Road traffic accidents (RTAs) impose substantial human, social and economic burdens. This study analysed 78,987 police-reported crashes that occurred in Auckland, New Zealand between 2015 and 2024 to examine how neighbourhood-level socioeconomic deprivation influences crash frequency and severity. Crash outcomes (fatal, serious, minor and non-injury) were assessed in relation to the IMD18, a composite index of deprivation across seven domains (Housing, Access, Crime, Health, Education, Income and Employment). Using regression analyses, strong associations between deprivation and crash frequency were found, with the Housing and Access domains being the most significant predictors of crash rates and related social costs ( > 0.80). This reflects both environmental and systemic conditions - such as inadequate infrastructure, poor transport access and reliance on older vehicles. By linking deprivation domains to crash data, this study highlights the importance of targeted interventions to help reduce road traffic rates and their economic impact in the most affected areas.

Rear-end and side-impact crash risks are the two principal types of multi-vehicle crash risk on freeways. Most previous studies examine a single crash risk type, limiting understanding of their combined effects. This study employs a multilevel structural equation modelling (SEM) framework to investigate the sequential and joint impacts of roadway geometry, dynamic traffic flow, and driving behaviour on multi-type crash risks. The framework was calibrated using 1,762 rear-end and 1,243 lane-changing conflicts from 14 directional sites. The multilevel SEM accounts for site-level heterogeneity to produce more robust estimates. The path analysis identifies two dominant causal chains: 'Horizontal Curve - Density - Car-following Behaviour - Crash Risk' and 'Vertical Slope - Speed Distribution - Lane-changing Behaviour - Crash Risk'. Low-speed fluctuating traffic flow shows higher crash risks than high-speed stable traffic flow. Car-following behaviour increases both rear-end and side-impact risks, while lane-changing activity raises side-impact risk but reduces rear-end risk.

Wearable systems for knee pathology detection and prosthetic control remain constrained by diagnostic limitations or rigid actuation. This study introduces an integrated two-phase framework combining non-invasive screening with adaptive prosthetic control. Phase 1 employs novel time-frequency features (Enhanced Mean Absolute Value/Enhanced Wavelength), achieving 94.7% abnormality detection accuracy Extra Trees classifier,  + 3.16% improvement over conventional features, which is validated through 10-fold cross-validation and rigorous statistical testing (Friedman/Nemenyi, 95% confidence intervals). SHAP analysis yields clinician-interpretable thresholds (e.g. Semitendinosus EMAV > 0.3 mV). Phase 2 utilises multimodal fusion (EMG, FSR, IMU) to achieve 99.2% gait phase accuracy with XGBoost, enabling real-time health-adaptive prosthetic control that dynamically modulates: phase-transition timing (400 ms abnormal vs. 300 ms normal), EMG thresholds (0.15 mV vs. 0.10 mV), and motor gains (2.5× vs. 1.0×) based on pathology status. Validated in a LabVIEW-based control environment across variable terrains and speeds, this end-to-end diagnostics-to-control implementation delivers superior screening accuracy (>4.7% gain vs. deep learning) while enabling context-aware prosthetic adaptation, establishing a new paradigm for accessible musculoskeletal rehabilitation.

This study evaluated how correct use of child protective equipment (child restraint systems, seat belts, and helmets) influences predicted injury severity for children involved in police-reported road crashes. Data from 69,108 participants under 18 years were analyzed, covering occupant, vehicle, roadway, environmental, and protection factors. An XGBoost classifier achieved ROC AUC = 0.8186 with balanced accuracy, precision, and recall. SHAP interpretation identified seating position and participant type as the most influential predictors. Counterfactual simulations, assuming full compliance with protective-equipment use, showed improved predicted outcomes in 64 cases, while 15 worsened. Helmet non-use was the most frequent lapse. Consistent, correct use of protective devices significantly shifts predicted outcomes toward less severe injuries. The explainable machine-learning and counterfactual framework quantifies the benefits of compliance and provides actionable evidence for targeted education, enforcement, and vehicle-safety design. The approach can be extended to other vulnerable groups, including pregnant occupants.

