Risk Of Accidents Research Articles

Experiences and needs of persons who have undergone limb amputation in Saki West, Oyo State, Nigeria

Abstract Background Amputation is a life-changing experience involving the surgical removal of a body part. However, little is known about the experiences of persons who have undergone limb amputation in the Saki West Local Government Area (LGA), which prompted this study. Methods An exploratory qualitative study design was used. Thirty participants were interviewed using face-to-face focus group discussions. Five sessions were conducted, with six participants in each session. Data were analysed using the thematic analysis framework of Braun and Clarke. Results A total of 76.3% of the participants were males and 73.3% were married. Their ages ranged from 22 and 69 y, with a mean age of 48.0 y and a monthly income of 20 000 naira (US$12). Three major themes emerged from the study: the needs and challenges of those who have undergone amputation, coping strategies of those who have undergone amputation and recommendations by persons who have undergone amputation. The study revealed that people who have undergone amputation experienced psychological pain, such as a loss of self-worth and stigmatization from friends and loved ones. Coping strategies identified were accepting the situation and emotional support from family. Participants recommended providing financial support, empowerment programs, employment opportunities and fostering a community of amputees in the Saki West LGA. Conclusions Amputees undergo psychological and psychosocial problems that can affect their health and recovery. They need emotional and financial support, rehabilitation services and provision of prostheses from family, society and the government. These services should be adequately provided in the Saki West LGA, the largest among the 10 suburban local governments in Oyo State, with numerous commercial activities and a high risk for road traffic accidents.

Abstract 4134345: Diastolic Dysfunction And The Risk Of Stroke And Major Bleeding

Background: Diastolic dysfunction (DD) is associated with adverse cardiovascular outcomes including atrial fibrillation. Whether DD is independently associated with incident ischemic stroke/transient ischemic attack (TIA) and with bleeding events is not well established. Aims: To examine the association of presence and severity of DD with the risk of incident ischemic stroke/TIA and major bleeding. Methods: A total of 219,667 patients who underwent at least one echocardiogram between 2010 and 2022 at a large academic institution and were followed for at least 3 months were included. Patients with prior history of stroke or TIA (n=21,505, 10%) were excluded. Smart key-phrase search was applied to echocardiographic reports to classify patients into 4 groups, based on the most severe DD assessment: Normal (n=113,265), grade I (n=21,981), grade II (n=7,215), and grade III (n=1,627). Patients in whom the presence of DD could not be determined (n=54,074) were excluded. The final study cohort (n=144,088) was followed to the endpoint of hospital admission for ischemic stroke/TIA or for major bleeding. Results: Over a median follow-up of 3.4 years, 3482 (2.4%) patients were hospitalized for stroke/TIA and 6994 (4.9%) for major bleeding. After adjusting for several confounding variables (Table), DD remained a strong predictor of incident stroke/TIA (hazard ratio (HR)=1.34 per grade increase in DD, p<0.001, Figure) and of major bleeding (HR=1.32 per grade increase in DD, p<0.001, Figure). These results were similar in patients with (n=11,508, 8%) or without prior history of atrial fibrillation. Conclusions: Patients with DD have a heightened risk of ischemic cerebrovascular accidents and major bleeding independent of the presence of atrial fibrillation. These risks escalate with the severity of DD. DD should be considered when counseling patients regarding their risk profile and management options.

