Search Results (141)

Traffic accidents pose a significant threat to public safety, resulting in numerous fatalities, injuries, and a substantial economic burden each year. The development of predictive models capable of the real-time forecasting of post-accident impact using readily available data can play a crucial role in preventing adverse outcomes and enhancing overall safety. However, existing accident predictive models encounter two main challenges: first, a reliance on either costly or non-real-time data, and second, the absence of a comprehensive metric to measure post-accident impact accurately. To address these limitations, this study proposes a deep neural network model known as the cascade model. It leverages readily available real-world data from Los Angeles County to predict post-accident impacts. The model consists of two components: Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN). The LSTM model captures temporal patterns, while the CNN extracts patterns from the sparse accident dataset. Furthermore, an external traffic congestion dataset is incorporated to derive a new feature called the “accident impact” factor, which quantifies the influence of an accident on surrounding traffic flow. Extensive experiments were conducted to demonstrate the effectiveness of the proposed hybrid machine learning method in predicting the post-accident impact compared to state-of-the-art baselines. The results reveal a higher precision in predicting minimal impacts (i.e., cases with no reported accidents) and a higher recall in predicting more significant impacts (i.e., cases with reported accidents). Full article

When emergency incidents, such as bridge damage, abruptly occur on highways and lead to traffic disruptions, the multidimensionality and complexity of driver behaviors present significant challenges to the design of effective emergency response mechanisms. This study introduces a multi-level collaborative emergency mechanism grounded in driver behavior characteristics, aiming to enhance both traffic safety and emergency response efficiency through hierarchical collaboration and dynamic optimization strategies. By capitalizing on human drivers’ perception and decision-making attributes, a driver behavior classification model is developed to quantitatively assess the risk response capabilities of distinct behavioral patterns (conservative, risk-taking, and conformist) under emergency scenarios. A multi-tiered collaborative framework, comprising an early warning layer, a guidance layer, and an interception layer, is devised to implement tailored emergency strategies. Additionally, a rear-end collision risk propagation model is constructed by integrating the risk field model with probabilistic risk assessment, enabling dynamic adjustments to interception range thresholds for precise and real-time emergency management. The efficacy of this mechanism is substantiated through empirical case studies, which underscore its capacity to substantially reduce the occurrence of secondary accidents and furnish scientific evidence and technical underpinnings for emergency management pertaining to highway bridge damage. Full article

Background: Renal trauma accounts for approximately 3–5% of all trauma cases, predominantly affecting young males. The most common etiology is blunt trauma, particularly due to road traffic accidents, and it frequently occurs as part of polytrauma involving multiple organ systems. Management strategies are primarily dictated by hemodynamic stability, overall clinical condition, comorbidities, and injury severity graded according to the AAST classification. This study aimed to evaluate the effectiveness of non-operative management (NOM) in high-grade renal trauma (AAST grades III–V), beyond its established role in low-grade injuries (grades I–II). Secondary endpoints included the identification of independent prognostic factors for NOM failure and in-hospital mortality. Methods: We conducted a retrospective observational study including patients diagnosed with blunt renal trauma who presented to the Emergency Department of Policlinico Umberto I in Rome between 1 January 2013 and 30 April 2024. Collected data comprised demographics, trauma mechanism, vital signs, hemodynamic status (shock index), laboratory tests, blood gas analysis, hematuria, number of transfused RBC units in the first 24 h, AAST renal injury grade, ISS, associated injuries, treatment approach, hospital length of stay, and mortality. Statistical analyses, including multivariable logistic regression, were performed using SPSS v28.0. Results: A total of 244 patients were included. Low-grade injuries (AAST I–II) accounted for 43% (n = 105), while high-grade injuries (AAST III–V) represented 57% (n = 139). All patients with low-grade injuries were managed non-operatively. Among high-grade injuries, 124 patients (89%) were treated with NOM, including observation, angiography ± angioembolization, stenting, or nephrostomy. Only 15 patients (11%) required nephrectomy, primarily due to persistent hemodynamic instability. The overall mortality rate was 13.5% (33 patients) and was more closely associated with the overall injury burden than with renal injury severity. Multivariable analysis identified shock index and active bleeding on CT as independent predictors of NOM failure, whereas ISS and age were significant predictors of in-hospital mortality. Notably, AAST grade did not independently predict either outcome. Conclusions: In line with the current international literature, our study confirms that NOM is the treatment of choice not only for low-grade renal injuries but also for carefully selected hemodynamically stable patients with high-grade trauma. Our findings highlight the critical role of physiological parameters and overall ISS in guiding management decisions and underscore the need for individualized assessment to minimize unnecessary nephrectomies and optimize patient outcomes. Full article