Disproportionately more of the world's fatalities and injuries on the roads occur in low- and middle-income countries, despite these countries having approximately only 60% of the world's vehicles. Injury rates due to motor-vehicles are related to a complex multidimensional array of risk factors, embedded in the social and economic infrastructure of a country or region. Whether environmental infrastructure factors differ in determining the risk of an injury for motor vehicle occupants compared to pedestrians and other vulnerable road users has not been extensively studied. We explored the role of environmental infrastructure factors on motor-vehicle-related non-fatal injury using the Prospective Urban and Rural Epidemiology (PURE) cohort study of 162,793 adults from 23 high-, middle- and low-income countries. As expected, low-income countries had slightly higher motor vehicle injury rates, with pedestrians tending to have higher injury rates in these countries. There was considerable variation in motor vehicle injury rates within country-income-categories, while there were similarities in motor vehicle injury rates despite large differences in motorization of countries. There was a meaningful community effect on motor vehicle injury rates. We found that community-level infrastructure risk factors for motor vehicle injuries differed for car occupants and for pedestrians, with road quality and alcohol use being the main factors associated with an injury for car occupants, while poor roadside infrastructure (streetlights, sidewalks) and alcohol use were the main risk factors for an injury as a pedestrian. Active transport, such as walking and bicycling, are being promoted as leading to healthy lifestyle habits and reduced pollution. These require improved walkability for pedestrians, but also separation from motorized vehicles, which leads to recommending that low-and middle-income countries devote more funds for roadway quality and streetlight infrastructure. Policies to reduce motor vehicle injuries should be supported at the national level, but should be specific at the community level, since they must be focused on the specific local infrastructure. Countermeasures for reducing road transport injuries for pedestrians have different risk factors than for reducing injuries for car occupants.

World Health Organization's (WHO) Healthy Cities Programme (HCP), initiated in 1984, addresses the global health challenges arising from urbanization and globalization within its six regions. In 2021, the Government of India (GOI) recommended to develop 500 Health Cities by 2030, aligning with WHO's HCP. The programme emphasizes addressing social, political, environmental and economic health determinants for policy interventions, guided by existing models of determinants of health (MoDH). However, these models exhibit gaps in capturing determinants in an evolving globalized world. This research conducts content analysis of the proceedings of ten WHO-led Global (GCHP) and international conferences on health promotion (ICHP) to identify the existing and emerging determinants. The integrative literature review of MoDH revealed limitations in addressing emerging legal, technological, and commercial determinants, and spatial scales, thereby informing the development of an updated framework for determinants of health for effective decision-making amidst dynamic global health landscapes.

Road crashes and resulting fatalities are a major concern globally. Low- and Medium-Income Countries (LMIC) contribute to nearly 93% of the global fatalities due to road crashes. In this regard, the present study aims to identify associated factors which influence fatal crashes in the context of an LMIC. Also, it aims to investigate if these associated factors are different for different road categories. The work is carried out by analysing 20,556 police-reported crash data obtained from the state of West Bengal in India. Various factors considered in analysis include roadway characteristics, vehicle characteristics, crash characteristics and human-related factors. The analysis of data using association rules mining reveals that factors associated with fatal crashes vary across different categories of roads. While causal factors on high-speed corridors, i.e. National Highways (NH) and State Highways (SH) show some similarities, such as collision with pedestrians in open area and straight sections, they are substantially different on other roads, such as hitting fixed object, involvement of two-wheeler. However, regardless of road category, speeding and absence of speed limit were found to be important associated factors in all categories of road. The findings derived from the present work may be used advantageously for formulating policy and necessary interventions to reduce fatalities.

The Bedouin population of the Negev experiences the highest child mortality rate from unintentional childhood injury (UCI) in Israel. This study examines the underlying mechanisms of fatal UCI in Bedouin communities and proposes the culturally tailored prevention strategies. Data were collected from multiple sources, including national mortality records, hospitalization and emergency department data, and the Israel Trauma Registry. UCI mortality among Arabs was 2.9 times higher than among Jews, with traffic accidents as the leading cause. Bedouin communities had a 3.14-fold higher UCI mortality rate than other Muslim communities and 2.7 times higher than Arab municipalities with religious heterogeneity. Over half (53.3%) of UCI deaths in Bedouin towns and villages occurred near the home, significantly higher than the national average, often involving toddlers (0-4 years) run over by family members. These findings underscore the need for community-driven, evidence-based interventions to reduce UCI mortality in Bedouin populations and improve child safety in marginalized communities.