Diastolic Dysfunction and the Risk of Stroke and Major Bleeding

BACKGROUND: Left ventricular diastolic dysfunction (DD) is associated with adverse cardiovascular outcomes including atrial fibrillation. Whether DD is independently associated with incident stroke and transient ischemic attack (TIA) and with bleeding events is not known. We performed this observational cohort analysis to examine the impact of DD on the risk of stroke/TIA and major bleeding. METHODS: Patients who underwent at least 1 cardiac echocardiogram and were followed for at least 3 months were included in this study. Patients with a prior history of stroke, TIA, or major bleeding, as determined by International Classifications of Diseases codes, were excluded. Smart key-phrase search was applied to echocardiographic reports to classify patients into 4 groups based on the most severe DD assessment. Patients in whom the presence of DD could not be determined were excluded. The final study cohort was followed to the end point of hospital admission for stroke/TIA and major bleeding, and independent predictors of these events were evaluated using the multivariable Cox proportional hazards method. RESULTS: The final study cohort (age, 56±18 years; 56% women) had 96 702 patients with no DD and 18 164, 5881, and 1340 patients with DD grades I, II, and III, respectively. Over a median follow-up of 3.4 years, 2938 (2.4%) patients were hospitalized for stroke/TIA and 5567 (4.6%) for major bleeding. After adjusting for age, the CHA 2 DS 2 -VASc score, chronic kidney disease, use of antiplatelet agents, use of anticoagulation agents, the year of echocardiographic testing, household income, and history of atrial fibrillation, DD remained a strong predictor of incident stroke/TIA (hazard ratio, 1.22 per grade increase in DD [95% CI, 1.16–1.29]; P <0.001) and major bleeding (hazard ratio, 1.20 per grade increase in DD [95% CI, 1.16–1.25]; P <0.001). CONCLUSIONS: DD is independently associated with a higher risk of cerebrovascular accidents and major bleeding. DD should be considered when counseling patients regarding their risk profile and management options.

A Study on the Rational Decision-Making Process of Vessel Organization—Focusing on Cases of Vessel Accidents

Vessel organizations are exposed to the risk of accidents due to the limited experience and intuition of the top decision makers in the organization in the special environment of the sea. This study aims to provide a direction for sustainable and rational decision-making in vessel organization through decision making process theory and case studies of actual vessel accidents. The results obtained from the actual case studies show that all three vessel accidents were caused by the arbitrary decision of the decision maker based on the Garbage Can model. This can be attributed to the closed nature of the vessel’s organization, which requires a decision-making process that is a hybrid of the Carnegie Decision model and Management Science model. Implications include the introduction of a ‘My Ship’ system, the de-subjectivization of top decision makers, situational awareness, and the need to provide top decision makers with the context and information relevant to their decisions. Limitations of this study include the fact that the case study was conducted only on Korean-flagged vessels and the study was limited to vessel accidents that occurred in the waters off Korea, China, and Japan, which limits the generalizability of the findings. In order to overcome these limitations, it would be interesting to conduct a follow-up study to include vessel accidents of different nationalities or to further investigate the characteristics of the decision-making process in vessel accidents by country.

Occupational accidents among rice farmers in Northern Vietnam

This study investigated the rate of agricultural occupational accidents among rice farmers in rural Northern Vietnam and identified factors associated with these accidents. A cross-sectional study was conducted on rice farmers in rural Thai Binh province, Northern Vietnam. A multi-stage random sampling method was used to select 1,171 rice farmers from six communes. Data were collected through structured interviews at the participants’ homes using a pilot-tested questionnaire that gathered information on occupational accidents in the previous six months, including their causes, frequency, and related injuries. Logistic regression analysis was performed to assess the factors associated with occupational accidents, considering variables such as age, gender, alcohol consumption, daily exercise, and working hours. Model fit was evaluated using goodness-of-fit tests, and significant variables were identified based on a p-value of < 0.05. Of the 1,171 rice farmers, 17.5% reported experiencing at least one occupational accident in the preceding six months. Sharp objects were the leading cause of accidents (46.3%), followed by falls (37.1%). Additionally, 41.0% of farmers who experienced accidents reported having three or more incidents in the past six months. The multivariate logistic regression analysis revealed that the likelihood of occupational accidents was significantly associated with increasing age, female gender, lack of daily exercise, alcohol consumption, and longer working hours. The final model showed good fit, with these variables demonstrating independent contributions to the accident risk. This study found a significant prevalence of occupational accidents among rice farmers in Northern Vietnam. Factors such as age, gender, exercise habits, alcohol use, and working hours were associated with higher accident risk. Future research should focus on exploring the knowledge, attitudes, and practices of farmers regarding accident prevention, and interventions should be developed to address these risk factors and improve safety in rice cultivation.

