Search Results (80)

In today’s dynamic and highly uncertain environment, organizations, particularly in the aviation sector, face increasing challenges that demand resilient, flexible, and data-driven change management decisions. Responding to the growing need for structured approaches to managing complex uncertainties—geopolitical tensions, economic volatility, social shifts, rapid technological advancements, environmental pressures and regulatory changes—this research proposes a theoretical change management model for aviation service providers, such as airports. Integrating three analytical approaches, the model offers a robust, multi-method approach for supporting sustainable transformation under uncertainty. Normative analysis using Bayesian decision theory identifies influential external environmental factors, capturing probabilistic relationships, and revealing causal links under uncertainty. Prescriptive planning through scenario theory explores alternative future pathways and helps to identify possible predictions, offer descriptive evaluation employing fuzzy comprehensive evaluation, and assess decision quality under vagueness and complexity. The proposed four-stage model—observation, analysis, evaluation, and response—offers a methodology for continuous external environment monitoring, scenario development, and data-driven, proactive change management decision-making, including the impact assessment of change and development. The proposed model contributes to the theoretical advancement of the change management research area under uncertainty and offers practical guidance for aviation organizations (airports) facing a volatile external environment. This framework strengthens aviation organizations’ ability to anticipate, evaluate, and adapt to multifaceted external changes, supporting operational flexibility and adaptability and contributing to the sustainable development of aviation services. Supporting aviation organizations with tools to proactively manage systemic uncertainty, this research directly supports the integration of sustainability principles, such as resilience and adaptability, for long-term value creation through change management decision-making. Full article

The aviation industry is under increasing pressure to reduce its environmental impact while maintaining safety and performance standards. One promising area for improvement lies in the use of sustainable materials in airport infrastructure. One of the issues preventing uptake of emerging sustainable technologies is the lack of guidance relating to the opportunities, potential benefits, associated risks and an implementation plan specific to airport pavements. This research reviewed opportunities to incorporate waste materials into rigid airport pavements, focusing on concrete base slabs. Commonly used supplementary cementitious materials (SCMs), such as fly ash and ground granulated blast furnace slag (GGBFS) were considered, as well as recycled aggregates, including recycled concrete aggregate (RCA), recycled crushed glass (RCG), and blast furnace slag (BFS). Environmental Product Declarations (EPDs) were also used to quantify the potential for environmental benefit associated with various concrete mixtures, with findings showing 23% to 50% reductions in embodied carbon are possible for selected theoretical concrete mixtures that incorporate waste materials. With considered evaluation and structured implementation, the integration of waste materials into rigid airport pavements offers a practical and effective route to improve environmental outcomes in aviation infrastructure. It was concluded that a Triple Bottom Line (TBL) framework—assessing financial, environmental, and social factors—guides material selection and can support sustainable decision-making, as does performance-based specifications that enable sustainable technologies to be incorporated into airport pavement. The study also proposed a consequence-based implementation hierarchy to facilitate responsible adoption of waste materials in airside pavements. The outcomes of this review will assist airport managers and pavement designers to implement practical changes to achieve more sustainable rigid airport pavements in the future. Full article

Emergencies can occur unexpectedly and require immediate action, especially in aviation, where time pressure and uncertainty are high. This study focused on improving emergency evacuation in airport and aircraft scenarios using real-time decision-making support. A system based on the Asynchronous Advantage Actor–Critic (A3C) algorithm, an advanced deep reinforcement learning method, was developed to generate faster and more efficient evacuation routes compared to traditional models. The A3C model was tested in various scenarios, including different environmental conditions and numbers of agents, and its performance was compared with the Deep Q-Network (DQN) algorithm. The results showed that A3C achieved evacuations 43.86% faster on average and converged in fewer episodes (100 vs. 250 for DQN). In dynamic environments with moving threats, A3C also outperformed DQN in maintaining agent safety and adapting routes in real time. As the number of agents increased, A3C maintained high levels of efficiency and robustness. These findings demonstrate A3C’s strong potential to enhance evacuation planning through improved speed, adaptability, and scalability. The study concludes by highlighting the practical benefits of applying such models in real-world emergency response systems, including significantly faster evacuation times, real-time adaptability to evolving threats, and enhanced scalability for managing large crowds in high-density environments including airport terminals. The A3C-based model offers a cost-effective alternative to full-scale evacuation drills by enabling virtual scenario testing, supports proactive safety planning through predictive modeling, and contributes to the development of intelligent decision-support tools that improve coordination and reduce response time during emergencies. Full article