Low-altitude drones, which are unimpeded by traffic congestion or urban terrain, have become a critical asset in emergency rescue missions. To address the current lack of emergency rescue data, UAV aerial videos were collected to create an experimental dataset for action classification and localization annotation. A total of 5082 keyframes were labeled with 1–5 targets each, and 14,412 instances of data were prepared (including flight altitude and camera angles) for action classification and position annotation. To mitigate the challenges posed by high-resolution drone footage with excessive redundant information, we propose the SlowFast-Traffic (SF-T) framework, a spatio-temporal sequence-based algorithm for recognizing traffic accident rescue actions. For more efficient extraction of target–background correlation features, we introduce the Actor-Centric Relation Network (ACRN) module, which employs temporal max pooling to enhance the time-dimensional features of static backgrounds, significantly reducing redundancy-induced interference. Additionally, smaller ROI feature map outputs are adopted to boost computational speed. To tackle class imbalance in incident samples, we integrate a Class-Balanced Focal Loss (CB-Focal Loss) function, effectively resolving rare-action recognition in specific rescue scenarios. We replace the original Faster R-CNN with YOLOX-s to improve the target detection rate. On our proposed dataset, the SF-T model achieves a mean average precision (mAP) of 83.9%, which is 8.5% higher than that of the standard SlowFast architecture while maintaining a processing speed of 34.9 tasks/s. Both accuracy-related metrics and computational efficiency are substantially improved. The proposed method demonstrates strong robustness and real-time analysis capabilities for modern traffic rescue action recognition. Full article

Despite navigation advancements in enhanced sensor utilization and increased focus on maritime training and education, most marine accidents still involve collisions with high human involvement. Furthermore, navigators’ knowledge and application of the most often misunderstood Rule 10 Traffic Separation Schemes (TSS) according to the Convention on the International Regulations for Preventing Collisions at Sea (COLREG) represents the first focus in this study. To provide insight into the level of understanding and knowledge regarding COLREG Rule 10, a customized, worldwide survey has been created and disseminated among marine industry professionals. The survey results reveal a notable knowledge gap in Rule 10, where we initially assumed that more than half of the respondents know COLREG regulations well. According to the probability calculation and chi-square test results, all three categories (OOW, Master, and others) have significant rule misunderstanding. In response to the COLREG misunderstanding, together with the increasing density of maritime traffic, the implementation of Decision Support Systems (DSS) in navigation has become crucial for ensuring compliance with regulatory frameworks and enhancing navigational safety in general. This study presents a structural approach to vessel prioritization and decision-making within a DSS framework, focusing on the classification and response of the own vessel (OV) to bow-crossing scenarios within the TSS. Through the real-time integration of AIS navigational status data, the proposed DSS Architecture offers a structured, rule-compliant architecture to enhance navigational safety and the decision-making process within the TSS. Furthermore, implementing a Fall-Back Strategy (FBS) represents the key innovation factor, which ensures system resilience by directing operator response if opposing vessels disobey COLREG rules. Based on the vessel’s dynamic context and COLREG hierarchy, the proposed DSS Architecture identifies and informs the navigator regarding stand-on or give-way obligations among vessels. Full article