This study examined visual attention in children's street-crossing behaviour using a virtual reality (VR) environment with integrated eye-tracking. We hypothesized that older children would spend more time and a higher proportion of time focusing on vehicles, that boys would spend less time looking at vehicles than girls, and that greater visual attention would be associated with fewer dangerous crossings. A total of 377 children aged 7 to 10 completed six VR street-crossing trials, during which their gaze behaviour was recorded and analysed using linear regression. Results showed that older children spent a higher proportion of time looking at vehicles, indicating developmental improvements in attention. Boys spent less total time focusing on vehicles. Greater visual attention to vehicles was associated with fewer dangerous crossings, underscoring its role in pedestrian safety. These findings highlight developmental differences in gaze and the importance of attention to traffic-relevant elements.

Despite advancements in occupational safety management, injury prevention remains a persistent challenge across industries. This study presents a data-driven investigation into severe occupational injuries using publicly available reports from the U.S. OSHA. Employing Association Rule Mining (ARM) combined with thematic analysis, we identify distinct industry-specific injury profiles and uncover interrelated risk patterns. Key findings indicate a prevalence of finger injuries in manufacturing, falls and burns in construction, lower limb injuries in transportation and wholesale sectors, frequent fall-related incidents in retail, burn and hand injuries in mining and high rates of lower back injuries in healthcare settings. The analysis reveals complex co-occurrence patterns among contributing risk factors, such as task type, environmental conditions and body part affected, that influence both the type and severity of injuries. These insights offer valuable guidance for designing targeted, sector-specific safety interventions and underscore the importance of leveraging occupational injury data to inform evidence-based prevention strategies.

Minimizing the losses that occur after traffic accidents is a primary duty for all humanity. To do so, it is necessary to examine and analyse the potential risk factors that affect the severity of traffic accidents. In this article, a new spatial decision-making-based statistical solution methodology is proposed to determine the accident risk factors that occur in three different accident types using 5-year (2015-2019) accident data. (i) 22 independent variables and 157 sub-variables were determined for the traffic accident categories where vehicle-vehicle, vehicle-pedestrian and vehicle-other collision types occurred, (ii) the fuzzy simple weight calculation method was preferred to determine the effects of risk factors on accident categories, (iii) spatial analyses of risk factors were provided geographical information system and combined with the obtained effect values, (iv) the current effect of risk factors on accident categories was tested with the multinomial logistic regression model. The multinomial logistic regression model results revealed a strong model fit (McFadden = 0.749) and identified the variables that significantly increase or decrease the probability of each crash type compared to the reference category. For instance, while the geo-intersection had the highest effect for vehicle-vehicle crashes, the pedestrian defect had the highest impact for vehicle-pedestrian crashes. Spatial analysis results also showed that accident severity tends to be higher in the western, southern, and central regions of Türkiye. The proposed methodology offers a comprehensive framework that supports evidence-based policy development for improving traffic safety. The resulting findings serve as a guide for local administrators, policy makers, and traffic safety experts with regard to vehicle and pedestrian safety.

Road traffic accidents (RTAs) remain a significant public health challenge in India, causing substantial fatalities, injuries, and economic losses. Despite global improvements in road safety, India's performance has been subpar, accounting for a significant portion of the worldwide number of road fatalities. Between 2012 and 2022, RTA-related deaths in India rose by 23%, contrasting with a 5% global decline. This study employs the Seasonal Autoregressive Integrated Moving Average with Exogenous variables (SARIMAX) model to forecast future RTA trends in India, taking into account the COVID-19 pandemic as an external factor. While traditional models, such as SARIMA, effectively capture historical patterns, they often overlook external shocks, including pandemic-induced changes in mobility. By integrating pandemic-related disruptions, the SARIMAX model offers more robust, data-driven forecasts. Analysis of monthly RTA data from 2010 to 2022 suggests that, without intervention, annual crash rates could exceed 440,000 cases. The findings underscore the urgent need for comprehensive measures, including stronger policies, improved infrastructure, stricter law enforcement, and advanced technologies like AI-driven monitoring systems, to enhance road safety in the post-pandemic era.