Accurate short-term passenger forecasts help regional airports align capacity with demand and plan investments effectively. Drawing on quarterly traffic data for 2010–2024 supplied by the Polish Civil Aviation Authority, this study employs Holt–Winters exponential smoothing to predict passenger volumes at Szczecin–Goleniów Airport for 2025. Additive and multiplicative formulations were parameterized with Excel Solver, using the mean absolute percentage error to identify the better-fitting model. The additive version captured both the steady post-pandemic recovery and pronounced seasonal peaks, indicating that passenger throughput is likely to rise modestly year on year, with the highest loads expected in the summer quarter and the lowest in early spring. These findings suggest the airport should anticipate continued growth and consider adjustments to terminal capacity, apron allocation, and staffing schedules to maintain service quality. Because the Holt–Winters method extrapolates historical patterns and does not incorporate external shocks—such as economic downturns, policy changes, or public health crises—its projections are most reliable over the short horizon examined and should be complemented by scenario-based analyses in future work. This study contributes to sustainable airport management by providing a reproducible, data-driven forecasting framework that can optimize resource allocation with minimal environmental impact. Full article

The optimisation of airport infrastructure capacities lacks adequate tools that would enable airport owners and managers to make strategic decisions related to sustainable development and strengthening multimodal connectivity. Assessing the sustainability of the transport system is one of the important issues in creating transport policies worldwide. In this research, the methodology of multi-criteria decision making (MCDM) was used, which can be applied to decision making and the evaluation of transport projects, considering more than one criterion in the selection process. The Stepwise Weight Assessment Ratio Analysis (SWARA) method is one of the new MCDM methods. The SWARA method will assess the weights of the selected main criteria and sub-criteria for the multimodal connection of airports to the railway transport infrastructure. In this method, the expert plays an important role in the evaluation and calculation of the criteria weights. This research also aims to respond to the need to define a framework for objective and transparent decision-making based on the assessment of the weighting factors of the selected main criteria and sub-criteria. To assess the justification for the choice of railway transport for connecting airports, financial, traffic, environmental, and availability criteria were used. Full article

The advancement of sustainable economic development has become a strategic imperative for enterprises aiming to combine financial development with environmental and social responsibility. In this regard, strategic enterprise management (SEM) has a critical role in incorporating the aspects of sustainability into decision making. The present paper suggests a multicriteria decision-making framework that utilizes fuzzy TOPSIS in assessing and ranking sustainability integration aspects in organizations. By considering the intrinsic vagueness of sustainability analysis, the fuzzy TOPSIS model enables the systematic analysis of environmental, social, and governance (ESG) factors by companies for ensuring their alignment to corporate strategic goals. A case study of a major international airport in Greece demonstrates how the proposed methodology assists strategic choice making, balancing economic viability and sustainable value creation. The results show primary trade-offs among human capital investment, environmental footprint reduction, and stakeholder communication, demonstrating how companies can enhance long-term resilience and competitiveness. This research adds to the existing literature by giving an integrated strategic enterprise management framework with the use of decision support instruments to foster sustainability-oriented corporate governance and strategic efficacy. The suggested model is flexible and can be applied in any industry, hence being a benchmark for sustainable business practice. This paper contributes to the literature by integrating fuzzy TOPSIS with balanced scorecard in the context of airport strategic sustainability management, offering both methodological advancement and empirical insights for transport and supply chain enterprises. Full article

