Search Results (111)

Traffic accidents pose a persistent challenge for urban mobility, public safety, and sustainable development in smart cities, particularly in rapidly growing urban environments. This study presents a data-driven spatiotemporal analysis of traffic accidents in Ecuador, aimed at supporting evidence-based urban traffic management and road safety planning. Using large-scale historical accident records, the proposed approach combines spatial clustering and temporal forecasting techniques to characterize accident concentration patterns and temporal dynamics at national and metropolitan scales. Spatial accident hotspots are identified using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling the detection of high-risk zones without imposing assumptions on cluster shape or size. This analysis reveals strong spatial concentration of accidents, with a limited number of clusters accounting for a substantial proportion of fatalities and injuries. Complementary temporal analysis is conducted using a multi-model ensemble framework to examine accident trends and seasonal patterns. This approach integrates SARIMA for linear stochastic modeling and Prophet for additive trend analysis, alongside two Long Short-Term Memory (LSTM) architectures: a direct 12-month vector output and a recursive horizon-3 model. By synthesizing these statistical and neural network-based methods through inverse-RMSE weighting, the study captures both stable seasonal cycles and non-linear, short-to-medium-term variations in accident frequency. Results show that traffic accidents in Ecuador exhibit stable diurnal and seasonal structures, alongside pronounced spatial heterogeneity across urban regions. The combined spatial and temporal insights provide a coherent representation of accident risk patterns, facilitating the prioritization of critical zones and high-risk periods. The resulting hotspot maps and multi-model forecasting horizons offer actionable information for smart city stakeholders, supporting targeted infrastructure interventions, adaptive enforcement strategies, and data-informed urban mobility policies. This work contributes to the broader understanding of traffic safety analytics as a core component of smart city decision-support systems. Full article

Occupational safety analytics is increasingly moving toward data-driven methodologies; however, existing models often struggle to capture the multidimensional nature of accident causation. This study presents a multimodal Hybrid Transformer-LSTM framework for classifying occupational fatalities by jointly modeling unstructured narratives, cyclical temporal features, and regional spatial indicators. Utilizing a large-scale dataset of 14,914 OSHA fatality records, the proposed architecture leverages BERT-based embeddings for semantic extraction and Bidirectional LSTMs as non-linear pattern encoders for spatiotemporal context. Conceptually grounded in the Swiss Cheese Model, the framework treats different data modalities as proxies for distinct layers of system risk, ranging from proximal unsafe acts to environmental preconditions. Experimental results show that the multimodal architecture achieves an accuracy of 84.56%, representing a 5.33% gain over unimodal BERT baselines. To address the inherent “black-box” nature of deep learning, a SHAP-based explainability framework is incorporated to quantify the contributions of both textual tokens and environmental features to the model’s decision-making process. The results indicate that integrating narrative semantics with temporal and spatial context enhances discriminative performance and enables context-aware classification within a weakly supervised setting. By providing a scalable and interpretable classification framework, this study offers a data-driven decision-support approach for safety professionals and regulatory bodies seeking to implement evidence-based risk management strategies in high-risk industrial sectors. Full article

Accurate forecasting of fatal construction accidents is critical for proactive safety management; however, accident time series exhibit strong non-stationarity, nonlinear dynamics, and multi-scale temporal patterns that challenge conventional models. This study proposes a hybrid STL–BiGRU framework that integrates Seasonal–Trend decomposition using Loess (STL) with a Bidirectional Gated Recurrent Unit (BiGRU) network to deliver robust and interpretable forecasts tailored to construction safety needs. STL first decomposes the original monthly accident series (January 2012–December 2024, OSHA) into trend, seasonal, and residual components, reducing structural complexity and mitigating non-stationarity. Independent BiGRU models are then trained on each component to capture bidirectional temporal dependencies, and final forecasts are reconstructed through component aggregation. Comparative experiments against Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA), and their STL-enhanced variants demonstrate that the proposed STL–BiGRU model achieves superior performance across both short-term and medium-term horizons. The model achieves the lowest error levels, with a short-term Root Mean Squared Error (RMSE) of 6.8522 and a medium-term RMSE of 7.0568, and shows consistent improvements in Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Results indicate that multi-scale decomposition combined with bidirectional deep learning provides a practical, forward-looking tool. It helps regulators and contractors anticipate high-risk periods, optimize resource allocation, and reduce fatal accidents through targeted preventive measures. Full article

