Search Results (163)

Road traffic accidents are a leading cause of injury and death among adolescents, making road safety education crucial. This study assesses the performance of and users’ opinions on the Route 2 School (R2S) traffic safety education program, designed for secondary school students (13–17 years) in Belgium. The program incorporates gamified e-learning modules containing, among others, podcasts, interactive 360° visuals, and virtual reality (VR), to enhance traffic knowledge, situation awareness, risk detection, and risk management. This study was conducted across several cities and municipalities within Belgium. More than 600 students from school years 3 to 6 completed the platform and of these more than 200 students filled in a comprehensive questionnaire providing detailed feedback on platform usability, preferences, and behavioral risk assessments. The results revealed shortcomings in traffic knowledge and skills, particularly among older students. Gender-based analysis indicated no significant performance differences overall, though females performed better in risk management and males in risk detection. Furthermore, students from cities outperformed those from municipalities. Feedback on the R2S platform indicated high usability and engagement, with VR-based simulations receiving the most positive reception. In addition, it was highlighted that secondary school students are high-risk groups for distraction and red-light violations as cyclists and pedestrians. This study demonstrates the importance of gamified, technology-enhanced road safety education while underscoring the need for module-specific improvements and regional customization. The findings support the broader application of e-learning methodologies for sustainable, behavior-oriented traffic safety education targeting adolescents. Full article

Autonomous vehicles are expected to reduce traffic accident casualties, as driver distraction accounts for 90% of accidents. These vehicles rely on sensors and controllers to operate independently, requiring robust security mechanisms to prevent malicious takeovers. This research proposes a novel approach to assessing the impact of cyber-attacks on autonomous vehicles and their surroundings, with a strong focus on prioritizing human safety. The system evaluates the severity of incidents caused by attacks, distinguishing between different events—for example, a pedestrian injury is classified as more critical than a collision with an inanimate object. By integrating deep neural network technology with feature engineering, the proposed system provides a comprehensive impact assessment. It is validated using metrics such as MAE, loss function, and Spearman’s correlation through experiments on a dataset of 5410 samples. Beyond enhancing autonomous vehicle security, this research contributes to real-world attack impact assessment, ensuring human safety remains a priority in the evolving autonomous landscape. Full article

Pedestrian behaviour and safety are essential components of urban sustainability. They are influenced by a complex interplay between various factors from different perspectives, particularly in culturally and linguistically diverse (CALD) communities. This study presents a comprehensive overview of the factors influencing pedestrian behaviour and safety with a focus on CALD communities. By synthesizing the existing literature, the study identifies six key groups of influencing factors: social–psychological, cultural, risk perceptions, environmental, technological distractions, and demographic differences. It discovers that well-designed interventions, such as tailored education campaigns and programs, may effectively influence pedestrian behaviour. These interventions emphasize the importance of targeted messaging to address specific risks (e.g., using mobile phones while crossing the road) and engage vulnerable groups, including children, seniors, and CALD communities. The study reveals that CALD communities face higher risks of pedestrian injuries and fatalities due to language barriers, unfamiliarity with local road rules, and different practices and approaches to road safety due to cultural differences. This study underlines the importance of developing and promoting tailored road safety education programs to address the unique challenges faced by CALD communities to help promote safer pedestrian environments for all. Full article

Pedestrian safety at crosswalks remains a critical concern as traffic accidents frequently result from drivers’ failure to yield, leading to severe injuries or fatalities. In response, various jurisdictions have enacted pedestrian priority laws to regulate driver behavior. Nevertheless, intersections lacking clear traffic signage and environments with limited visibility continue to present elevated risks. The scarcity and difficulty of collecting data under such complex conditions pose significant challenges to the development of accurate detection systems. This study proposes a CyclicGAN-based loop-learning framework, in which the learning process begins with a set of manually annotated images used to train an initial labeling model. This model is then applied to automatically annotate newly generated synthetic images, which are incorporated into the training dataset for subsequent rounds of model retraining and image generation. Through this iterative process, the model progressively refines its ability to simulate and recognize diverse contextual features, thereby enhancing detection performance under varying environmental conditions. The experimental results show that environmental variations—such as daytime, nighttime, and rainy conditions—substantially affect the model performance in terms of F1-score. Training with a balanced mix of real and synthetic images yields an F1-score comparable to that obtained using real data alone. These results suggest that CycleGAN-generated images can effectively augment limited datasets and enhance model generalization. The proposed system may be integrated with in-vehicle assistance platforms as a supportive tool for pedestrian-crossing detection in data-scarce environments, contributing to improved driver awareness and road safety. Full article