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. Full article

Vehicular ad hoc networks (VANETs) aim to manage traffic, prevent accidents, and regulate various parts of traffic. However, owing to their nature, the security of VANETs remains a significant concern. This study provides insightful information regarding VANET vulnerabilities and attacks. It investigates a number of security models that have recently been introduced to counter VANET security attacks with a focus on machine learning detection methods. This confirms that several challenges remain unsolved. Accordingly, this study introduces a lightweight machine learning model with a gain information feature selection method to detect VANET attacks. A balanced version of the well-known and recent dataset CISDS2017 was developed by applying a random oversampling technique. The developed dataset was used to train, test, and evaluate the proposed model. In other words, two layers of enhancements were applied—using a suitable feature selection technique and fixing the dataset imbalance problem. The results show that the proposed model, which is based on the Random Forest (RF) classifier, achieved excellent performance in terms of classification accuracy, computational cost, and classification error. It achieved an accuracy rate of 99.8%, outperforming all benchmark classifiers, including AdaBoost, decision tree (DT), K-nearest neighbors (KNNs), and multi-layer perceptron (MLP). To the best of our knowledge, this model outperforms all the existing classification techniques. In terms of processing cost, it consumes the least processing time, requiring only 69%, 59%, 35%, and 1.4% of the AdaBoost, DT, KNN, and MLP processing times, respectively. It causes negligible classification errors. Full article

Background: This study aimed to evaluate the clinical and forensic characteristics of cases admitted to a high-volume tertiary emergency department, focusing on severity-based classification using treatability with simple medical intervention (SMI) and life-threatening status. Methods: We retrospectively analyzed 3014 forensic cases over one year. Patients were classified based on injury severity, anatomical region, and clinical outcomes. Documentation practices and report types were also reviewed. Results: Among all the cases, 60.4% were treatable with SMI, and 10.5% were identified as life threatening. Notably, all patients who died (1.3% mortality) were in the life-threatening group, and none of the SMI-treated patients died, underscoring the accuracy of early triage and alignment between documentation and outcomes. Road traffic accidents were the leading cause of life-threatening injury and hospitalization, while assault cases were predominantly minor and managed conservatively. Seasonal variation peaked in July, and sex-based differences revealed a higher SMI eligibility among female patients. Final forensic reports were more frequently issued in SMI cases, while preliminary reports were predominant in severe trauma. Conclusions: Severity-based classification using SMI and life-threatening categories offers valuable insight for clinical decision-making and forensic documentation. Integrating structured triage, anatomical injury mapping, and standardized report templates can enhance both patient safety and legal reliability. Full article

Traffic accident prediction is essential for improving road safety and optimizing intelligent transportation systems. However, deep learning models often struggle with distribution shifts and class imbalance, leading to degraded performance in real-world applications. While distribution shift is a common challenge in machine learning, Transformer-based models—despite their ability to capture long-term dependencies—often lack mechanisms for dynamic adaptation during inferencing. In this paper, we propose a TTT-Enhanced Transformer that incorporates Test-Time Training (TTT), enabling the model to refine its parameters during inferencing through a self-supervised auxiliary task. To further boost performance, an Adaptive Memory Layer (AML), a Feature Pyramid Network (FPN), Class-Balanced Attention (CBA), and Focal Loss are integrated to address multi-scale, long-term, and imbalance-related challenges. Our experimental results show that our model achieved an overall accuracy of 96.86% and a severe accident recall of 95.8%, outperforming the strongest Transformer baseline by 5.65% in accuracy and 9.6% in recall. The results of our confusion matrix and ROC analyses confirm our model’s superior classification balance and discriminatory power. These findings highlight the potential of our approach in enhancing real-time adaptability and robustness under shifting data distributions and class imbalances in intelligent transportation systems. Full article

