The rail accident statistical data highlights the core reasons related to structural safety are ageing, high-density network, infrastructure defect, environmental hazard and human error. This study proposes a socio-technical architecture pivoting around UAVs within an intelligent transport ecosystem. A three-pillar framework is introduced, consisting of Monitoring, Analysis and Decision-Making, and Response and Mitigation. Data from multiple sources and different sensor types are utilized in machine-actionable safety intervention. Scenario-based assessments demonstrate the framework's impact. The proposed approach offers quantifiable benefits, such as reconnaissance flights adjusting early warning thresholds according to operational context, optimizing field deployment and resource allocation. This guide intends to achieve two primary objectives: firstly, to meaningfully reduce the risk of accidents; and secondly, to support sustainable mobility goals. Additionally, the framework is intended to align with evolving aviation and data governance standards.
Artificial intelligence and other immersive technologies have transformed transportation logistics. ChatGPT and the Metaverse have changed transportation system design, monitoring, and optimization. Integrating these technologies may improve multimodal transport network decision-making, efficiency, and risk. Metaverse and ChatGPT are used to study how current smart logistics frameworks might enhance transportation logistics efficiency and security. The study uses systematic methods for finding academic resources and selecting and extracting data. Metaverse technology increases logistics, risk management, and inventory optimization in supply chain design with real-time visualization and immersive virtual simulations. ChatGPT's natural language processing capabilities automate data analysis, enhance communication, and inform strategic choices, increasing operational efficiency. Integrating these technologies improves resilience and efficiency, but technical integration, security, and acceptability difficulties must be solved. Combining Metaverse and ChatGPT technologies in supply chain management may improve efficiency, decision-making, and resilience. However, security, acceptability, and technology integration must be addressed. Future research should examine best practices, ethical concerns, and empirical validation of these technologies. Finally, Metaverse and ChatGPT may modify SCM. Companies realize they need these technologies to simplify and modify their supply chain operations and transportation networks to compete in today's complex and ever-changing business environment.
This study presents the conceptual design of a fixed-wing Unmanned Aerial Vehicle (UAV) equipped with a fire-extinguishing ball deployment system for forest firefighting. The UAV design includes a wingspan of 3.250 m and a length of 3.075 m, using the NACA 6412 airfoil and a high-wing configuration to enhance aerodynamic stability. The fuselage and tail dimensions, including horizontal and vertical stabilizers, were optimized for aerodynamic efficiency. The UAV carries a payload of 13.5 kg, consisting of nine fire-extinguishing balls with a diameter of 152 mm each, providing a total effective coverage area of approximately 90 m². This configuration enables rapid and targeted suppression of ignition points in remote areas affected by wildfires during their growth or declining stages. Aerodynamic performance was evaluated using Computational Fluid Dynamics (CFD) simulations at 30 m/s and angles of attack (AoA) of 0°, 5°, 10°, 15°, and 20°. The UAV achieves a maximum take-off weight of 76.5 kg at an optimal AoA of 7.73°, while the best lift-to-drag ratio (L/D = 29.165) occurs at 5°. Pressure and velocity contours confirm stable flight up to 10° AoA, with stall behavior starting between 15° and 20°. Operational feasibility was assessed for remote regions such as the Amazon, with a take-off distance of 214.3 m and 317.2 m required to clear a 50-ft obstacle. These results demonstrate that the UAV can safely and efficiently deliver fire-extinguishing payloads in areas with limited accessibility, reducing the need for manned firefighting aircraft and providing a safer alternative for forest fire management.
This study focuses on simulating a queuing model aimed at optimizing the passenger processing at an airport. The objective is to minimize waiting times and reduce operational costs. The model employs Markov processes to simulate two main phases: passport and security screening. The simulation aims to keep waiting times for passport control under 8 minutes and for security screening under 2 minutes for priority passengers. Software tools Witness were used, and simulations were conducted at intervals of 60 and 75 minutes. The results indicate that longer intervals lead to increased average waiting times and higher costs per passenger. The study also includes an analysis of the operational characteristics of Václav Havel Airport in Prague, with a focus on the capacity and efficiency of the processing counters. The outputs from this simulation provide valuable insights for improving airport management and planning, aiming to enhance efficiency and passenger satisfaction.