Epidemiological analysis has revealed key insights into the frequency, severity, and circumstances surrounding subway-to-pedestrian incidents; however, there remains a lack of available impact test data specific to this impact type that can be used in modelling and countermeasure design studies. To address this gap, nine controlled impact tests were conducted using a cylindrical headform to derive force–penetration relationships for foam, as well as foam encased in 1 mm aluminium or 3 mm ABS shells. These relationships were validated in MADYMO multibody simulations. Building on a previous multibody computational study of subway-to-pedestrian collisions this research evaluates three passive countermeasure designs using a reduced simulation test matrix: three impact velocities (8, 10, and 12 m/s) and a trough depth of 0.75 m. In subway collisions, due to the essential rigidity of a subway front relative to a pedestrian, it is the pedestrian stiffness characteristics that primarily dictate the contact dynamics, as opposed to a combined effective stiffness. However, the introduction of energy-absorbing countermeasures alters this interaction. Results indicate that modular energy-absorbing panels attached to the train front significantly reduced the Head Injury Criterion (HIC) (by 90%) in the primary impact and pedestrian-to-wheel contact risk (by 58%), with greater effectiveness when a larger frontal area was covered. However, reducing primary impact severity alone did not substantially lower total fatal injury risk. A rail-guard design, used in combination with frontal panels, reduced secondary impact severity and led to the largest overall reduction in fatal injuries. This improvement came with an expected increase in hospitalisation-level outcomes, such as limb trauma, reflecting a shift from fatal to survivable injuries. These findings demonstrate that meaningful reductions in fatalities are achievable, even with just 0.5 m of available space on the train front. While further development is needed, this study supports the conclusion that subway-to-pedestrian fatalities are preventable. Full article

Background: Electric motorized rental scooters (ES) were introduced in Italy in 2019 as an alternative form of urban transportation, aiming to reduce traffic congestion and air pollution. As their popularity has grown, a parallel increase in ES-related injuries has been observed. This study aims to investigate the types and patterns of ES-related injuries and to identify potentially modifiable risk factors. Methods: We conducted a retrospective analysis of all consecutive patients admitted to the Emergency Department of Policlinico Umberto I in Rome between January 2020 and December 2022 following ES-related trauma. Collected data included demographics, injury mechanisms and types, helmet use, Injury Severity Score (ISS), blood alcohol levels, and patient outcomes. Results: A total of 411 individuals presented to the Emergency Department due to ES-related injuries, either as riders or pedestrians. The mean age was 31 years (range: 2–93); 38 patients (9%) were under 18 years of age. Fifty-six accidents (14%) occurred during work-related commutes. Only three riders (0.7%) wore helmets, and nine patients (2%) had blood alcohol levels > 0.50 g/L. Cranial injuries (134 cases, 32%) and upper limb fractures (93 cases, 23%) were the most frequently reported serious injuries. The mean ISS was 4.5; 17 patients (4%) had an ISS ≥ 16. A total of 270 orthopedic injuries and 118 (29%) maxillofacial injuries were documented. Head trauma was reported in 115 patients (28%), with 19 cases classified as severe traumatic brain injuries. Twenty-three patients (5.5%) were hospitalized, three (0.7%) required intensive care, and one patient (0.2%) died. Conclusions: ES-related injuries are becoming increasingly common and present a significant public health concern. A nationwide effort is warranted to improve rider safety through mandatory helmet use, protective equipment, alcohol consumption control, and stricter enforcement of speed regulations. Full article