The latest developments in Advanced Driver Assistance Systems (ADAS) have greatly enhanced the comfort and safety of drivers. These technologies can identify driver abnormalities like fatigue, inattention, and impairment, which are essential for averting collisions. One of the important aspects of this technology is automated traffic accident detection and prediction, which may help in saving precious human lives. This study aims to explore critical features related to traffic accident detection and prevention. A public US traffic accident dataset was used for the aforementioned task, where various machine learning (ML) models were applied to predict traffic accidents. These ML models included Random Forest, AdaBoost, KNN, and SVM. The models were compared for their accuracies, where Random Forest was found to be the best-performing model, providing the most accurate and reliable classification of accident-related data. Owing to the black box nature of ML models, this best-fit ML model was executed with explainable AI (XAI) methods such as LIME and permutation importance to understand its decision-making for the given classification task. The unique aspect of this study is the introduction of explainable artificial intelligence which enables us to have human-interpretable awareness of how ML models operate. It provides information about the inner workings of the model and directs the improvement of feature engineering for traffic accident detection, which is more accurate and dependable. The analysis identified critical features, including sources, descriptions of weather conditions, time of day (weather timestamp, start time, end time), distance, crossing, and traffic signals, as significant predictors of the probability of an accident occurring. Future ADAS technology development is anticipated to be greatly impacted by the study’s conclusions. A model can be adjusted for different driving scenarios by identifying the most important features and comprehending their dynamics to make sure that ADAS systems are precise, reliable, and suitable for real-world circumstances. Full article

Vehicle-to-Vehicle (V2V) communications technology offers enhanced road safety, traffic efficiency, and connectivity. In V2V, vehicles cooperate by broadcasting safety messages to quickly detect and avoid dangerous situations on time or to avoid and reduce congestion. However, vehicles might misbehave, creating false information and sharing it with neighboring vehicles, such as, for example, failing to report an observed accident or falsely reporting one when none exists. If other vehicles detect such misbehavior, they can report it. However, false accusations also constitute misbehavior. In disconnected areas with limited infrastructure, the potential for misbehavior increases due to the scarcity of Roadside Units (RSUs) necessary for verifying the truthfulness of communications. In such a situation, identifying malicious behavior using a standard misbehaving management system is ineffective in areas with limited connectivity. This paper presents a novel mechanism, Distributed Reputation for Accurate Misbehavior Reporting (DRAMBR), offering a fully integrated reputation solution that utilizes reputation to enhance the accuracy of the reporting system by identifying misbehavior in rural networks. The system operates in two phases: offline, using the Local Misbehavior Detection Mechanism (LMDM), where vehicles detect misbehavior and store reports locally, and online, where these reports are sent to a central reputation server. DRAMBR aggregates the reports and integrates DBSCAN for clustering spatial and temporal misbehavior reports, Isolation Forest for anomaly detection, and Gaussian Mixture Models for probabilistic classification of reports. Additionally, Random Forest and XGBoost models are combined to improve decision accuracy. DRAMBR distinguishes between honest mistakes, intentional deception, and malicious reporting. Using an existing mechanism, the updated reputation is available even in an offline environment. Through simulations, we evaluate our proposed reputation system’s performance, demonstrating its effectiveness in achieving a reporting accuracy of approximately 98%. The findings highlight the potential of reputation-based strategies to minimize misbehavior and improve the reliability and security of V2V communications, particularly in rural areas with limited infrastructure, ultimately contributing to safer and more reliable transportation systems. Full article

