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Traffic accidents have become severe risks as they are one of the causes of enormous deaths worldwide. Reducing the number of incidents is critical to saving lives and achieving sustainable cities and communities. Machine learning and data analysis techniques interpret the reasons for car accidents and propose solutions to minimize them. However, this needs to take the benefits of big data solutions as the size and velocity of traffic accident data are increasingly large and rapid. This paper explores road car accident data patterns and proposes a predictive model by investigating meaningful data features, such as accident severity, the number of casualties, and the number of vehicles. Therefore, a pre-processing model is designed to convert raw data using missing and meaningless feature removal, data attribute generalization, and outlier removal using interquartile. Four classification methods, including decision trees, random forest, multinomial logistic regression, and naïve Bayes, are used and evaluated to study the performance of road accident prediction. The results address acceptable levels of accuracy for car accident prediction except for naïve Bayes. The findings are discussed through a data-driven approach to understand the factors influencing road car accidents and highlight the key ones to propose accident prevention solutions. Finally, some strategies are provided to achieve healthy and community-friendly cities.

Car accidents are among the most common causes of fetal trauma during pregnancy. The most frequent maternal complications include placental abruption, uterine rupture, and hypovolemic shock, while fetal complications include premature birth, cranial injuries, skull fractures, and even death. To examine how car accidents affect pregnant women and their newborns. Three cases of newborns delivered by mothers involved in car accidents during pregnancy were analyzed. The data were collected from the medical records of the neonatology department. These three clinical cases demonstrate the significant impact of maternal trauma from car accidents on perinatal outcomes and neonatal development, with a wide range of clinical manifestations, from transient neonatal complications to neonatal death. Properly using seatbelts during pregnancy is essential to prevent injuries to both the mother and the fetus. Maternal motor vehicle accidents can have severe and diverse consequences for newborns, ranging from transient complications to congenital malformations and neonatal death. Proper and correct use of seatbelts during pregnancy is a critical preventive measure to reduce maternal and fetal injuries. Immediate neonatal resuscitation and thorough post-trauma evaluation, are essential for improving outcomes. Long-term pediatric monitoring is recommended due to the risk of delayed complications. Further research is needed to develop standardized protocols for trauma management in pregnancy and to better understand the effects of intrauterine trauma on fetal development.

With an increase in population, there is an increase in the number of accidents that happen every minute. These road accidents are unpredictable. There are situations where most of the accidents could not be reported properly to nearby ambulances on time. In most of the cases, there is the unavailability of emergency services which lack in providing the first aid and timely service which can lead to loss of life by some minutes. Hence, there is a need to develop a system that caters to all these problems and can effectively function to overcome the delay time caused by the medical vehicles. The purpose of this paper is to introduce a framework using IoT, which helps in detecting car accidents and notifying them immediately. This can be achieved by integrating smart sensors with a microcontroller within the car that can trigger at the time of an accident. The other modules like GPS and GSM are integrated with the system to obtain the location coordinates of the accidents and sending it to registered numbers and nearby ambulance to notify them about the accident to obtain immediate help at the location.