TPTrans: Vessel Trajectory Prediction Model Based on Transformer Using AIS Data

The analysis of large amounts of vessel trajectory data can facilitate more complex traffic management and route planning, thereby reducing the risk of accidents. The application of deep learning methods in vessel trajectory prediction is becoming more and more widespread; however, due to the complexity of the marine environment, including the influence of geographical environmental factors, weather factors, and real-time traffic conditions, predicting trajectories in less constrained maritime areas is more challenging than in path network conditions. Ship trajectory prediction methods based on kinematic formulas work well in ideal conditions but struggle with real-world complexities. Machine learning methods avoid kinematic formulas but fail to fully leverage complex data due to their simple structure. Deep learning methods, which do not require preset formulas, still face challenges in achieving high-precision and long-term predictions, particularly with complex ship movements and heterogeneous data. This study introduces an innovative model based on the transformer structure to predict the trajectory of a vessel. First, by processing the raw AIS (Automatic Identification System) data, we provide the model with a more efficient input format and data that are both more representative and concise. Secondly, we combine convolutional layers with the transformer structure, using convolutional neural networks to extract local spatiotemporal features in sequences. The encoder and decoder structure of the traditional transformer structure is retained by us. The attention mechanism is used to extract the global spatiotemporal features of sequences. Finally, the model is trained and tested using publicly available AIS data. The prediction results on the field data show that the model can predict trajectories including straight lines and turns under the field data of complex terrain, and in terms of prediction accuracy, our model can reduce the mean squared error by at least 6×10−4 compared with the baseline model.

ANÁLISE DE RISCOS NA PRODUÇÃO DE FRIGORÍFICOS: ESTRATÉGIAS PARA PREVENÇÃO E CONTROLE

This paper is a scientific article whose theme is the analysis of risks in the production of meatpacking plants: strategies for prevention and control. In this environment, workers face physical risks, biological risks and risks of accidents. This is because, in meatpacking plants, we find places where the temperature is extremely low (usually below 0º). Therefore, the function of the PPE provided is to provide safety to avoid humidity, low temperatures, noise, injuries caused by sharp instruments and biological contamination. What strategies should be adopted to prevent and control the occurrence of accidents and/or the spread of diseases in the production line of meatpacking plants, based on risk analysis? The general objective is: to describe what risk management is as well as the best way to apply it in a meatpacking plant to prevent accidents and control occupational diseases. The specific objectives are: to characterize what risk analysis and management are in a meatpacking plant; to present the main characteristics of the production environment as well as the main precautions that should be taken to ensure the physical integrity of the worker; describe the guidelines set out in NR-32 and its determinations for occupational health and safety, especially in companies that work with the slaughter and processing of meat and meat products. The methodology used will be a descriptive and exploratory literature review

Can Historical Accident Data Improve Sustainable Urban Traffic Safety? A Predictive Modeling Study

Traffic safety is a critical factor for the sustainable development of urban transportation systems. This study investigates the impact of historical accident information on the prediction of future traffic accident risks, as well as the interaction between this information and other features, such as driver violations and vehicle attributes. Using a comprehensive dataset of traffic accidents involving passenger vehicles in a western Chinese city, we developed two predictive models: Model 1, which is based on vehicle information and driver violations, and Model 2, which integrates historical accident data. The results indicate that the inclusion of historical accident information significantly enhances the predictive performance of the model, particularly in terms of AUC (Area Under the Curve) and AP (Average Precision) values. Furthermore, through feature importance analysis and SHAP (SHapley Additive exPlanations) value evaluation, this study reveals the interaction effects between historical accident data and other features, and how these interactions influence model decisions. The findings suggest that historical accident data play a positive role in predicting future accident risk, with varying effects on risk mitigation. These insights provide a scientific basis for developing strategies to ensure the sustainable development of urban transportation systems.

