Search Results (106)

Civil aviation safety remains a critical concern globally, with continuous efforts aimed at reducing accidents and fatalities. This paper focuses on the comprehensive evaluation of civil aviation safety in the Slovak Republic over the past several years, with the main objective of identifying prevailing trends and key risk factors. A comprehensive analysis of 155 accidents and incidents was conducted based on selected operational parameters. Logistic regression was applied to identify potential causal factors influencing various levels of injury severity in aviation accidents. Moreover, the prediction model can also be used to predict the probability of specific injury severity for accidents with given parameter values. The results indicate a clear declining trend in the annual number of aviation safety events; however, the fatality rate has stagnated or slightly increased in recent years. Human error, particularly mistakes and intentional violations of procedures, was identified as the dominant causal factor across all sectors of civil aviation, including flight operations, airport management, maintenance, and air navigation services. Despite technological advancements and regulatory improvements, human-related failures persist as a major safety challenge. The findings highlight the critical need for targeted strategies to mitigate human error and enhance overall aviation safety in the Slovak Republic. Full article

Autonomous vehicles (AVs) have emerged as a promising sustainable urban mobility solution, expected to lead to enhanced road safety, smoother traffic flows, less traffic congestion, improved accessibility, better energy utilization and environmental performance, as well as more efficient passenger and freight transportation, in terms of time and cost, due to better fleet management and platooning. However, challenges also arise, mostly related to data privacy, security and cyber-security, high acquisition and infrastructure costs, accident liability, even possible increased traffic congestion and air pollution due to induced travel demand. This paper presents the results of a survey conducted among 654 residents who experienced an autonomous bus (AB) service in the city of Trikala, Greece, in order to assess their willingness to use (WTU) and willingness to pay (WTP) for ABs, through testing a range of factors based on a literature review. Results useful to policy-makers were extracted, such as that the intention to use ABs was mostly shaped by psychological factors (e.g., users’ perceptions of usefulness and safety, and trust in the service provider), while WTU seemed to be positively affected by previous experience in using ABs. In contrast, sociodemographic factors were found to have very little effect on the intention to use ABs, while apart from personal utility, users’ perceptions of how autonomous driving will improve the overall life standards in the study area also mattered. Full article

In urban areas with warm climates, a lack of proper curing during concrete pavement construction can significantly reduce service life, increase maintenance needs, and compromise sustainability goals. Despite its relevance, the comprehensive impact of curing has been poorly quantified from a multidimensional perspective. This study aims to evaluate the effect of applying a liquid curing compound on the sustainability of concrete slab pavements over a 20-year horizon using a simulation-based approach. Two scenarios, cured and uncured, were modeled with HIPERPAV^®, incorporating site-specific climatic, structural, and material parameters. Based on projected maintenance cycles, nine sustainability indicators were calculated and grouped into environmental (CO₂ emissions, energy, water, and waste), social (accidents, travel time, satisfaction, and jobs), and economic (life-cycle maintenance cost) dimensions. Statistical tests (ANOVA, Welch ANOVA, and Kruskal–Wallis) were applied to assess significance. Results showed that curing reduced CO₂ emissions (−13.7%), energy consumption (−12.5%), and waste (−20.7%), while improving accident rates (−40.3%), user satisfaction (+17.8%), and maintenance cost savings (−9.5%). The findings support curing as a cost-effective and sustainability-enhancing strategy for urban pavement design and management. Full article