The efficiency of a significant part of motor transport enterprises is unsatisfactory due to their inability to respond quickly to changes in the external and internal environments. One of the ways to solve this problem is to implement transformational changes across functional, organizational, structural, and managerial domains. These changes are implemented through a set of external and internal measures aimed at developing the enterprise in a dynamic external environment to ensure the efficiency of its operations and the necessary level of competitiveness. A model of transformational changes in motor transport enterprises has been developed. This model and the developed modelling algorithm allow us to identify promising strategies and to form and explore options for implementing transformational changes. To select the optimal transformational change in the work, an objective function that includes a competitiveness indicator (an integrated competitiveness indicator) and economic indicators (profitability index, net present value, and internal rate of return) has been justified. It is proposed to select the optimal option based on the “worst-case method”. Through this approach, the weight coefficients for the objective function criteria have been identified as follows: 0.308 for the integral competitiveness indicator; 0.154 for net present value; 0.462 for the profitability index; and 0.076 for the internal rate of return. Utilizing the developed models and algorithms, strategies have been outlined, and various options for implementing transformational changes have been formulated for the Vinnytsia branch of the private enterprise “Avtotranskom” (Ukraine). Using the set objective function and the “worst-case method”, the optimal transformational change among those developed was determined. By implementing this option, enterprises can operate more efficiently and compete more successfully in the market.
The article discusses the problem of assessing the risks of non compliance with liner shipping schedules, which is a key factor in the efficiency and reliability of international trade. Given the complexity and diversity of factors affecting schedule disruptions, ranging from port congestion and terminal productivity to the disruptive impact of external force majeure accidents (pandemics, storms, hurricanes, wars, etc.). The study proposes a methodology based on formal conceptual analysis (FCA). This approach allows for a structural risk assessment by grouping factors according to liner service ports of call, assigning weights to them, and calculating their integrated impact on the overall reliability of the schedule. The proposed approach is illustrated with a numerical model that demonstrates how changes in weighting factors can affect the final risk assessment. The results contribute to the development of theoretical approaches to risk management in shipping, while offering practical tools for reducing schedule disruptions in the LS-industry.
Airports are destinations where aircraft operate, and facilities such as runways, control towers, terminals, and hangars directly serve aircraft, passengers, and cargo. Airports are essential for moving human beings, goods, and services from one local or international destination to another. They are usually the main entry points for foreigners (expatriates or tourists) into a country. Hence, they have the capacity to drive economic growth, provide employment, and boost trade and tourism. This study aimed to examine the impact of airport infrastructure on the livelihoods of community members in Alakia, Ibadan. A descriptive research design was used for this study, and data were collected from respondents using a questionnaire. The sample size for this study was 384 respondents, as determined using Cochran's formula for the sample size determination. Collected data were analyzed using the statistical package for the social sciences (SPSS). This study revealed that the local Airport in Alakia is generally well perceived by community members. The fact that the Airport has enhanced income-generating opportunities for the community members implies that the community members will have increased savings and investments, which ultimately can reduce poverty in the community. The Airport has yielded significant infrastructural development in the community; for instance, roads are being constructed and renovated, making it easy for more vehicles to access the community.
The article examines the tasks and directions of transformation of logistics and sales management in the agricultural sector caused by the crisis. Quantitative statistical analysis of the Trade Policy Uncertainty Index proves that the level of challenges of the current crisis and the dynamics of changes in their impacts are significantly higher than during the crisis caused by the pandemic. The significance of these indicators demonstrates the presence of a threat not only to the sustainability of the activities of all business components of the logistics and sales system, but even to their survival in crisis conditions. At the same time, the significant pace of change in the challenges of the crisis complicates the process of management adaptation to them. To assess the level of management adaptation, a mathematical model of the dynamics of the logistics and sales process was developed, which allows determining the adequacy of the pace of change in management measures. The use of a systems approach made it possible to establish that important areas of logistics and sales process management also include supporting the resilience of not only the company, but also all components of the specified system - from the manufacturer to the sales markets. This necessitates the introduction of multi-purpose management and its construction on the principles of a hierarchy of management decisions. It is noted that this requires the interpretation of the logistics and sales chain as a system cycle, which in the process of optimizing sales logistics requires considering the set of risks as a mutually agreed system. The systems approach also necessitated the study of the components of the management of the logistics and sales system of the agricultural industry, including its institutional component. A comparative analysis of the components of the Logistics Efficiency Index by agricultural exporting countries indicated areas for improving Ukrainian institutional management. It is indicated that the dynamic reorientation of the directions of Ukraine's export logistics and sales activities leads to the formation of much longer logistics and sales chains, which reduces the possibility of participation in them not only of small but also of medium-sized agricultural producers, which radically changes the logistics and sales system of the country. In particular, this causes the need to involve logistics and sales companies on the principles of outsourcing or the formation of cooperatives specializing in logistics and sales of agricultural products.