Driving Safety and Comfort Enhancement in Urban Underground Interchanges via Driving Simulation and Machine Learning

Urban transportation systems, particularly underground interchanges, present significant challenges for sustainable and resilient urban design due to their complex road geometries and dense traffic signage. These challenges are further compounded by the interaction of diverse road users, which heightens the risk of accidents. To enhance both safety and sustainability, this study integrates advanced driving simulation techniques with machine learning models to improve driving safety and comfort in underground interchanges. By utilizing a driving simulator and 3D modeling, real-world conditions were replicated to design key traffic safety features with an emphasis on sustainability and driver well-being. Critical safety parameters, including speed, acceleration, and pedal use, were analyzed alongside comfort metrics such as lateral acceleration and steering torque. The LightGBM machine learning model was used to classify safety and comfort grades with an accuracy of 97.06%. An important ranking identified entrance signage and deceleration zones as having the greatest impact on safety and comfort, while basic road sections were less influential. These findings underscore the importance of considering visual cues, such as markings and wall color, in creating safer and more comfortable underground road systems. This study’s methodology and results offer valuable insights for urban planners and engineers aiming to design transportation systems that are both safe and aligned with sustainable urban mobility objectives.

Work Accident Risk Analysis Using Failure Mode and Effect Analysis (FMEA) Method at PT. XYZ

In the production process, the interaction between humans and machines is closely related. From this the higher the risk of accidents along with the higher workload. This study aims to identify the risk of accidents that may arise during the work in progress. In addition, to identify actions that can be taken to reduce these risks. This type of descriptive qualitative research was conducted to assess the risk of work accidents at PT. XYZ. The data used in this study is primary data obtained through interviews using a questionnaire on one of the workers of PT. XYZ. The data were analyzed using the Failure Mode and Effect Analysis (FMEA) method to find the RPN and critical values ​​to find priority risks for action. The results obtained are that there are 4 work objects on the machine being analyzed, namely the stamping machine, lathe, welding machine, and milling machine. Based on the RPN value, it was obtained a value of 264 for a cut hand, 357 for a hand hit by a craft, 148 for a hand exposed to a welding spark, and 371 for a hand-carried by a machine. Based on the highest critical value, a stamping machine was obtained with a value of 66. The suggestion given to anticipate the occurrence of work accidents is by affirming SOPs in the use of Personal Protective Equipment (PPE), as well as workers' understanding of the risks of work accidents and their impacts.

Textile Fabric Defect Detection Using Enhanced Deep Convolutional Neural Network with Safe Human–Robot Collaborative Interaction

The emergence of modern robotic technology and artificial intelligence (AI) enables a transformation in the textile sector. Manual fabric defect inspection is time-consuming, error-prone, and labor-intensive. This offers a great possibility for applying more AI-trained automated processes with safe human–robot interaction (HRI) to reduce risks of work accidents and occupational illnesses and enhance the environmental sustainability of the processes. In this experimental study, we developed, implemented, and tested a novel algorithm that detects fabric defects by utilizing enhanced deep convolutional neural networks (DCNNs). The proposed method integrates advanced DCNN architectures to automatically classify and detect 13 different types of fabric defects, such as double-ends, holes, broken ends, etc., ensuring high accuracy and efficiency in the inspection process. The dataset is created through augmentation techniques and a model is fine-tuned on a large dataset of annotated images using transfer learning approaches. The experiment was performed using an anthropomorphic robot that was programmed to move above the fabric. The camera attached to the robot detected defects in the fabric and triggered an alarm. A photoelectric sensor was installed on the conveyor belt and linked to the robot to notify it about an impending fabric. The CNN model architecture was enhanced to increase performance. Experimental findings show that the presented system can detect fabric defects with a 97.49% mean Average Precision (mAP).