Road traffic accidents (RTAs) are a leading cause of physical injury worldwide, but they also frequently result in post-traumatic stress disorder (PTSD). This systematic review examines the prevalence, predictors, comorbidity, and treatment of PTSD among RTA survivors. Four electronic databases (PubMed, Scopus, EBSCO, and ProQuest) were searched following PRISMA 2020 guidelines. Articles were included if reporting on the presence of post-traumatic stress disorder as a result of a road traffic accident in adults aged 18 years and older. Including peer-reviewed journal articles and awarded doctoral theses across all publication years, and written in English, Macedonian, Serbian, Bosnian, Croatian, and Bulgarian, identified 259 articles, and using Literature Evaluation and Grading of Evidence (LEGEND) assessment of evidence 96 were included in the final review, involving 50,275 participants. Due to the heterogeneity of findings, quantitative data were synthesized thematically rather than through meta-analytic techniques. Findings are reported from Random Control Trial (RCT) and non-RCT studies. PTSD prevalence following RTAs ranged widely across studies, from 20% (using Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, DSM-5 criteria) to over 45% (using International Classification of Diseases, 10th Revision, ICD-10 criteria) within six weeks post-accident (non-RCT). One-year prevalence rates ranged from 17.9% to 29.8%, with persistence of PTSD symptoms found in more than half of those initially diagnosed up to three years post-RTA (non-RCTs). Mild or severe PTSD symptoms were reported by 40% of survivors one month after the event, and comorbid depression and anxiety were also frequently observed (non-RCTs). The review found that nearly half of RTA survivors experience PTSD within six weeks, with recovery occurring over 1 to 3 years (non-RCTs). Even minor traffic accidents lead to significant psychological impacts, with 25% of survivors avoiding vehicle use for up to four months (non-RCT). Evidence-supported treatments identified include Cognitive Behavioural Therapy (CBT) (RCTs and non-RCTs), Virtual Reality (VR) treatment (RCTs and non-RCTs), and Memory Flexibility training (Mem-Flex) (pilot RCT), all of which demonstrated statistically significant reductions in PTSD symptoms across validated scales. There is evidence for policy actions including mandatory and regular psychological screening post RTAs using improved assessment tools, sharing health data to better align early and ongoing treatment with additional funding and access, and support and interventions for the family for RTA comorbidities. The findings underscore the importance of prioritizing research on the psychological impacts of RTAs, particularly in regions with high incident rates, to understand better and address the global burden of post-accident trauma.

Road traffic accidents (RTAs) in Northwest Ethiopia, a region with a fatality rate of 32.2 per 100,000 residents, pose a critical public health challenge exacerbated by infrastructural deficits and environmental hazards. This study leverages machine learning (ML) to predict accident severity, addressing gaps in localized predictive frameworks for low- and middle-income countries (LMICs). Our study aims to predict the severity of car accidents in Northwest Ethiopia via machine-learning techniques. Using a dataset of 2,000 accidents (2018–2023) from police reports, we integrated driver demographics, behavioral factors (e.g., alcohol use, seatbelt compliance), and environmental conditions (e.g., unpaved roads, weather) in North West Ethiopia. Ten ML models, including Random Forest, XGBoost, and LightGBM, were evaluated after addressing class imbalance via the Synthetic Minority Oversampling Technique (SMOTE). Hyperparameter tuning and Shapley Additive explanations (SHAP) provided model optimization and interpretability. Random Forest outperformed other models, achieving 82% accuracy (AUC-ROC: 0.87) post-tuning. Driver age (mean: 44 years) and environmental factors (e.g., nighttime on unlit roads, rainy conditions) were critical predictors, increasing fatal accident likelihood by 62%. SMOTE improved the accuracy of the outperforming random forest accuracy from 78.6 to 82%. Random Forest exhibited the highest recall (0.82) after optimization, while ensemble methods dominated performance metrics. The study underscores the efficacy of ML in contextualizing accident severity in LMICs, with Random Forest emerging as a robust tool for policymakers. Prioritizing road paving, sobriety checkpoints, and motorcycle safety could mitigate risks, aligning with Sustainable Development Goal 3.6. Future work should address data limitations (underreporting, geospatial gaps) and expand model interpretability.

Until now, there have been no published attempts to utilize ensemble learning approaches to pre-crash velocity estimation. In this research article, we focus on the method of vehicle crash velocity prediction based on the random forest regression approach. In particular, the study aims to develop and validate a random forest-based non-linear model for estimating pre-crash velocity using EES-related parameters for compact vehicles in a crash scenario against an immovable, stationary barrier. The estimation technique is trained and evaluated using the compact vehicle class from the NHTSA database, which consists of 399 records of frontal impacts against a rigid barrier. The relative error obtained for the presented calculation method is 7.57%, with absolute error being equal to 1.12 m/s. We subsequently compare our results with some other techniques which were tested on this dataset. Despite the simplicity of random forest regression, we obtain surprisingly good results, as the method outperforms linear regressor and artificial neural network predictors, which have relative errors of 8.17% and 9.63%, respectively. The independence of Event Data Recorders along with the ease of obtaining the necessary data makes the proposed approach a highly desirable tool in forensic analysis, especially in cases involving older vehicles.