This paper provides a novel approach to airport sustainability with a comparative analysis of frameworks presented by Airports Council International (ACI) and the World Economic Forum (WEF), a case study on environmental social governance (ESG) reporting for large US airports, a historical perspective and discussion regarding legal considerations, and sustainability metrics. Airport sustainability reporting provides numerous advantages, including enhanced transparency and accountability, and it also supports risk management, regulatory compliance, operational efficiency, risk management, community engagement, and investor relations. There are 30 large hub airports in the US, and each one of these publishes information on sustainability, which may consist of a sustainability report, reports on sustainability related topics, or website information. Eight of these large US airports publish an ESG report. ESG reports are of increasing interest due to their use internationally and due to the role of ESG reports in investment decisions. This paper presents an analysis of the information contained in ESG reports published by US airports and compares the frame of reference used by airports that utilize UN Sustainable Development Goals (SDGs) in their reporting. Case studies of ESG reports for Salt Lake City and Dallas Fort Worth Airports are presented to illustrate ESG reports, and the use of the SDG identified in these reports is compared the framework identified by Airports Council International (ACI) and the World Economic Forum (WEF). The discussion of airport ESG reporting provides a thorough and contextual review of the topic and examines how this framework may evolve to address the increasing interest in ESG reporting for US airports. The information provided may be used by airports to improve their sustainability reporting. Full article

Airport sustainability has gained increasing attention as the aviation industry faces the challenge of balancing economic growth, environmental responsibility, and social standards. This study conducts a systematic literature review (SLR) using the OpenAlex database. The PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was applied to refine the selection process, resulting in 66 relevant studies. Then, a bibliometric–systematic literature review (B-SLR) approach was employed to analyze trends and identify research gaps. The findings indicate that most studies often focus on two sustainability pillars at a time, while neglecting a fully integrated perspective. Not many research works simultaneously address all three dimensions of sustainability (economic, environmental, and social), leading to fragmented insights into sustainable airport management. Notably, some industry-driven reports are starting to suggest emerging holistic approaches, but the majority of the academic literature remains segmented. Hence, this study highlights the need for a more comprehensive research framework that considers environmental, economic, and social factors concurrently. Future research should integrate these dimensions to develop practical and well-balanced sustainability strategies; while methodological limitations may exist in this work, such as language constraints and dataset selection criteria, this review provides valuable insights into airport sustainability and lays the groundwork for further scientific studies. Full article

Aviation emissions significantly impact air quality, contributing to environmental degradation and public health risks. This study aims to assess the impact of aviation-related emissions on air quality at Alexandroupolis Regional Airport, Greece, and evaluate the role of meteorological factors in pollution dispersion. Using machine learning models, we analyzed emissions data, including CO₂, NOx, CO, HC, SOx, PM_2.5, fuel consumption, and meteorological parameters from 2019–2020. Results indicate that NOx and CO₂ emissions showed the highest correlation with air traffic volume and fuel consumption (R = 0.63 and 0.67, respectively). Bayesian Linear Regression and Linear Regression emerged as the most accurate models, achieving an R² value of 0.96 and 0.97, respectively, for predicting PM_2.5 concentrations. Meteorological factors had a moderate influence, with precipitation negatively correlated with PM_2.5 (−0.03), while temperature and wind speed showed limited effects on emissions. A significant decline in aviation emissions was observed in 2020, with CO₂ emissions decreasing by 28.1%, NOx by 26.5%, and PM_2.5 by 35.4% compared to 2019, reflecting the impact of COVID-19 travel restrictions. Carbon dioxide had the most extensive percentage distribution, accounting for 75.5% of total emissions, followed by fuels, which accounted for 24%, and the remaining pollutants, such as NOx, CO, HC, SOx, and PM_2.5, had more minor impacts. These findings highlight the need for optimized air quality management at regional airports, integrating machine learning for predictive monitoring and supporting policy interventions to mitigate aviation-related pollution. Full article