Road traffic injuries (RTIs) and air pollution present dual burdens that disproportionately affect school-going children in low-income urban settings like Malawi. Despite availability of evidence-based interventions, their implementation often overlooks local contexts and perspectives. This study aimed to elicit stakeholder input on interventions addressing RTIs and air pollution exposure among children in urban Blantyre through stakeholder engagement. It used a mixed method Delphi technique combining expert consultations with community focus groups to achieve consensus on interventions. Successive rounds of prioritization and qualitative discussions explored contextual barriers and facilitators to implementation. Stakeholders identified 40 interventions, 23 for road safety and 17 for air pollution. Measures prioritized by experts included speed limit enforcement, pedestrian infrastructure improvements, and emission controls. Contextual barriers identified by experts and the community included socio-political and financial constraints. Community perspectives emphasized behavioral interventions, while experts highlighted systemic and legislative changes. The study underscored the value of combining expert and community perspectives to design context-sensitive interventions. Synergies between road safety and air pollution interventions offer opportunities for dual benefits but require careful adaptation to urban Malawi’s realities. This study provides a model for participatory design in low-income settings, emphasizing stakeholder engagement for tailored solutions. Full article

Background: Road traffic injuries (RTIs) significantly contribute to disability and death in Qatar. This observational study aimed to explore RTI mortality and injury severity trends from 2011 to 2022. Methods: Data from the national trauma database were analyzed retrospectively for mortality rates, injury severity, and characteristics of the injured populations over the years (2011–2022). Results: RTIs represented around 61.3% (n = 12,644) of 20,642 trauma hospitalizations over 12 years. The aggregate RTI mortality rate decreased from 12 to 8 per 100,000 persons, with a mean patient age of 31.8 years. The sum of deaths was 2464, comprising 1022 (41%) in-hospital and 1442 (59%) out-of-hospital fatalities. Among in-hospital deaths, bike-related mortalities totaled 35 (3%), motorcycle-related mortalities 53 (5%), motor vehicle mortalities 561 (55%), and pedestrian mortalities 373 (36%). Based on the injury severity score (ISS), RTIs were divided into four categories, namely, mild (ISS: 1–9), moderate (ISS: 10–15), severe (ISS: 16–24), and fatal (ISS: 25–75). The ISS ranged from 12 to 14, while the median ranged from 10 to 12. The injury frequency showed that mild injuries comprised 40.6% (4545), moderate injuries 26.2% (2934 subjects), and severe 16.7% (1873 subjects). Profound injuries accounted for 13.3% (1490 subjects). Severe and fatal injuries combined dropped from 30% in 2011 to 25% by 2022. Inversely, moderate injuries increased from 24% to 30%, representing a downward trend of the injury severity. Motorcycle-related injuries rose from around 3% to 28% between 2011 and 2022. Motor vehicle and pedestrian injuries declined from about 67% to 54% and 27% to 15%, respectively. Winter, Autumn, Spring, and Summer accounted for 27%, 26%, 24%, and 23% of the total injuries (11,153), respectively. Conclusions: RTI in-hospital mortality and injury severity decreased over the study period. Injury prevention programs should target frequent injury seasons and high-risk populations, such as motorcyclists. Full article

Pedestrians, as the most vulnerable road users in traffic crashes, prompt transportation researchers and urban planners to prioritize pedestrian safety due to the elevated risk and growing incidence of injuries and fatalities. Thorough pedestrian crash data are indispensable for safety research, as the most detailed descriptions of crash scenes and pedestrian actions are typically found in crash narratives and diagrams. However, extracting and analyzing this information from police crash reports poses significant challenges. This study tackles these issues by introducing innovative image-processing techniques to analyze crash diagrams. By employing cutting-edge technological methods, the research aims to uncover and extract hidden features from pedestrian crash data in Michigan, thereby enhancing the understanding and prevention of such incidents. This study evaluates the effectiveness of three Convolutional Neural Network (CNN) architectures—VGG-19, AlexNet, and ResNet-50—in classifying multiple hidden features in pedestrian crash diagrams. These features include intersection type (three-leg or four-leg), road type (divided or undivided), the presence of marked crosswalk (yes or no), intersection angle (skewed or unskewed), the presence of Michigan left turn (yes or no), and the presence of nearby residentials (yes or no). The research utilizes the 2020–2023 Michigan UD-10 pedestrian crash reports, comprising 5437 pedestrian crash diagrams for large urbanized areas and 609 for rural areas. The CNNs underwent comprehensive evaluation using various metrics, including accuracy and F1-score, to assess their capacity for reliably classifying multiple pedestrian crash features. The results reveal that AlexNet consistently surpasses other models, attaining the highest accuracy and F1-score. This highlights the critical importance of choosing the appropriate architecture for crash diagram analysis, particularly in the context of pedestrian safety. These outcomes are critical for minimizing errors in image classification, especially in transportation safety studies. In addition to evaluating model performance, computational efficiency was also considered. In this regard, AlexNet emerged as the most efficient model. This understanding is precious in situations where there are limitations on computing resources. This study contributes novel insights to pedestrian safety research by leveraging image processing technology, and highlights CNNs’ potential use in detecting concealed pedestrian crash patterns. The results lay the groundwork for future research, and offer promise in supporting safety initiatives and facilitating countermeasures’ development for researchers, planners, engineers, and agencies. Full article