Vehicle owners often use certified service centers throughout the warranty period, which usually extends for five years after buying. Nonetheless, after this timeframe concludes, a large number of owners turn to unapproved service providers, mainly motivated by financial factors. This change signifies a significant drop in income for automakers and their certified service networks. To tackle this issue, manufacturers utilize customer relationship management (CRM) strategies to enhance customer loyalty, usually depending on segmentation methods to pinpoint potential clients. However, conventional approaches frequently do not successfully forecast which clients are most likely to need or utilize maintenance services. This research introduces a machine learning-driven framework aimed at forecasting the probability of monthly maintenance attendance for customers by utilizing an extensive historical dataset that includes information about both customers and vehicles. Additionally, this predictive approach supports workforce planning and scheduling within after-sales service centers, aligning with AI-driven labor optimization frameworks such as those explored in the AI4LABOUR project. Four algorithms in machine learning—Decision Tree, Random Forest, LightGBM (LGBM), and Extreme Gradient Boosting (XGBoost)—were assessed for their forecasting capabilities. Of these, XGBoost showed greater accuracy and reliability in recognizing high-probability customers. In this study, we propose a machine learning framework to predict vehicle maintenance visits for after-sales services, leading to significant operational improvements. Furthermore, the integration of AI-driven workforce allocation strategies, as studied within the AI4LABOUR (reshaping labor force participation with artificial intelligence) project, has contributed to more efficient service personnel deployment, reducing idle time and improving customer experience. By implementing this approach, we achieved a 20% reduction in information delivery times during service operations. Additionally, survey completion times were reduced from 5 min to 4 min per survey, resulting in total time savings of approximately 5906 h by May 2024. The enhanced service appointment scheduling, combined with timely vehicle maintenance, also contributed to reducing potential accident risks. Moreover, the transition from a rule-based maintenance prediction system to a machine learning approach improved efficiency and accuracy. As a result of this transition, individual customer service visit rates increased by 30%, while corporate customer visits rose by 37%. This study contributes to ongoing research on AI-driven workforce planning and service optimization, particularly within the scope of the AI4LABOUR project. Full article

As an emerging development field, in recent years, emergency industrial parks in China have faced increasingly complex and high-risk challenges. This article proposes the establishment of a scientific safety risk assessment and grading model to help improve the safety management level of emergency industrial parks, in response to the problems of the multi-source heterogeneity of fire risks in emergency industrial parks and the difficulty of comprehensive assessment using traditional methods. This approach combines enterprise type classification with multi-level assessment for the first time, effectively identifying high-risk links such as fires and explosions and playing an effective role in preventing accidents such as fires in the park. Enterprises within the park are categorized into seven distinct groups based on their characteristics and associated safety risks: medical and healthcare, new energy storage, composite materials and new materials, intelligent manufacturing, mechanical manufacturing, consulting and technical services, and construction and installation. The following models are constructed: (1) a risk assessment model based on AHP-FCE, which can assess the safety risk levels of individual enterprises and the industrial park at a macro level; (2) a risk grading model based on the risk matrix method, which can inspect and control specific risk sources at a micro level. The integration of these two methods establishes a comprehensive model for safety risk assessment and grading in emergency industrial parks, significantly improving both the accuracy and the systematic nature of risk management processes. Full article

Traffic congestion represents an urban challenge that authorities are trying to solve through various means. Current traffic management systems do not solve these challenges, which is why the research presents a new proposal for a traffic optimization system. The proposed solution integrates small-sized equipment (ESP32 equipped with accelerometers, gyroscopes, and cameras), cloud-based AI services (Azure Content Safety), and a multi-parametric analytical framework for real-time navigation. The system uses the Traffic Optimization Algorithm (TOA) proposed by the authors to calculate the Global Route Quality Indicator (GRQIk). It associates each route with a value based on which the degree of optimality is estimated. GRQIk is calculated based on the distance traveled, traffic delays, estimated travel time, road safety, and the individual’s sensitivity. Real-time data are collected using ESP32, with a pothole detection threshold set at 0.8 rad/s. Through the TomTom API, four alternative routes are identified. The performance evaluation showed that GRQIk differentiates route quality, with scores ranging from 26.40% for optimal routes to 100% for the least favorable ones. In addition, Azure’s Content Safety API achieved 100% accuracy in identifying violent incidents and accidents. The limitations of the research concern the small number of images available to test the Content Safety service. The research establishes new approaches for future developments in the field of smart transportation. Full article