Efficacy of Acupuncture in Reducing Accidental Injury Risk in Stroke Patients: A National-Scale Cohort Study

BackgroundFalls among stroke patients pose a significant health concern, leading to injuries, reduced mobility, and functional limitations. Recent research has suggested the potential benefits of acupuncture in enhancing balance and motor function in stroke patients. However, large-scale, long-term studies on acupuncture's role in reducing the risk of accidental injuries are scarce. This study aimed to evaluate acupuncture's effectiveness in decreasing the risk of accidental injuries in stroke patients. Materials and methodsThis study employed a large-scale cohort design, utilizing data from the 2000 Longitudinal Generation Tracking Database (LGTD 2000) in Taiwan, covering two million individuals. The cohort comprised stroke patients hospitalized between 2001 and 2012, with follow-up periods of at least six years to estimate accidental injury incidence and risk. Acupuncture treatments during both inpatient and outpatient phases after stroke diagnosis were included. Accidental injury data, including vehicular accidents and falls, were extracted from specialized hospitalization records. Hazard ratios were calculated, adjusting for variables like comorbidities, hypnotic medication usage, and demographic factors. ResultA total of 108,196 newly diagnosed stroke patients were included in the final cohort, with 42,083 receiving acupuncture treatment and 66,113 not receiving acupuncture. The acupuncture group exhibited a significantly lower incidence rate of accidental injuries (10.2 per 1,000 person-years) compared to the non-acupuncture group (15.8 per 1,000 person-years). After adjusting for various covariates, the acupuncture group had a reduced risk of accidental injuries with an adjusted hazard ratio (aHR) of 0.74 (95% CI: 0.51-0.92). Subgroup analyses revealed consistent protective effects of acupuncture across different patient profiles and treatment characteristics, including age, sex, comorbidities, and hypnotic medication usage. ConclusionThis study provides evidence supporting the potential of acupuncture as an adjunctive therapy to reduce the risk of accidental injuries among stroke patients. Regardless of stroke subtype, age, or comorbidity burden, acupuncture was associated with a lower risk of accidental injuries.

A Neural-Metaheuristic Kalman Filter for Moving Microburst Wind Shear Identification

Microburst wind shears (MB) are powerful localized three dimensional (3D) columns of wind that occur when cooled air drops from the base of thunderstorms at high speeds. They eventually hit the ground and spread out in all directions. As such MBs are considered a major atmospheric hazard for aircrafts (ACs), especially in terminal flight phases. Though pilots train regularly to survive it, yet the most recommended practice within the aviation community is to avoid its encounter upon detection. Some modern aircrafts have predictive wind shear warning systems, but they have limited short range capability and do not provide quantitative data for engineering application. In this sense, accurate onboard detection and identification of MBs and its characteristics is of importance for flight safety, whose success can lead to design of automatic flight control systems (FCSs) that reduce the risk of aircraft crash landings and accidents. Even though there are some studies on FCS designs for MB encounter, research on MB identification is rare but ongoing. To this aim, the present study is among the firsts that focuses on online identification of multiple and moving MB parameters using onboard aircraft air data and inertial sensors. The identification task is initially performed using the aircraft six degrees of freedom (6DoF) equations of motion (EOM) integrated with a 3D MB model via the utility of nonlinear Kalman filtering (KF) algorithms. Subsequently, an enhanced neural metaheuristic Kalman filter (NMKF) is proposed that improves the estimation accuracy of the MB model parameters. The results demonstrate the efficacy of the proposed NMKF in comparison with previous studies.

Reduce Speed Limits to Minimize Potential Harm and Maximize the Health Benefits of Street Trees

Urban greening is threatened by the concern that street trees increase traffic-related injury/death. Associations between all serious and fatal traffic crashes and street tree percentages were examined in Sydney, Australia. Associations were adjusted for confounding factors relating to driver behavior (speeding, fatigue, and use of alcohol) and road infrastructure, including alignment (e.g., straight, curved), surface condition (e.g., dry, wet, ice), type (e.g., freeway, roundabout), and speed limit. Models indicated that 10% more street trees were associated with 3% and 20% higher odds of serious or fatal injuries and 20% tree collisions on roads of any speed, respectively. However, further analysis stratified by speed limit revealed contrasting results. Along roads of 70 km/h or greater, 10% more street trees were associated with 8% higher odds of serious or fatal injury and 25% higher odds of death. Comparable associations were not found between street trees and serious or fatal injuries along roads below 70 km/h. Reducing speed limits below 70 km/h saves lives and may mitigate risks of serious or fatal traffic accidents associated with street trees, enabling greener, cooler, healthier cities.