Road traffic accidents (RTAs) are a leading cause of morbidity and mortality worldwide, frequently resulting in traumatic brain injuries (TBIs), skull fractures, and spinal injuries. This manuscript examines the forensic aspects of head trauma caused by RTAs, focusing on the role of autopsy and imaging in diagnosing and characterizing injuries. Through a systematic review of the literature, the study highlights the mechanisms of injury, including high-speed collisions, whiplash, and pedestrian impacts, and explores their pathological consequences, such as subarachnoid hemorrhage, intracranial hemorrhage, and diffuse axonal injury. The differentiation between traumatic and non-traumatic conditions, such as aneurysmal subarachnoid hemorrhage, is emphasized to ensure accurate clinical and forensic assessments. Advances in imaging technologies, particularly virtopsy, are discussed for their potential in non-invasive documentation and analysis of head injuries, while limitations of this approach are acknowledged. Furthermore, the manuscript underscores the importance of preventive measures, including helmet and seatbelt use, vehicle safety innovations, and improved road design, in reducing the incidence and severity of RTAs. By integrating clinical, forensic, and preventive perspectives, this study provides a comprehensive framework for understanding and addressing the burden of head trauma related to RTAs. Full article

Introduction: Detection of blunt traumatic diaphragm injury (TDI) can be challenging in the absence of surgical exploration. Our objective is to study the mechanisms of injury and detection modes for patients with blunt TDI. Methods: This is a single-center, retrospective review conducted in a level 1 trauma center from 2016 to 2023, inclusive. We identified seven patients with blunt TDI using the primary mechanisms and trauma type. Results: Out of seven patients, two were associated with motor vehicle collisions, four were pedestrians struck, and one fell down the stairs. The mean ISS was 48.4 (29–75). Of the seven patients with blunt TDI, four died in the trauma bay–two from traumatic arrest and two died spontaneously. Multiple rib fractures were one of the common injury patterns in six cases, whereas in the remaining case, blunt TDI was confirmed at laparotomy and repaired. One patient died two days after admission. Of the two patients who survived, one had a TDI identified during video-assisted thoracic surgery (VATS) for retained hemothorax, and one patient had a TDI repaired during emergent exploratory laparotomy for other injuries. In the remaining four patients, blunt TDI was confirmed based on their autopsy reports. Conclusions: Injuries in all seven cases were sustained with a high-energy injury mechanism. Multiple rib fractures were reported in six cases. Based on our findings, we recommend that clinicians maintain a high level of suspicion for blunt TDI in patients with thoracoabdominal trauma, especially in cases with rib fractures or high-impact trauma. Full article

In traffic accidents, elderly individuals face a significantly higher risk of mortality compared with other age groups. To investigate the factors contributing to elderly pedestrian accidents and their impact on injury severity, 1420 motor vehicle/elderly pedestrian collisions from the 2019–2023 Chinese Traffic Accident Deep Investigation Database were analyzed using the FP-growth algorithm. This analysis identified 5594 association rules across 28 types of variables within 4 categories of influencing factors. Logistic regression results indicate that pedestrian age, collision speed, time of occurrence, and accident location are significant factors affecting the mortality rate of elderly pedestrians in traffic accidents. Specifically, pedestrian age and collision speed significantly influence mortality rates. As collision speed increases, the mortality rate rises markedly. For elderly pedestrians aged 60 and above, the mortality rate increases by 3.7% with each additional year of age. Moreover, accidents occurring at night, in suburban areas, or in villages are associated with a higher mortality rate. This study offers scientific support for the formulation of safety measures aimed at improving the traffic safety of elderly pedestrians. Full article

