were used for the population with limited literacy to elicit information from the guardians. Chi- square, univariate and multivariate logistic regression were used for inferential analysis. The prevalence of unintentional injuries was 48.8%. There was a significant association between injury occurrence and guardians' employment status, alcohol consumption and non- medicinal drug use by the guardian, cigarette use by the guardian, household income level, gender and age of the child who got injured. The guardians age and cigarette use by the guardian were identified as independent risk factors for injury occurrence among under- five children. The findings highlight the need for policymakers and practitioners to develop policies that support comprehensive health education for caregivers on childhood injury prevention and creating a safe environment for children.

Even though accidents with pedestrians are more likely to have a severe or fatal outcome, most research concentrates on the vehicle part of the accidents. To make better policies and infrastructure decisions it is crucial to understand how and why such accidents happen. In this article, 2.588 pedestrian accidents from the year 2018 that resulted in injury or death in the city of Ankara, Türkiye are considered, and different attributes are analyzed co-location-wise. Three types of analysis will be undertaken for these accidents: temporal, natural environment, and pedestrian characteristics. The season of the year, weekend-weekday, and time of day will be explored for the temporal analysis. Visibility (daylight, twilight, and darkness) and rain are the natural surroundings of interest. The analyzed pedestrian characteristics are age group, gender, clothing colour, and nationality. Generally, the accident properties are evenly distributed and mostly the higher co-locations occur with themselves. Three important results stand out in this study. Firstly, the type of precaution appropriate to different time periods of the day should be applied, taking into account the locations that have the strongest co-location with themselves. Secondly, a seasonal approach should be adopted in determining places where pedestrian safety needs to be increased in the city. Lastly, pedestrian safety measures targeting especially both '0-14 and 65 + age groups' and 'foreign citizenship' should be prioritized in the identified locations. The results show that the co-location methodology is a good fit for analyzing pedestrian accidents and a wider use of this methodology for other accident types and in general might be beneficial.

Understanding the nature of traffic accidents in relation to urban access networks is crucial for building safer and more resilient cities. This paper examines the issue of traffic accidents through the lenses of urban configurational theory and urban land use. Three data layers were used in the study, including space syntax analysis conducted in Depthmap X, geotagged traffic accidents collected by the police department, and geotagged land-use data. The method involved superimposing these data layers and exploring potential correlations using a geographic information system (GIS). The findings indicate significant correlations between the spatial frequency of traffic accidents and the choice measure (at 2500 m), local integration, and active land use. The findings of this study can help inform planners and policymakers about the best location to implement safety measures to reduce the risk of traffic accidents in urban access networks.

Machine learning (ML) models are widely employed for crash severity modelling, yet their interpretability remains underexplored. Interpretation is crucial for comprehending ML results and aiding informed decision-making. This study aims to implement an interpretable ML to visualize the impacts of factors on crash severity using 5 years of freeways data from Iran. Methods including classification and regression trees (CART), K-nearest neighbours (KNNs), random forest (RF), artificial neural network (ANN) and support vector machines (SVM) were applied, with RF demonstrating superior accuracy, recall, F1-score and ROC. The accumulated local effects (ALE) were utilized for interpretation. Findings suggest that light traffic conditions ($\text{volume}/\text{capacity}<0.5$) with critical values around 0.05 or 0.38, and higher proportion of large trucks and buses, particularly at 10% and 4%, are associated with severe crashes. Additionally, speeds exceeding 90 km/h, drivers younger than 30 years, rollover crashes, collisions with fixed objects and barriers, nighttime driving and driver fatigue elevate the likelihood of severe crashes.