A study of the factors and correlations influencing the safety level, job stress and job satisfaction of workers in small business in Korea

BackgroundJob stress was found to directly increase the likelihood of accidents and indirectly increase the likelihood of accidents by lowering the safety atmosphere. Job stress has a significant impact on the safety environment, and job satisfaction is effective in reducing accident rates. Accordingly, we sought to investigate the correlation between safety level, job stress, and job satisfaction in small businesses with a high risk of industrial accidents. Additionally, we aim to investigate factors that may affect job stress, job satisfaction, and safety levels. MethodsThis study was conducted on 230 workers in small businesses in Korea. First, hierarchical regression analysis was conducted using SPSS software, and anova analysis was used to investigate significant variables. Finally, the relationships between variables were investigated using structural equation modeling (SEM). ResultsThe analysis results showed that safety level and job stress had a negative correlation with a coefficient of -0.8. Safety level and job satisfaction had a correlation with a coefficient of 0.2. Job satisfaction and job stress had a negative correlation with a coefficient of -0.7. This shows that when job stress is high, there is a negative correlation between job satisfaction and safety level. In addition, 4 general characteristics items and 3 job characteristics items showed a correlation with job stress. 2 general characteristics items and 8 job characteristics items showed a correlation with job satisfaction. 4 general characteristics items and 11 job characteristics items showed a correlation with safety level. ConclusionSmall businesses' job stress, safety level, and job satisfaction showed a significant correlation. In addition, the relationship between the variables affecting them was significant. In order to reduce the occurrence of accidents among workers in small businesses, control of factors and efforts to reduce job stress are necessary.

Ocean wave prediction using Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) in Tuban Regency for fisherman safety

The fishing industry has a large role in the Indonesian economy, with potential profits in 2020 of around US$ 1.338 billion. Tuban Regency is one of the regions in East Java that contributes to the fisheries sector. Fisheries relate to the work of fishermen. Accidents in shipping are still a major concern. One of the natural factors that influence shipping accidents is the height of the waves. Fisherman safety regulations have been established by the Ministry of Maritime Affairs and Fisheries and the Meteorology, Climatology and Geophysics Agency. Apart from regulations, the results of wave height predictions using the Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) methods can help fishermen determine shipping departures, thereby reducing the risk of accidents. In this study, the Grid Search hyperparameter tuning process was used for both methods which were carried out on four location coordinates. Based on the analysis results, LSTM is superior in predicting wave height for the next 30 days because it can predict wave height at all three locations, with results at the first location (RMSE 0.045; MAE 0.029; MAPE 8.671%), second location (RMSE 0.051; MAE 0.035; MAPE 10.64%), and third location (RMSE 0.044; MAE 0.027; MAPE 7.773%), while XGBoost only has the best value at fourth location (RMSE 0.040; MAE 0.025; MAPE 7.286%).Hyperparameter tuning with gridsearch is used in LSTM and XGBoost to obtain optimal accuracyLSTM outperforms in three locations, while XGBoost outperforms in the fourth location.Advanced prediction techniques such as LSTM and XGBoost improve fishermen's safety by providing accurate wave height estimates, thereby reducing the possibility of shipping accidents.