The article considers the possibilities of integrating the basic principles of Industry 5.0 into the business processes of transport enterprises. Particular attention is paid to the general issues of the essence of Industry 5.0 and determining its genesis. It is established that the digitalization of business processes of transport enterprises on the principles of Industry 5.0 contributes to the formation of new goals, objectives and helps to meet customer needs and expand the capabilities of enterprises through the introduction of digital products. In this context, the main principles of Industry 5.0 include additive manufacturing, transparent production process, hyper-customization and cyber-physical cognitive systems, etc. The author outlines a number of characteristic features inherent in Industry 5.0, namely: customer focus, social responsibility, integration of advanced technologies, creation of innovative solutions in the field of flexible production systems, human capital development, and widespread use of digital technologies, which allow the creation of lean, sustainable and innovative production systems. The possibilities of integrating the basic principles of this concept of Industry 5.0 into the business processes of transport enterprises to ensure investment and security management are considered, and a number of advantages that enterprises will receive are identified, namely: improving the level of safety and maintenance; optimization of routes and flights; improving customer service; and reducing emissions into the atmosphere. It is proven that solving the tasks of resource management in the business processes of transport enterprises on the principles of Industry 5.0 in real time is ensured by the use of multi-agent systems, which are built on the basis of a network of small agents and parallel operations. A model for determining the level of implementation of digital technologies in the business processes of transport enterprises is proposed, and the coefficient of return on investment in digitalization is determined, which allows for establishing the level of net income and gross profit received by transport enterprises from increasing the use of digital technologies in their business processes.
This article examines the main factors influencing user satisfaction with urban public transport, based on the case of the ALSA network in Greater Agadir (Morocco). A questionnaire survey conducted among 205 users was analyzed using a two-step statistical approach: Principal Component Analysis (PCA) was first employed to identify the dimensions of perceived service quality, followed by multiple linear regression to assess their impact on overall satisfaction. The results highlight three major determinants: service reliability, travel time, and cost. These findings emphasize the importance of improving service regularity and adapting fare policies in order to enhance equity and the overall quality of urban transport. The study therefore provides an empirical framework that can support decision-making and may be applicable to other emerging urban contexts.
Cities near the coastline are the most important coastal tourism cities in Albania, which are experiencing a fourfold increase in population in the summer season, with an ever-increasing trend. Such a high tourist frequency in the summer season brings an extremely heavy traffic situation, especially at city entrances and exits. Environmental impact assessment represents the importance of implementing infrastructure projects, starting this analysis at the study and design stage of roads. The environmental impact assessment procedure focuses on describing the project, identifying the main negative impacts on the environment, and designing mitigation measures to minimize these negative impacts as much as possible, with the aim of maintaining the balance between them to achieve sustainable development of the area. Studies on environmental issues present the implementation of infrastructure development projects and the economic benefit from their implementation, always protecting the environment and taking into account the "Principle of Sustainable Development". This study shows the importance of developing road infrastructure projects well-focused on environmental protection. Road construction projects usually cause environmental pollution, impacts on habitats, changes in water flow patterns, and these projects must be developed taking into account environmental, social and economic impacts.
The article analyses the calculation rules for assessing the exceedance of permissible noise levels generated by road transport vehicles, their interpretation and application. Possible limitations and interpretation errors associated with the currently used rules for quantifying exceedances of permissible noise levels in the environment are highlighted. The related application consequences were discussed. Attention was drawn to the advisability of searching for a different methodology for classifying the results of exceedances of permissible noise levels, in relation to the acoustic protection of the environment applicable in practice. It was proposed that the methodology of modeling should be linked to the choice of a metric appropriate for comparisons of decibel numbers in the space of modeling the conditions of their reception by humans. Examples of metrics meeting the new criteria for the analysis of exceedances of permissible noise levels in the environment are provided. The authors, using the example of the analysis of noise monitoring results on one of the main communication arteries of the city of Kielce, presented the functioning of the new idea of classifying exceedances of permissible noise levels. The article presents a verification of the noise threat assessment using the Euclidean measure of exceedances of permissible noise levels, and using a measure that meets the requirements of the metric for the decibel space of human perception of acoustic phenomena. Statistical characteristics of the analyzed measures of exceedances of permissible noise levels are presented.