This study examines the impact of urban form and street infrastructure on pedestrian safety in Atlanta, Georgia, and Boston, Massachusetts. With a significant rise in pedestrian fatalities over the past decade, understanding how cities' built environments influence safety is critical. We conducted geospatial analyses and statistical tests, revealing unique patterns in each city. Atlanta's sprawling, motorist-oriented layout is associated with increased pedestrian accidents, particularly at crosswalks, due to limited land use diversity, arterial roads, and streets with high speed limits and multiple lanes. In contrast, Boston's compact, pedestrian-oriented design leads to improved safety, featuring safer pedestrian crossings, greater land use diversity, reduced arterial roads and lower speed limits on single-lane streets. This study also highlights the importance of diverse urban forms and pedestrian infrastructure in shaping pedestrian safety. While population density and land use diversity impact accident rates, the presence of crosswalks and street configurations play crucial roles. Our findings underscore the urgency for urban planners to prioritize pedestrian safety through targeted interventions, such as enhancing crosswalks, reducing speed limits and promoting mixed land use. Future research should explore additional variables, alternative modelling techniques and non-linear approaches to gain a more comprehensive understanding of these complex relationships.

A well-developed road network plays a crucial role in fostering social and economic progress within a region. However, road crashes resulting in massive injuries and deaths profoundly affect socioeconomic development. There is a need therefore to identify working approaches used in road safety strategic management which provide evidence and a foundation to achieve safer road transport. This may be achieved through a systematic literature review considering both state-of-the-art technologies and best practice. Such a review is presented in this paper. The review involved searching twenty-six bibliographic databases and twenty-four websites of road-related organizations. Following the EPPI-Reviewer methodology, the researchers identified 30 studies that demonstrated various methods employed in the strategy development process. The review highlighted the prevalence of information technology in crash data analysis, particularly concerning big data applications. Moreover, existing resource allocation methods primarily focus on local countermeasures prioritization and ranking based on benefit cost analysis. However, the review identified a gap in comprehensive crash database understanding, and only a few single-objective optimization methods have been developed for strategy development, while there is a need for data mining methods and multi-objective optimisation methods supported by expert knowledge.

As the popularity of electric bicycles (e-bikes) continues to surge, the number of accidents involving them has commensurately increased. A significant factor contributing to the high fatality rate in these accidents is the low usage of helmets among e-bike riders. Helmets have been proven to reduce the severity of injuries, yet their usage remains unexpectedly low. This issue is particularly pronounced among college students, the primary buyer group for e-bikes. Regrettably, there is a lack of research exploring their intentions to wear helmets. Understanding determinants of their intentions to wear helmets is crucial in promoting safe e-bike travel. Therefore, the present study aims to develop an integrated theoretical model that combines the Theory of Planned Behavior (TPB) and the Health Belief Model (HBM) to examine the factors influencing e-bike riders' helmet-wearing intentions among college students. Additionally, two variables-descriptive norms and law enforcement-are incorporated. The results indicate that the integrated model accounts for 76% of the variance in helmet-wearing intention, surpassing single-theory models. Specifically, the TPB accounts for 65%, while the HBM explains 53%. Notably, law enforcement emerges as the most influential factor, highlighting the crucial role of enforcing regulations and promoting awareness. Other significant factors include subjective and descriptive norms, attitudes, perceived benefits, perceived susceptibility, perceived barriers, and perceived severity. These findings provide valuable insights for policy development and targeted interventions aimed at improving helmet wear rates among e-bike riders, especially among the college student population.

This study aims to classify motorcycle (MC) following distance based on trajectory traffic data and identify the risks associated with MC following distances to prevent rear-end collisions. A total of 8,223 events of a MC following a vehicle were investigated in Pathum Thani, Thailand, and 41 cases of MC rear-end crashes were analyzed between 2017 and 2021. Time headway (TH), safe stopping distance (SSD) and time to collision (TTC) were applied to the proposed concept to determine safe following distance (SFD). Speed and following distance for actual rear-end crashes were applied to validate SFD. Results showed that the proposed SFD model identified the causes of MC rear-end collision events as mostly due to longitudinal critical area (38 cases, 92.68%), implying insufficient MC rider reaction and decision time for evasive action. The longitudinal warning area had relatively few chances for rear-end collisions to occur, with only 3 cases recorded. VDO clip extracts from MC rear-end crashes illustrated 11 cases (26.83%) of rider fatality. The study findings revealed that the SFD concept can help to prevent MC rear-end collision events by developing reminder systems when the rider reached the following distances of both warning and critical areas.