Investigation of the risk influential factors of maritime accidents: A novel topology and robustness analytical framework

This study aims to develop a novel and fully data-driven approach to analyse the maritime accidents risk influential factors (RIFs) by integrating Association Rule Mining (ARM) and Complex Network (CN) modelling. Firstly, a comprehensive dataset comprising 21,206 maritime accident records from Marine Accident Investigation Branch and Transportation Safety Board is collected and processed to serve as the foundational data source supporting the development of the new approach. Secondly, a novel Combined Association Rule Mining method is proposed to extract the interconnections among RIFs, with the mined results mapped into a CN framework. Finally, two importance ranking algorithms, namely the PageRank-Information-Entropy algorithm and edge betweenness centrality, are applied to identify the key RIFs and their information transmission paths. By simulating deliberate and random attacks within this network, a robustness analysis is conducted to further explore the evolution of RIFs. The findings reveal that ship-related factors demonstrate greater centrality and connectivity, exerting a more substantial influence on information propagation within the network structure. The robustness analysis illustrates that strategic node and edge removals are effective in preventing risk propagation. It therefore makes contributions to the development of a theoretical basis for stakeholders to develop cost-effective preventive measures against specific RIFs, ultimately enhancing maritime safety.

Analysis of pedestrian crossing violations at an overpass location by using a logit model

Jaywalking significantly increases accident risks compared to marked crosswalks. Overpasses are often installed to address this, but violations continue. This study examines pedestrian behavior at overpass locations, focusing on 438 individuals recorded on video. Factors like parked vehicles, gender, age, group size, mobile phone distraction, carrying items, the number of waiting pedestrians upon arrival, waiting times, and traffic volume were analyzed. Since jaywalking is more frequent during off-peak hours when traffic is not congested, observations were made at random intervals during these times. A binary logit model was used to identify the factors influencing jaywalking. The study found that points of interest near overpasses significantly affect pedestrian behavior. Additionally, parked vehicles near the overpass reduce crossing distance, encouraging jaywalking. Policy recommendations include implementing parking restrictions and strategically locating overpasses to align with pedestrian movement patterns and nearby points of interest.

Bibliometric Analysis of Traffic Accident Prediction Studies from 2003 to 2023: Trends, Patterns and Future Directions

Traffic accidents are one of the main causes of fatalities and serious injuries among both adults and children worldwide. Due to the ongoing significant socio-economic losses brought on by traffic accidents, precise estimation of the risk of accidents is crucial to reducing subsequent incidents. For this reason, a significant proportion of the studies in the literature include studies on estimating the risk, severity, frequency, location and duration of accidents. The objective of this article is to identify patterns, gaps and future research trends in traffic accident prediction studies conducted between 2003 and 2023. A bibliometric study is carried out to investigate the links and trends in traffic accident and forecasting studies, with a focus on identifying dominant narratives and networks within the academic community. In the keyword search, 1,566 articles were analysed using the Web of Science main collection and bibliometric indicators such as annual publications and citations, top 10, authors, journals, institutions, most cited articles, and a citation analysis of the articles was presented. The results obtained suggest that the discernible patterns identified in this bibliometric analysis of traffic accidents and their predictions will find a much broader application in new paradigms that are ready to catalyse transformative advances in this field, such as artificial intelligence, machine learning and Industry 4.0 applications.

Adaptive robust constraint-following control method for improving carbody hunting stability of high-speed trains

The low-frequency carbody hunting motion of high-speed trains (HSTs) frequently occurs due to the complex operating environment and deterioration of wheel–rail contact conditions. It not only affects ride comfort but also poses a risk of derailment safety accidents for HSTs. In such scenarios, traditional passive suspension systems struggle to ensure satisfactory dynamic performance of HSTs. However, active suspension systems can offer an effective solution. To enhance the low-frequency carbody hunting stability and environmental adaptability of HSTs, this study proposes a novel adaptive robust displacement inequality constraint following control (AICFC) method for active suspension utilising the beneficial nonlinearity inspired by bio-inspired structures (BIS). The effectiveness of the proposed AICFC-BIS method is validated through numerical simulations comparing it with other control strategies. Simulation results show that the AICFC-BIS method can effectively suppress low-frequency carbody hunting motion and improve the dynamic performance of HSTs. Compared with conventional passive suspensions and other active suspensions, HSTs equipped with AICFC-BIS active suspension exhibit significantly reduced operational safety risk and improved ride comfort indices while being energy efficient.