Human factors play a vital role in the energy industry and often lead to fatal and non-fatal accidents. Therefore, understanding the underlying reasons behind poor safety climate and safety culture is crucial. This study employs a questionnaire method, considering 8 items of safety culture and 57 items of safety climate. Structural equation modeling was conducted and validated using model fit indices. Results indicate that 4 items of safety culture and 26 items of safety climate contribute to safety issues in the utility sector. The weights of each item under safety culture and safety climate provide insights into which factors and items require attention to reduce safety issues. These findings assist managers, supervisors, policymakers and government bodies in implementing necessary measures to mitigate accidents.

The risk level of alcohol-involved traffic crashes is closely related to alcohol consumption. However, research on different influencing factors for DUI (Driving Under Influence) and DWI (Driving While Intoxicated) remains limited. This study analyzed data from 3,365 alcohol-related traffic crashes in Tianjin, China. The crashes were categorized into DUI and DWI based on drivers' Blood Alcohol Concentration. Four machine learning models were enhanced and compared. The accuracy, precision, recall and F1-score were used to evaluate the performance of the models. Shapley additive explanations were used to interpret model outputs and quantify risk factors and interaction effects on DUI and DWI crashes. The enhanced CatBoost model performed the best, with an AUC-ROC value of 0.953. The time period of crashes, intersection control or not, and the density of companies were identified as significant factors affecting DUI and DWI crashes. Interaction analysis indicated that drivers aged between 40 and 50 had a higher risk of DWI in areas with high intersection density; two-wheeled motorcycle riders exhibited higher DWI risk compared to car drivers between 21:00 and 24:00. These findings provide valuable insights for the traffic management department to implement targeted and refined control measures for DUI and DWI violations.

Fourteen million children participate in summer camps annually, yet injury data for these settings remain outdated. Our study aimed to modernize camp injury data collection by leveraging an electronic national camp-specific database to analyze the epidemiology of camp-related injuries. Deidentified data from 89 residential summer camps (2016-2019) were abstracted. Descriptive statistics and multivariable logistic regression analysis were used to determine injury rates and identify risk factors. We identified 13,934 injuries, with an injury rate of 575 injuries per 100,000 camp-days. Common injuries were lacerations/abrasions (37.6%), sprains/strains (27.8%), and head injury/concussions (14.1%). Lower and upper extremity injuries (49.4% and 25.7% respectively) were common. 2.6% ( = 363) of injuries required a higher level of medical care. Older age (adjusted odds ratio [AOR] = 1.2, 95% confidence interval [CI]: 1.1-1.2), male sex (AOR = 1.4, 95% CI: 1.1-1.9), upper extremity injuries (AOR = 3.0, 95% CI: 1.5-6.0), and injuries to head/face (AOR = 2.1, 95% CI: 1.0-4.4) had significantly higher odds of moderate or severe injury. Our study found a higher injury rate than previous research, reflecting the enhanced data collection made possible by utilizing a camp-specific database. Capturing a broad spectrum of injuries provides insights to guide camp stakeholders in developing tailored, data-informed injury prevention strategies.

Traffic disruptions (including frequent and abrupt lane changes in critical merging, diverging and overtaking zones) often result in expressway accidents. This study analysed crash data from the Ethiopian Toll Road Enterprise (2015-2022) using statistical and multinomial logistic regression models to identify high-risk crash locations, assess the severity and investigate the contributing factors in key merging and diverging sections. The analysis considered risk factors such as driver behaviour, traffic patterns, vehicle types, road conditions and lighting. The results indicated a 22.5% increase in accidents on wet pavements compared to dry surfaces across the entire length of the expressway, for a 2.04% increase in traffic volume. Fatalities and severe injuries were more frequent in the merging areas. Over 308 days of rainy weather across 8 years, accidents in the merging and diverging zones were 9.24% more likely to occur on wet roads than on dry surfaces. These observations highlight the increased accident risk caused by frequent and abrupt lane changes under wet conditions, emphasizing the need for improved safety measures in critical areas.