Benchmarking railway infrastructure managers (RIMs) has become a crucial tool in the context of European transport market liberalization, facilitating efficiency improvements and strategic decision-making. RIMs face challenges in increasing capacity, optimizing operations, and ensuring competitive, safe, and economically sustainable services. To address these challenges, this study proposes a hybrid benchmarking model that integrates Principal Component Analysis (PCA) to identify key performance indicators (KPIs) and reduce data dimensionality, the Grey Best–Worst Method (G-BWM) to determine KPI weight coefficients based on expert evaluations, and Assurance Region Data Envelopment Analysis (AR-DEA) to assess the relative efficiency of RIMs while incorporating real-world constraints. The research findings confirm that RIM8 is the most efficient unit, driven by high electrification levels, strong accident prevention measures, and optimal use of infrastructure. In contrast, RIM2 and RIM4 record the lowest efficiency scores, primarily due to poor safety performance, high infrastructure-related delays, and suboptimal resource utilization. By introducing weight constraints through AR-DEA, the model ensures that efficiency assessments reflect actual operational conditions, rather than relying on unrestricted weight allocations. The main contribution of this study lies in developing a systematic and objective framework for evaluating RIM efficiency, ensuring consistency and reliability in performance measurement. The practical implications extend to policy development and operational decision-making, providing insights for infrastructure managers, regulatory bodies, and policymakers to optimize resource allocation, enhance infrastructure resilience, and improve railway sector sustainability. The results highlight key efficiency factors and offer guidance for targeted improvements, reinforcing benchmarking as a valuable tool for long-term railway infrastructure management and investment planning. By offering a quantitatively grounded efficiency assessment, this model contributes to the competitiveness and sustainability of railway networks across Europe. Full article

Runway skid resistance is crucial for the safety of aircrafts. Despite being internationally regulated, investigation reports published by the Australian Transport Safety Bureau and the US National Transportation Safety Board indicate that 4.9–22% of runway excursion accidents are related to insufficient friction, or to friction overestimation. Consequently, based on this review of friction physics, aircraft accident reports, international runway surface regulation, and aircraft braking performance regulation, it was concluded that significant improvement in the management of runway surface characteristics can be achieved. Areas for potential improvement in the current systems for aircraft skid resistance include gaps in the operational reporting of prevailing runway contamination, as well as friction and surface texture measurement and interpretation protocols. Furthermore, aircraft braking performance regulations are not related to actual runway surface friction levels, resulting in reportedly good runways being found to provide inadequate aircraft skid resistance in certain conditions. Recommendations include improvements in the management of runway friction and texture measurement and analysis during pavement design, and through the service life of the pavement surfaces. Finally, the basis of an improved international runway surface engineering design and management system is outlined. Recommendations can reduce the risk of aircraft skidding accidents in the future. Full article

In the 4th industrial era, the proliferation of interconnected smart devices and advancements in AI, particularly big data and machine learning, have integrated various industrial domains into cyberspace. This convergence brings novel security threats, making it essential to prevent known incidents and anticipate potential breaches. This study develops a scenario-based evaluation system to predict and evaluate possible security accidents using the MITRE ATT&CK framework. It analyzes various security incidents, leveraging attack strategies and techniques to create detailed security scenarios and profiling services. Key contributions include integrating security logs, quantifying incident likelihood, and establishing proactive threat management measures. The study also proposes automated security audits and legacy system integration to enhance security posture. Experimental results show the system’s efficacy in detecting and preventing threats, providing actionable insights and a structured approach to threat analysis and response. This research lays the foundation for advanced security prediction systems, ensuring robust defense mechanisms against emerging cyber threats. Full article