Road traffic accidents at intersections pose a persistent challenge in Riyadh, Saudi Arabia, contributing significantly to public health burdens and economic losses. Traditional statistical approaches often fail to capture the complex, non-linear interactions among geometric design, traffic parameters, and accident severity. This study develops a multi-methodological machine learning framework to predict intersection accident severity using the Equivalent Property Damage Only (EPDO) metric. Historical data (2017–2023) from Riyadh Municipality for 150 high-risk intersections were analyzed, incorporating predictors such as service road distance (SRD), U-turn distance (UTD), median width (MW), peak hour volume (PHV), heavy vehicle percentage (HV%), and injury/frequency counts. Six algorithms, i.e., Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Linear Regression, and Artificial Neural Network, were compared using a 70/30 train–test split and k-fold cross-validation in this study. The Gradient Boosting model achieved superior performance (R² = 0.89 with MSE = 63.43 and RMSE = 7.96) and was selected for final deployment. SHAP feature importance analysis revealed minor injuries (MIs), serious injuries (SRIs), and fatalities (FAs) as the most important dominant predictors, with geometric factors (UTD, MW) and traffic composition (HV%) providing actionable infrastructure insights. The model ranked intersections and identified the “Jeddah Road with Taif Road” (predicted EPDO = 137.22) as the highest-risk location. Evidence-based recommendations include enforcing the minimum 300 m U-turn buffers with staggering service road exits ≥150 m and restricting heavy vehicles during peak hours. The scalable framework developed in this study supports the data-driven prioritization of safety interventions and aligns with sustainable urban mobility goals and offers transferability to other metropolitan contexts worldwide. Full article

This research examines the risk factors that influence injury severity in individual motorcycle accidents, utilising a dataset of 5253 incidents. Five machine learning algorithms—multinomial logistic regression, classification trees, random forests, XGBoost, and neural networks—were used to classify the results into three groups: Death (13.48%), Injury (80.14%), and No injury (6.38%). In all models, passenger presence was the most important predictor of injury. Motorcycle accidents involving passengers do not always have more serious consequences for several overlapping reasons. On the one hand, a motorcycle with a passenger has a significantly higher mass, which increases the braking distance and kinetic energy at the moment of collision, hindering quick defensive manoeuvres, cornering, and reactions to sudden hazards. Often, the rider also refrains from sudden movements to prevent the passenger from losing their balance. In the case of single-rider motorcycle accidents on roadways, approximately 5% of those involved with a passenger were fatalities, while approximately 48% were uninjured; in the case of those without a passenger, no one was uninjured. It follows from the above that the presence of a passenger increases the rider’s sense of responsibility. Other factors that significantly increased risk were single-lane carriageways, vehicle overturning, contaminated road surfaces, and collisions with complex objects, e.g., like trees. The multinomial logistic regression model had an overall accuracy of 69.2% on the test set. The Recurrent Neural Network achieved the best overall accuracy of 79.56%. Balanced accuracy, as the average between sensitivity and specificity of the RNN model for the “death” class was 68.15%, for the “injury” class—72.6%, and for the “no injury” class—96.61%. The Area Under the ROC Curve of the Recurrent Neural Networks model for “no injury” was 0.97, indicating it was very good at distinguishing between this class and the other classes. Even though it was easy to tell which cases did not involve injuries, it was still hard to tell the difference between fatal and non-fatal injuries in all models. The results support interventions tailored to specific situations, such as improved road lighting and speed control in rural areas, as well as helmet enforcement and safety measures at intersections in cities. Full article

Car accidents remain a leading cause of death worldwide, with drowsiness and distraction accounting for roughly 25% of fatal crashes in Ecuador. This study presents a real-time driver monitoring system that uses computer vision and deep learning to detect fatigue, distraction, and emotions from facial expressions. It combines a MobileNetV2-based CNN trained on RAF-DB for emotion recognition and MediaPipe’s 468 facial landmarks to compute the EAR (Eye Aspect Ratio), the MAR (Mouth Aspect Ratio), the gaze, and the head pose. Tests with 27 participants in both real and simulated driving environments showed strong results. There was a 100% accuracy in detecting distraction, 85.19% for yawning, and 88.89% for eye closure. The system also effectively recognized happiness (100%) and anger/disgust (96.3%). However, it struggled with sadness and failed to detect fear, likely due to the subtlety of real-world expressions and limitations in the training dataset. Despite these challenges, the results highlight the importance of integrating emotional awareness into driver monitoring systems, which helps reduce false alarms and improve response accuracy. This work supports the development of lightweight, non-invasive technologies that enhance driving safety through intelligent behavior analysis. Full article