A principal safety issue at highway-rail grade crossings (HRGCs) is the severity of crashes. Although many studies have analyzed crash severity at HRGCs, they often rely on national datasets or a narrow set of variables, frequently overlooking region-specific factors such as roadway design, driver behavior, and local environmental conditions. However, this study contributes to the existing body of literature by providing additional insights into the factors associated with injury severity in HRGC crashes. This study aimed to model HRGC crash severity using statistical and machine learning methods, specifically Ordinal Logistic Regression (OLR) and Random Forest (RF) algorithms, to determine significant factors associated with severe injury HRGC crashes. The statistical modeling and analyses were based on five years of HRGC crash data (2017-2021) at state-maintained HRGCs in Florida. Based on the OLR statistical model, ten variables were significant at a 95% confidence interval: crashes that occurred in the morning peak hours, no lighting condition, adverse weather conditions, railway vehicle (i.e. train or train engine), driver action (i.e. disregarded signs, signals, markings as well as other contributing actions), a speed limit of greater than 45 mph, four-lane highways, driver younger than 25, female drivers, crashes that occurred at the railroad crossings, and estimated vehicle damage of more than $1,000. Results from the OLR model indicate that all significant variables increase the likelihood of an HRGC crash being more severe, except for the time of crash occurrence (morning peak), adverse weather conditions, and drivers under 25 years of age. According to the RF model, the most important (top five) factors affecting the injury severity of HRGC crashes include estimated vehicle damage, posted speed limit, type of shoulder, driver action, and crash type. Except for the type of shoulder and crash type, the RF model results are consistent with those of the OLR model. Finally, based on the model results, potential countermeasures to mitigate fatalities and injuries at HRGCs were presented.

Active traveller (including pedestrians and bicyclists) crashes pose significant challenges to sustainable transportation. Active traveller injury severities not only demonstrate temporal variations, but also differ across different functional zones within the city. Therefore, conducting a spatiotemporal analysis to understand the impact of various factors on active traveller injury severities can help develop effective strategies aimed at mitigating these severities. However, most existing studies mainly focus on temporal instability from year to year, ignoring the spatial difference between rural and urban areas. To examine spatiotemporal instability, this study uses North Carolina as a case study and divides the six-year (2017-2022) active traveller crashes into four sub-datasets according to distinct spatial and temporal characteristics. An explainable and balanced machine learning framework is designed to address the challenges associated with small sample size and imbalanced crash data and explore factors affecting active traveller injury severities. Results demonstrate that spatial instability has a greater impact than temporal instability. For instance, non-intersection, bicycle and travel lanes, medium speed limit and dark with light conditions are important in urban areas, but crosswalk areas are significant in rural areas. These results can help policymakers develop region-specific countermeasures to promote the reliability of active transportation systems.

Safety culture, defined as the shared values, attitudes and behaviours toward workplace safety, plays a vital role in preventing accidents and ensuring workforce well-being. This article presents a novel method for assessing safety culture using the Hazard and Operability Study (HAZOP), a structured approach for identifying and mitigating process-related risks. We propose that HAZOP can be effectively applied to analyze an organization's Integrated Vibrant Safety Management System (IVSMS) and develop a Safety Culture Index (SCI). The IVSMS comprises 21 elements, including Industry 4.0, Process Safety Management, and Occupational Safety and Health, offering a comprehensive view of safety practices. While these elements are typically weighted equally, our approach accounts for their varying impacts on safety performance, enabling more targeted interventions. These weightings can be adapted to suit different organizations. By evaluating each element through HAZOP, we can uncover strengths and gaps in risk management, communication and mitigation. The resulting SCI provides a quantifiable measure of safety culture, supporting benchmarking and continuous improvement. Strengthening safety culture through this method not only enhances safety outcomes but also contributes to organizational resilience and success.

Pedestrian fatalities remain a significant global concern, particularly in low- and middle-income countries, where road safety measures are often lacking. In urban areas across Africa, pedestrian safety is especially critical, yet research into pedestrian behaviour in these settings is limited. This gap is addressed by applying cluster analysis to explore the role of socio-demographic factors, such as gender and education, in shaping pedestrian safety and risk perceptions. Using the Pedestrian Behaviour Scale, we identified four distinct clusters: Cluster 1, the highest-risk group, exhibited high levels of irresponsibility, recklessness, and aggressive behaviours, particularly among males. Cluster 4 represented law-abiding pedestrians with high compliance with traffic regulations. To examine the influence of socio-demographic factors, we conducted independent sample -tests and Analysis of Variance, revealing significant variations in violation and error scores across demographic groups. Valuable insights are provided for urban planners and policymakers, offering data-driven recommendations to improve pedestrian safety in rapidly urbanising regions. By filling a critical gap in pedestrian safety research, it lays the groundwork for more effective interventions to reduce pedestrian fatalities and promote safer road environments in developing countries.