Road traffic accidents cause nearly 1.3 million deaths and around 50 million injuries each year globally. Most organisations generate travel and transport, and influence thereby traffic safety. In Sweden, 47 percent of fatal accidents in road traffic are work-related, and 36 percent are linked to a procured transport service. Effective approaches to managing traffic safety in organisations have not yet been established to advance traffic safety implementation, especially through sustainability practices and reporting. This study explores the untapped potential of improving traffic safety by addressing the traffic safety impact of organisations within the context of sustainability. Firstly, this study examines organisations’ views on traffic safety as a sustainability issue, and the status and driving forces in handling traffic safety as an integrated part of sustainability practices and reporting. Secondly, it identifies enablers for advancing traffic safety implementation in organisations with a focus on the sustainability context. The study is based on interviews with 22 organisations (mainly private companies) and analysis of 23 sustainability reports. It is concluded that sustainability is a relevant context for traffic safety for all organisations that consider traffic safety as a significant sustainability issue due to the travel and transport generated, directly or indirectly. However, traffic safety is generally not viewed as a sustainability issue and is rarely included in sustainability reports. Transport companies are more likely to consider traffic safety in the context of sustainability. Enablers concern the necessity to communicate traffic safety as a sustainability issue and to raise awareness of traffic safety as part of the 2030 Agenda for Sustainable Development. Communication is needed to raise awareness among private and public organisations on their traffic safety footprint and possibilities to influence traffic safety. Furthermore, legislative directives and standards on sustainability reporting should explicitly include traffic safety. By integrating traffic safety in sustainability practices and reporting, organisations can more clearly draw from and utilise the positive synergies between traffic safety and other sustainability goals. Furthermore, such integration is a way to bring traffic safety issues up to the management level, facilitating leadership for traffic safety. Full article

With the rising incidence of traffic accidents and growing environmental concerns, the demand for advanced systems to ensure traffic and environmental safety has become increasingly urgent. This paper introduces an automated highway safety management framework that integrates computer vision and natural language processing for real-time monitoring, analysis, and reporting of traffic incidents. The system not only identifies accidents but also aids in coordinating emergency responses, such as dispatching ambulances, fire services, and police, while simultaneously managing traffic flow. The approach begins with the creation of a diverse highway accident dataset, combining public datasets with drone and CCTV footage. YOLOv11s is retrained on this dataset to enable real-time detection of critical traffic elements and anomalies, such as collisions and fires. A vision–language model (VLM), Moondream2, is employed to generate detailed scene descriptions, which are further refined by a large language model (LLM), GPT 4-Turbo, to produce concise incident reports and actionable suggestions. These reports are automatically sent to relevant authorities, ensuring prompt and effective response. The system’s effectiveness is validated through the analysis of diverse accident videos and zero-shot simulation testing within the Webots environment. The results highlight the potential of combining drone and CCTV imagery with AI-driven methodologies to improve traffic management and enhance public safety. Future work will include refining detection models, expanding dataset diversity, and deploying the framework in real-world scenarios using live drone and CCTV feeds. This study lays the groundwork for scalable and reliable solutions to address critical traffic safety challenges. Full article

Performance safety is one of the important goals for the high-quality development of modern performance services. The in-service stage suspension equipment that has been put into use is one of the most frequently used and most closely related stage machinery in performances, and there are often significant safety hazards during its use. In response to the current lack of safety risk assessment methods and incomplete assessment techniques for in-service stage suspension equipment, this paper proposes a safety risk assessment method for in-service stage suspension equipment based on grey fuzzy comprehensive evaluation, with professional theaters as the target scenario. This method first identifies risk factors based on Failure Mode and Effects Analysis (FMEA), then uses the grey relational analysis (GRA) method for risk factor analysis, and finally adopts the fuzzy comprehensive evaluation (FCE) method to achieve safety risk level assessment. By constructing and analyzing an evaluation model for professional theater stage suspension equipment, the safety risk levels and corresponding safety risk factor rankings of performance accidents such as electric shock, falling, and failure can be obtained, and measures to reduce risks can be provided based on the most important risk factors. The research results show that more attention should be paid to the influence of human factors in the safety assessment and detection of in-service stage suspension systems. The research in this article is of great significance for improving the safe use of in-service stage suspension equipment, enhancing the level of performance safety management, and improving the quality of performance equipment services, laying the foundation for the formation of relevant regulatory systems and standards. Full article