The aim of this article is to develop a machine learning (ML)-based predictive model for industrial accidents in the energy sector. The dataset used in this study was obtained from the Kaggle platform and consists of summaries derived from reports of occupational incidents resulting in injuries or deaths between 2015 and 2017. A total of 4739 accident cases were included, containing information on accident date, accident summary, degree and nature of injury, affected body part, event type, human factors, and environmental factors. Six supervised machine learning models—Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost)—were developed and compared to identify the most suitable model for the data. Model performance was evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC), which were selected to ensure reliable prediction in safety-critical accident scenarios. The results indicate that XGBoost and GBDT achieve superior performance in predicting human and environmental risk factors. These findings demonstrate the potential of machine learning for improving safety management in the energy sector by identifying risk mechanisms, enhancing safety awareness, and providing quantitative predictions of fatal and non-fatal accident occurrences for integration into safety management systems. Full article

This study investigates the causes of traffic accidents involving Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS) and their interdependencies. Using a source dataset comprising 3015 ADAS accident records and 1085 ADS accident records from National Highway Traffic Safety Administration (NHTSA), the study categorizes accident severity into four levels and applies association rule mining (ARM) to identify high-frequency risk factor combinations. Key risk factors include environmental, road, vehicle, and accident characteristics. Findings show that ADAS accidents are concentrated in highway straight-driving scenarios, strongly correlated with rainy weather, and often involve rear-end collisions due to delayed driver reactions. ADS accidents predominantly occur in intersection stopping scenarios, favor clear weather, and exhibit better safety performance in non-damage cases with Level 5 (L5) systems, though they still face perception and decision-making challenges in complex scenarios like nighttime wet roads. The study further reveals that vehicle design purpose (ADAS for highways, L5 for urban areas) strongly influences accident severity, with L5 systems reducing fatality risks through advanced perception but still affected by high speeds, extreme lighting, and system aging. Make attributes and technological maturity also significantly impact outcomes. This study provides insights for technological advancement, regulatory improvements, and human–machine collaboration optimization. Full article

Background: Free fatal falls (FFF) are a frequent occurrence in forensic medicine. Many variables, such as the victim’s sex, BMI, intoxication, height of the fall, and mental illness, can influence injury patterns. Previous studies identified fracture patterns and frequencies mostly with general anatomical detail, focusing on broad areas. As specific fractures might be roots for new statistical connections, this leaves a gap in our understanding. Using postmortem computed tomography, we aim to establish fracture frequencies and identify possible new statistical connections. Methods: In total, we retrospectively analyzed seventy-nine cases of confirmed deaths due to falls using the database of the Department and Institute of Forensic Medicine in Lublin. Our inclusion criteria were death due to free fall onto hard, non-deformable surfaces. We excluded cases of ground-level falls. All victims must have undergone postmortem computed tomography. Furthermore, each analyzed case documented individual intrinsic variables (sex, age, body mass, height, pre-existing mental conditions, and drug or alcohol use) and extrinsic variables (fall height, landing surface, time between the fall and death, and known cause of the fall). Results: Injuries in free fatal falls tend to focus on the axial skeleton. Suicides experience more severe, bilateral fractures, often involving the pelvis and limbs, while accidents tend to have unilateral injuries with rare limb involvement. We established new correlations with the height of the fall for the maxilla, mandible, anterior and posterior regions of the occipital bone, and the temporal bone. Moreover, our research confirmed previously noted correlations between the height of the fall and fractures of the limbs (and their individual bones), the lumbar vertebrae, and the chest. Conclusions: Our findings highlight that free fatal falls are characterized by distinct skeletal injury patterns that differ between accidents and suicides, with bilateral pelvic and limb fractures being particularly indicative of intentional falls. The integration of PMCT with autopsy improves the detection of these patterns. It provides valuable diagnostic and medico-legal insights, supporting a more precise determination of the cause and manner of death. Full article

Background: Free fatal falls (FFF) represent a distinct form of blunt force trauma (BFT) that is frequently encountered in forensic practice. Distinguishing FFF injuries from other forms of BFT, such as motor vehicle accidents (MVAs), can pose challenges. Despite its growing usage, the role of postmortem computed tomography (PMCT) in diagnosing FFF and its comparison with autopsy remains underexplored. Purpose: This review synthesizes fracture patterns in FFF, examining both extrinsic and intrinsic variables that influence skeletal injuries. It also compares PMCT and autopsy findings to establish a replicable database for forensic analysis. Methods: PubMed and Google Scholar were systematically searched by three independent reviewers. The inclusion criteria required studies to be published in English, report at least 10 cases, focus on fatal falls, and provide precise data on skeletal injuries. Studies lacking detailed descriptions, focusing on survivors, or involving non-free falls were excluded. Data extraction tables facilitated synthesis and analysis. Key Findings: FFF are characterized mainly by axial skeletal fractures, particularly of the chest, skull, and pelvis. A history of intoxication and psychiatric disorders often correlates with the manner of death. Fracture patterns vary by fall height, impact surface, and cause: accidental falls show greater chest and skull involvement, whereas suicidal falls present more pelvic and skull fractures. PMCT detects fractures more frequently than traditional autopsy. Conclusions: Distinct fracture patterns aid in differentiating suicidal from accidental FFF, shaped by extrinsic and intrinsic factors. Given its superior fracture detection capabilities, PMCT should be integrated into forensic protocols for FFF investigations. Full article