Background/Objectives: The incidence of pedestrian traffic injuries is an escalating concern for public health worldwide. Particularly in fast-developing nations, such as Saudi Arabia, these injuries form a significant portion of trauma-related healthcare challenges. This study aims to explore age-specific differences in trends, seasonal variations, and the overall impact of pedestrian traffic injuries in Riyadh, Saudi Arabia, with a focus on injury characteristics and clinical outcomes. Methods: The study conducted a retrospective analysis using data from the Saudi Trauma Registry (STAR) covering the period between August 2017 and December 2022. It employed descriptive statistics, chi-square tests, and multivariable linear regression analyses to explore demographic trends, characteristics of injuries, and hospital-based outcomes. Results: This study analyzed data from 1062 pedestrian injury cases, revealing key demographic and clinical patterns. Most incidents occurred on weekdays (71.9%) and during nighttime hours (63.3%), with seasonal peaks observed from April to June (30.4%). The lower extremities (27.5%) and head (21.3%) were the most frequently injured body regions. ICU admissions were more common among individuals aged 30–40, females, and those with head or chest trauma, while higher in-hospital mortality was associated with patients over 60 years old, transport by private or police vehicles, and extended ICU and hospital stays. Approximately 25.6% of cases required ICU care, with an overall in-hospital mortality rate of 4.9%. Conclusions: This study provides an in-depth analysis of pedestrian traffic injuries treated at a trauma center in Riyadh, highlighting significant demographic, temporal, and clinical patterns. Understanding these trends is essential for optimizing resource allocation and improving emergency care outcomes. Furthermore, the identified age-specific risk factors and seasonal variations underscore the critical need for targeted interventions and policy enhancements to improve road safety and reduce the burden of pedestrian injuries. Full article

This study focuses on an equivalent model of Polyvinyl Butyral (PVB) laminated glass to simulate the Head Injury Criterion (HIC) when a pedestrian collides with a TRAM. To simulate the collision behavior that occurs when a pedestrian’s head collides with PVB laminated glass, a comparison was made between the results of the widely used PLC model for PVB laminated glass modeling and an actual dynamic head impact test. The material properties of the tempered glass and PVB film used in the PLC and equivalent models were obtained via four-point bending tests and tensile tests, respectively. The proposed equivalent model is developed by assigning the thickness, material properties, and positional information of each layer in the multilayer PLC model to the integration points of the shell element. The results of the equivalent analysis model were found to accurately simulate the collision behavior when compared with the results of both the dynamic head impact test and the PLC model. Moreover, the analysis cost improved to approximately 15% of that of the traditional PLC model. Full article

Background: Despite improvements in technology and safety measures, injuries from collisions involving motor vehicles (CIMVs) continue to be prevalent. Therefore, our goal is to investigate the different patterns of head injuries associated with CIMVs. Method: This is a single-center, retrospective study of patients with motor vehicle-related trauma between 1 January 2016–31 December 2023. Patients were identified based on the International Classification of Diseases (ICD) injury codes and the Abbreviated Injury Severity (AIS) for body region involvement. Result: 536 patients met the inclusion criteria. The majority of the injured population includes pedestrians (46.8%), followed by motorcycle drivers (25.2%), bicyclists (18.7%), and motor vehicle drivers (9.3%). The male-to-female ratios for bicyclists and motorcyclists were 13.7:1 and 11.9:1, respectively, which is higher compared with motor vehicle occupants and pedestrians, with ratios of 2.3:1 and 1.5:1. Patients with blunt trauma (99.63%) were higher than penetrating trauma (0.37%). In most cases, the head had the highest AIS score, with a mean of 3.7. Additionally, the median Injury Severity Score (ISS) was 20. Skull fractures were the most prevalent, followed by hemorrhages, lacerations, contusions, and abrasions. Conclusions: The most prevalent injuries were head injuries and fractures. Fractures were the most common, followed by hemorrhage, laceration, contusion, and abrasion. These findings underscore the high incidence of TBI and fractures in such CIMVs, highlighting the need for targeted trauma interventions and the need for injury prevention strategies to mitigate these severe outcomes. Full article