(1) Background: The right to refuse dangerous work (RTRDW) is essential for preventing industrial accidents and protecting worker safety in Korea. However, its use remains limited in practice. This study seeks to identify the factors hindering its activation across industries such as construction, manufacturing, and services, offering a comprehensive analysis beyond previous research. (2) Methods: A survey was conducted across key industries to assess five factors—safety behavior, communication, management commitment, education and training, and education and training—using structural equation modeling (SEM) to evaluate their influence on the exercise of RTRDW. (3) Results: The SEM model showed a good fit (χ² = 1151.333, p < 0.001, TLI = 0.978, CFI = 0.984, RMSEA = 0.05). The most significant factors influencing RTRDW were safety performance behavior and communication, while ambiguous regulations, poor training, and fear of job loss discouraged its use. (4) Conclusions: To improve RTRDW activation, clearer regulations, enhanced safety education and training, stronger management commitment, and better communication are necessary. Addressing these issues can help workers confidently exercise their right to refuse dangerous work, enhancing overall workplace safety. (5) Benefits: This study provides practical strategies for policymakers and industry leaders to promote safety, empowering workers to use RTRDW effectively and contributing to a safer work environment. Full article

Coal mining production processes are complex and prone to frequent accidents. With the continuous improvement of safety management systems in China’s coal mining industry, a vast amount of coal mine safety experience knowledge (CMSEK) has been accumulated, originating from on site operations. This knowledge has been recorded and stored in paper or electronic documents but it remains unconnected, and the increasing volume of documents further complicates the reuse and sharing of this knowledge. In the era of large models and digitalization, this knowledge has yet to be fully developed and utilized. To address these issues, a risk management checklist was derived from coal mining site data. By integrating intelligent algorithm models and the coal industry knowledge engineering design, a coal mine safety experience knowledge graph (CMSEKG) was developed to enhance the efficiency of utilizing coal mine safety experience knowledge. Specifically, we creatively developed a coal mine safety experience knowledge representation framework, capable of representing coal mine risk inspection records from different sources and of various types. Furthermore, we proposed a deep learning-based coal mine safety entity recognition model (CMSNER), which can effectively extract coal mine safety experience knowledge from text. Finally, the CMSEKG was stored using the Neo4j graph database, and a knowledge graph was constructed using selected case information as examples. The CMSEKG effectively integrates fragmented safety management experience and professional knowledge, promoting knowledge services and intelligent applications in coal mining operations, thereby providing knowledge support for the prevention and management of coal mine risks. Full article

In the urban public transport network, the transfer of buses and subways provides convenience for residents to travel efficiently. But in actual operation, it is found that accidents, natural disasters, and other damage are inevitable. These sudden events may lead to route suspensions and service delays, ultimately resulting in network paralysis. In this paper, complex network theory is used to construct a weighted double-layer network model. Carrying capacity is considered the edge weight. The model analyzes the impact of these sudden events on network performance. It also conducts in-depth research on network structure and node importance. A collective influence (CI) algorithm is proposed as a centrality index to evaluate node importance. Based on the dynamic nature of the attacks, the network state is divided into initial network and current network. Taking Hohhot as an example, the results show that the network based on a CI algorithm node attack has the worst invulnerability. The network invulnerability based on an edge weight attack is better than that of edge betweenness. Compared with the current network, the invulnerability of the initial network is stronger. This indicates that ongoing changes and adaptations in the network may accelerate the decline in overall performance. At the same time, targeted interventions on key nodes and edges can enhance the network’s invulnerability. Planners can continuously monitor network performance to provide a basis for dynamic management and real-time adjustments. Additionally, effective information about critical routes to the public helps ensure the sustainable operation of the public transportation network. Full article