In spark ignition engines, engine performance and emission control are determined through the varying influence of exhaust gas recirculation and its aero-dynamic properties. However, few intensive studies are available in this domain. This paper simulates the combustion process in a spark ignition engine, studying the effects of exhaust gas recirculation (EGR) control mechanism on engine performance parameters and the aerodynamic properties of the EGR value, for optimum emission control. Thermodynamic engine models were used for the simulation of the combustion process. Cycle peak temperature reduction was used to assess the EGR system in the emission control of NOx. Hence, the simulation was structured to yield the volume, temperature and pressure of the engine cylinder, every degree crank angle at varying% EGR (say 0 to 20% of recycled exhaust gas). The effect of% EGR on indicated power, indicated thermal efficiency, indicated mean effective pressure, cycle peak temperature and cycle peak pressure were simulated. Aero dynamic properties of the EGR value were simulated to examine the factors that affect the EGR value in metering the required quantity of recycled exhaust gas into the engine intake. The effect of temperature, velocity, pressure and area of flow of the EGR gas through the EGR value were simulated. BASIC program was written to generate simulated data, which were plotted with Microsoft Excel. The principal results of this study include a reduction in the net work done by the engine (0.393 kJ at 0% EGR to 0.353 kJ at 20% EGR) as the recycled exhaust gas increases. Moreover, an inverse variation between the indicated power and% EGR existed (5.895 kW at% EGR to 5.290 kW at 20% EGR). Furthermore, an inverse variation between the cylinder peak pressure and% EGR was observed (5681 kPa at 0% EGR to 5228 kPa at 20% EGR). Overall, significant control of the emission of NOx was achieved through the use of the EGR, system, demonstrating the robustness of the proposed framework.
Smart airports increasingly rely on interconnected cyber‑physical systems, data-driven operations, and automation, which expands the attack surface for incidents involving unmanned aircraft systems (UAS). This article develops an AI‑enabled defense‑in‑depth (DiD) conceptual framework for countering UAS threats in smart airports, addressing both kinetic and cyber‑physical vectors while respecting the constraints of safety‑critical aviation operations. The AI-based Intrusion Risk Intervention (AIRI) framework is central to the proposed approach. AIRI specifies a five-stage decision loop (detect–classify–assess–intervene–learn) integrating multimodal sensing, AI‑assisted risk scoring, and human‑in‑the‑loop decision support. The framework is positioned against representative airport‑oriented UAS incident-management guidance and counter‑UAS frameworks, and a compact validation is provided through (a) a cross‑walk between AIRI stages and established incident-management steps and (b) a scenario exercise illustrating decision thresholds and intervention options. The paper further discusses regulatory, ethical, and operational requirements for deploying AI‑enabled counter‑UAS capabilities in European aviation, emphasizing traceability, logging, robustness, information-security management, and accountability. Claims are therefore limited to conceptual and design contributions supported by the compact validation; the paper concludes by outlining the data, metrics, and governance artifacts required for future empirical evaluation in operational airport environments.
The last mile is the final stage of the goods transportation process from the distribution warehouse to the end customer. Although it constitutes only a part of the entire logistics chain, it generates significant costs, which may account for 40% to even 53% of the total delivery value. Along with the dynamic development of e-commerce, customer expectations regarding the speed and flexibility of deliveries are also growing. At the same time, new regulations introduce increasingly stringent restrictions for traditional combustion vehicles. Logistics companies must therefore adapt their fleets, often investing in low- or zero-emission vehicles. The aim of this article is to propose a tool supporting decision-making regarding the selection of vehicles for last-mile logistics, taking into account economic, environmental, technical and social criteria. For this purpose, the MAJA method was applied, enabling multi-criteria assessment of light commercial vehicle powertrain variants, i.e. conventional, electric and plug-in hybrid, taking into account the operating conditions in Poland. The research results can provide support for city fleet operators, logistics companies and decision makers responsible for the development of sustainable transport in cities.
Within the framework of promoting circular economy strategies and expanding the portfolio of renewable energy sources, synthesis gases (syngas) produced from the gasification of municipal and plastic waste constitute a promising alternative fuel. This research investigates five high-energy syngas compositions, each maintaining constant inert gas proportions (10% CO2 and 5% N2), focusing on their combustion behavior in a spark-ignition internal combustion engine designed for cogeneration applications. This analysis focuses on the characterization of in-cylinder pressures, the indicated mean effective pressure (IMEP), heat release dynamics, and the duration of the combustion process. Experimental results demonstrate that elevated hydrogen proportion in fuel mixtures accelerates combustion, evidenced by reduced burn duration. However, hydrogen content did not exhibit a direct correlation with peak in-cylinder pressure. The maximum peak pressure was achieved by a mixture containing moderate hydrogen and elevated carbon monoxide content. A hydrogen-rich mixture displayed the shortest burn duration yet produced the lowest maximum pressure, attributed to spark timing positioned near top dead center (TDC). Methane concentration directly influenced the volumetric lower heating value (LHV), subsequently affecting both IMEP and torque output. Relative to methane operation, engine torque output decreased by 6% to 13.4%, while hourly fuel consumption increased from 1.55 kg.h-1 to 3.88 kg.h-1 depending on mixture composition.