Drowsiness constitutes a significant risk factor in diverse occupational settings, including healthcare, industry, construction, and transportation, contributing to accidents, injuries, and fatalities. Electroencephalography (EEG) signals, offering direct measurements of brain activity, have emerged as a promising modality for drowsiness detection. However, the inherent non-stationary nature of EEG signals, coupled with substantial inter-subject variability, presents considerable challenges for reliable drowsiness detection. To address these challenges, this paper proposes a hybrid approach combining convolutional neural networks (CNNs), which excel at feature extraction, and support vector machines (SVMs) for drowsiness detection. The framework consists of two modules: a CNN for feature extraction from EEG scalograms generated by the Continuous Wavelet Transform (CWT), and an SVM for classification. The proposed approach is compared with 1D CNNs (using raw EEG signals) and transfer learning models such as VGG16 and ResNet50 to identify the most effective method for minimizing inter-subject variability and improving detection accuracy. Experimental evaluations, conducted on the publicly available DROZY EEG dataset, show that the CNN-SVM model, utilizing 2D scalograms, achieves an accuracy of 98.33%, outperforming both 1D CNNs and transfer learning models. These findings highlight the effectiveness of the hybrid CNN-SVM approach for robust and accurate drowsiness detection using EEG, offering significant potential for enhancing safety in high-risk work environments. Full article

Predicting driver injury severity is critical for enhancing road safety, but it is complicated because fatal accidents inherently create class imbalance within datasets. This study conducts a comparative analysis of machine-learning (ML) and deep-learning (DL) models for multi-class driver injury severity prediction using a comprehensive dataset of 107,195 traffic accidents from the Adana, Mersin, and Antalya provinces in Turkey (2018–2023). To address the significant imbalance between fatal, injury, and non-injury classes, the hybrid SMOTE-ENN algorithm was employed for data balancing. Subsequently, feature selection techniques, including Relief-F, Extra Trees, and Recursive Feature Elimination (RFE), were utilized to identify the most influential predictors. Various ML models (K-Nearest Neighbors (KNN), XGBoost, Random Forest) and DL architectures (Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN)) were developed and rigorously evaluated. The findings demonstrate that traditional ML models, particularly KNN (0.95 accuracy, 0.95 F1-macro) and XGBoost (0.92 accuracy, 0.92 F1-macro), significantly outperformed DL models. The SMOTE-ENN technique proved effective in managing class imbalance, and RFE identified a critical 25-feature subset including driver fault, speed limit, and road conditions. This research highlights the efficacy of well-preprocessed ML approaches for tabular crash data, offering valuable insights for developing robust predictive tools to improve traffic safety outcomes. Full article

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

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

Chemical plants inherently handle and operate with a wide range of hazardous materials, making them more prone to accidents compared to other industrial sectors. Consequently, safety management in chemical plants tends to be systematically organized based on elements of process safety management (PSM) systems. In June 2023, South Korea’s Ministry of Employment and Labor released the Serious Injury and Fatality (SIF) report, which summarized 4432 major accident cases that occurred over six years (2016–2021), including 1834 cases in manufacturing and related industries and 2574 cases in construction. The report provided an overview of these accidents, their causes, and measures to prevent their recurrence, with a focus on fatalities and severe injuries associated with critical losses across different industries. This study examined 16 accident cases that occurred at PSM-regulated facilities, which are managed on the basis of a systematic safety framework established by regulatory requirements. Among these, particular attention was paid to an explosion accident in the organic catalyst packaging process at a facility with no prior accident history and exhibiting unique accident characteristics. A systemic root cause analysis was conducted using the barrier-based systemic cause analysis technique (BSCAT) and the system theoretic accident model and process (STAMP-CAST) methodologies. The systemic analysis highlighted the critical importance of clearly identifying materials or factors that may inadvertently mix during the process design or mass production phases and evaluating whether such interactions could lead to accidents during the hazard assessment stage. Beyond incorporating the risk mitigation measures identified in the process design and procedural development phases without omissions, it is essential to periodically conduct “worker-centered risk assessments”. These assessments help evaluate the potential for accidents resulting from human errors, such as workers’ non-compliance with established procedures, which is a key aspect of preventing chemical accidents. Although this study did not include an evaluation of the impacts of high pressures or high temperatures on workers near chemical accident sites—hence, no specific recommendations regarding safe working distances are made—the findings are expected to contribute to the development of preventive measures for chemical accidents in smaller-scale plants where workers directly manage and operate processes. Full article