In response to the increasing frequency of maritime traffic accidents along China’s coast, this study develops an accident-cause analysis framework that integrates an optimized Bidirectional Encoder Representations from Transformers (BERT) with a Bidirectional Long Short-Term Memory network (BiLSTM), combined with the Apriori association rule algorithm. Systematic performance comparisons demonstrate that the BERT + BiLSTM architecture achieves superior unstructured-text-processing capability, attaining 89.8% accuracy in accident-cause classification. The hybrid framework enables comprehensive investigation of complex interactions among human factors, vessel characteristics, environmental conditions, and management practices through multidimensional analysis of accident reports. Our findings identify improper operations, fatigue-related issues, illegal modifications, and inadequate management practices as primary high-risk factors while revealing that multi-factor interaction patterns significantly influence accident severity. Compared with traditional single-factor analysis methods, the proposed framework shows marked improvements in Natural Language Processing (NLP) efficiency, classification precision, and systematic interpretation of cross-factor correlations. This integrated approach provides maritime authorities with scientific evidence to develop targeted accident prevention strategies and optimize safety management systems, thereby enhancing maritime safety governance along China’s coastline. Full article

This study analyzes factors influencing injury severity in motorcycle accidents involving non-helmeted riders using Bayesian spatiotemporal logistic models. Five models were developed, four of which incorporated different spatiotemporal configurations, including spatial, temporal, and spatiotemporal interaction error terms. The results indicate that the optimal model integrated Leroux CAR spatial priors, temporal random walks, and interaction terms, achieving 86.74% classification accuracy, with a 3% reduction in the DIC value; obtaining the lowest numerical fit demonstrating spatiotemporal interactions is critical for capturing complex risk patterns (e.g., rain amplifying nighttime collision severity). The results highlight rain (OR = 1.53), age ≥ 50 (OR = 1.90), and bi-directional roads (OR = 1.82) as critical risk factors. Based on these findings, several sustainable traffic safety strategies are proposed. Short-term measures include IoT-based dynamic speed control on high-risk roads and app-enforced helmet checks via ride-hailing platforms. Long-term strategies integrate age-specific behavioral training focusing on hazard perception and reaction time improvement, which reduced elderly fatalities by 18% in Japan’s “Silver Rider” program by directly modifying high-risk riding habits (non-helmets). These solutions, validated by global case studies, demonstrate that helmet use could mitigate over 60% of severe head injuries in these high-risk scenarios, promoting sustainable traffic governance through spatiotemporal risk targeting and helmet enforcement. Full article

Public transportation plays a crucial role in our lives, and the road network is a vital component in the implementation of smart cities. Recent advancements in AI have enabled the development of advanced monitoring systems capable of detecting anomalies in road surfaces and road signs, which can lead to serious accidents. This paper presents an innovative approach to enhance road safety through the detection and classification of traffic signs and road surface damage using advanced deep learning techniques (CNN), achieving over 90% precision and accuracy in both detection and classification of traffic signs and road surface damage. This integrated approach supports proactive maintenance strategies, improving road safety and resource allocation for the Molise region and the city of Campobasso. The resulting system, developed as part of the CTE Molise research project funded by the Italian Minister of Economic Growth (MIMIT), leverages cutting-edge technologies such as cloud computing and High-Performance Computing with GPU utilization. It serves as a valuable tool for municipalities, for the quick detection of anomalies and the prompt organization of maintenance operations. Full article

In recent years, an increasing number of accidents at ski resorts have raised significant safety concerns. To address these issues, it is essential to understand skiing traffic and the underlying dynamics. We collected 225 trajectories, which were analyzed after a correction process. To obtain a quantitative classification of typical trajectories we focus on three main quantities: turning points, minimum distance, and the fundamental diagram. Our objective was to analyze these trajectories in depth and identify key statistical properties. Our findings indicate that three factors—turning angle, curvature, and velocity change—can be used to accurately identify turning points and classify skiers’ movement styles. We found that aggressive skiers tend to exhibit larger and less stable turning angles, while conservative skiers demonstrate a more controlled style, characterized by smaller, more stable turns. This is consistent with observations made for the distribution of the minimum distance to other skiers. Furthermore, we have derived a fundamental diagram which is an important characteristic of any traffic system. It is found share more similarities with the fundamental diagram of ant trails than those of highway traffic. Full article