The transport sector is a crucial lever for economic, social, and territorial development, playing a key role in regional integration and population mobility. In Algeria, significant efforts are underway to modernize transport infrastructure. These initiatives address the challenges of rapid urbanization and growing demand for mobility, while promoting ecological and sustainable solutions. At the same time, Italy, with its advanced infrastructure, is focusing on sustainability and innovation to modernize an already well-developed transport network, including metros, streetcars, high-speed trains, and freeways. The two countries illustrate complementary dynamics: Algeria is undertaking ambitious projects to address existing gaps, while Italy is adapting its infrastructure to contemporary challenges such as managing urban congestion and upgrading aging assets. Finally, sustainability and social equity lie at the core of transport policies in both Algeria and Italy, with accessible and environmentally friendly solutions aimed at reducing social inequalities and promoting more livable cities. These efforts reflect a transition toward transport systems that support a resilient and environmentally sustainable future.
Maintaining saturation flow at signalized intersections is crucial for both intersection capacity and sustainable traffic management. Efficient signal systems reduce congestion, lower emissions, and improve urban air quality. Factors such as signal timing, traffic demand, vehicle types, and intersection design significantly impact traffic flow efficiency. This study investigates the signal system and traffic flow parameters affecting lane inefficiency using Response Surface Methodology (RSM). Key factors included green time (G), the ratio of unused green time to total green time (ϴ/G), and discharge flow rate (β), while lane inefficiency (ẟ) served as the response variable. The full quadratic model was identified as the best model for explaining lane inefficiency due to its high adjusted R-squared value and low error values. The study recommends a green time of 30 seconds and a discharge flow rate of 0.540 vehicles per second per lane to obtain minimum lane inefficiency. These findings support decision-makers in creating smarter, more efficient signal-controlled intersections, ultimately contributing to sustainable urban transport infrastructure by improving traffic flow, reducing emissions, and lowering fuel consumption.
The aviation industry encompasses a variety of stakeholders. In recent years, the growing reliance of airlines on leased aircraft has elevated leasing companies to a critical position within the sector. Despite their importance, the capital structure of leasing companies remains an underexplored area in the aviation literature. This study is a pioneering effort to investigate both the theoretical and empirical aspects of leasing companies' capital structure. Using panel data analysis, the research examines the capital structure behavior of these companies over the period from 2013 to 2023. Six different models are developed to provide a more indepth analysis of the effects of short-term and long-term financing decisions on the capital structure. The findings generally indicate that the financing behavior of leasing companies is in line with the pecking order theory, which suggests seeking internal financing before seeking external debt or equity.
In this study, machine learning algorithms were applied to predict the main injection quantity in a high-pressure common rail system on a diesel engine test bench. The input parameters included engine load, fuel pressure, injection speed, and pulse time. Two models were selected for comparison: Support Vector Regression (SVR) and Random Forest (RF). The results showed that on the training dataset, the RF model outperformed SVR, with RMSE and MAE values of 0.027362 and 0.017628 respectively, significantly lower than those of SVR (RMSE = 0.051563, MAE = 0.027733). Additionally, RF achieved a higher coefficient of determination R² (0.995759 vs. 0.984939), indicating better learning of the relationships among variables. However, on the test dataset, SVR demonstrated superior predictive accuracy, achieving RMSE = 0.050097, MAE = 0.027673, and R² = 0.983550, while RF showed higher RMSE (0.060355), greater MAE (0.040485), and lower R² (0.976123). These results indicate that SVR has better generalization capability and is less prone to overfitting than RF. To assess the contribution of each input parameter, SHAP (SHapley Additive exPlanations) analysis was employed. The results revealed that injection speed, pulse duration, and fuel pressure had the most significant impact on the injection quantity. Meanwhile, engine load had a relatively lower influence but still played an important role under certain operating conditions. These analyses not only provide an intuitive understanding of model sensitivity but also help identify key factors to prioritize in control strategies. This study lays a foundation for the development of optimized control systems aimed at accurately and effectively reducing engine emissions in the future.