•Authors use artificial neural network to find the precrash velocity in intermediate car class.•This approach is showing improvement over linear method-9% vs up to 18%.•The perceptron can be gradually improved and fine tuned producing even more accurate estimations. This paper introduces a new, innovative approach to pre-crash velocity determination, namely the artificial neural networks. A perceptron based on a database obtained from NHTSA (National Highway Traffic Safety Administration) with numerous data concerning frontal vehicle crash tests: i.e. vehicle mass, deformation zone and deformation coefficients C1-C6. Part of the database entries were used to train the network to develop consistent accuracy and the remainder was used as validation and training sets.

Car accident detection plays a crucial role in video-based traffic surveillance systems, contributing to prompt response and improved road safety. In the literature, various methods have been investigated for accident detection, among which deep learning approaches have shown superior accuracy compared to other methods. The popularity of deep learning stems from its ability to automatically learn complex features from data. However, the current research challenge in deep learning-based accident detection lies in achieving high accuracy rates while meeting real-time requirements. To address this challenge, this study introduces a deep learning approach using convolutional neural networks (CNNs) to enhance car accident detection, prioritizing accuracy and real-time performance. It includes a tailored dataset for evaluation, and the F 1-scores reveal reasonably accurate detection for “damaged-rear-window” (62%) and “damaged-window” (63%), while “damaged-windscreen” exhibits exceptional performance at 83%. These results demonstrate the potential of CNNs in improving car accident detection, particularly for certain classes. Following extensive experiments and performance analysis, the proposed method demonstrates accurate results, significantly enhancing car accident detection in video-based traffic surveillance scenarios.

Whiplash-Associated Disorders (WADs) often result from traffic accidents, leading to persistent symptoms, including neck pain, disability, dizziness, and central sensitization (CS). A key concern is cervical range of motion (cROM) impairment and sensorimotor dysfunction, which contribute to prolonged disability. This study assessed functional performance in individuals with subacute (>1, <3 months) WADs (n = 122) compared to healthy controls (n = 45). Clinical measures included cROM, movement control (Butterfly test), and position sense (Head–Neck Relocation Test, HNRT). Patient-reported outcomes included neck disability, pain intensity, central sensitization, and dizziness. Mixed and linear models evaluated group differences and the influence of demographic and symptom-related factors. WAD patients had significantly reduced cROM and impaired movement control (p < 0.001). Neck disability (p < 0.001) and pain intensity (p = 0.015) affected cROM within the WAD group. Interaction effects revealed greater amplitude accuracy (AA) impairments at greater difficulty levels (p = 0.043), while time on target (TOT) differences decreased (p < 0.001). Dizziness was associated with increased undershoot (p < 0.001), while pain negatively impacted both AA (p = 0.003) and TOT (p = 0.037). Position sense did not differentiate WAD patients from controls. Findings suggest task-dependent sensorimotor deficits, highlighting the need for multimodal assessment. Early CS screening may optimize rehabilitation and prevent chronic disability.

The purpose of this case report was to demonstrate a fatal motor vehicle accident in which a 33-year-old man died at the scene immediately after his car collided with the roadside curbstone at a normal speed. The autopsy of the deceased body revealed a penetrating injury on the neck as he was struck by the deployed airbag. Pathological examination showed the offending material to be a fractured cylinder-shaped metal piece, which had settled within the fourth cervical vertebral body. Further forensic engineering investigation of the airbag unit found that the metal fragment originated from a defective airbag gas generator, which had exploded upon deployment. These findings reflect on the increasing popularity of airbag-associated trauma across the globe in recent years. We suggest an effective management plan for the evaluation and mitigation of the complications associated with airbag-related incidents.