This paper discusses SESAR’s Auto-Steer Taxi at Airport (ASTAIR) project, which seeks to advance airport ground operations including engine-off taxiing to move towards sustainable airports. The ASTAIR concept integrates human–AI teaming to optimize aircraft movement from gates to runways, with the primary objectives of improving predictability, efficiency, and environmental sustainability at large airports. Building on previous initiatives such as SESAR’s AEON, ASTAIR brings high-level automation to tasks like autonomous taxiing and vehicle routing. The system assists operators by calculating conflict-free routes for vehicles and dynamically adjusting operations based on real-time data. Based on workshops with several stakeholders, we describe the operational challenges involved in implementing ASTAIR, including managing parking stand availability and adapting to unforeseen events. A significant challenge highlighted is the human–automation partnership, where AI plays a supportive role but humans retain control over critical decisions, particularly in cases of system failure. The need for clear and consistent collaboration between AI and human operators is emphasized to ensure safety, efficiency, and improved compliance with take-off schedules, which in turn facilitates in-flight optimization. Full article

In the era of globalization and information technology, the aviation industry has experienced rapid growth. However, the increase in flight numbers has exacerbated environmental issues such as exhaust emissions and noise pollution, raising significant concerns across society. This paper aims to explore the current state of environmental pollution within the aviation industry and propose solutions to promote the development of green airports and effective pollution control measures. This study primarily employs a literature analysis. Initially, a preliminary evaluation index system was established to represent various aspects of aviation pollution. The system was then refined and optimized using the entropy weight method. Subsequently, kernel density estimation and Moran index methods are applied to analyze the temporal and spatial trends in the evaluation indicators. An empirical study is conducted to investigate the degree of endogenous correlation and lag effects among the indices. The results are as follows: (1) Regional neutrality in pollution indicators. The spatial autocorrelation test reveals a lack of significant spatial correlation among the studied aviation environmental pollution indicators, indicating that these variables maintain a degree of regional neutrality. (2) Cargo throughput affects aviation environmental pollution. The PVAR model analysis highlights that cargo throughput has a significant self-impact on aviation environmental pollution, indicating that monitoring and managing cargo operations could be crucial in predicting and mitigating future pollution levels. Full article

The taxiing stage of an aircraft is characterized by its long duration, low operating thrust, and low combustion efficiency, resulting in substantial emissions of CO, CO₂, and VOCs, which adversely affect air quality near airports. This study has developed an open-path Fourier transform infrared spectroscopy (OP-FTIR) monitor with second-level time resolution to enable the optical remote monitoring of pollutants during taxiing. Measurements of CO, CO₂, and VOCs were conducted over one month at Hefei Xinqiao International Airport (HXIA). The generalized additive model (GAM) is used for data analysis to reveal complex nonlinear relationships between aircraft emission concentrations and meteorological factors, aircraft models, and their corresponding registration numbers. The GAM analysis shows that among meteorological factors, humidity, and atmospheric pressure have the most significant impact on aircraft exhaust monitoring, with a relative average contribution value as high as approximately six. The explanatory power of aircraft models for emissions is low (R² < 0.18), whereas that of registration numbers is high (R² > 0.6), suggesting that individual differences between aircrafts play a crucial role in emission concentration variations. Furthermore, a noticeable correlation was found between the CO/CO₂ ratio and volatile organic compound (VOC) concentrations (R² > 0.63), indicating that combustion efficiency significantly affects VOC emissions. This study not only advances the real-time remote sensing monitoring of pollutants during aircraft taxiing but also underscores the crucial role of the GAM in identifying the key drivers of emissions, providing a scientific basis for precise environmental protection management and policy-making. Full article

Seaplane operations connect remote areas, promote tourism, and provide unique transportation solutions. After many years of preparations, commercial seaplane operations on a network of 100 water airports and 200 waterways in Greece are about to commence. The network can serve the needs of 1.6 million permanent residents of the Greek islands, the inhabitants of the mainland, and over 35 million annual tourists. This paper aims to conduct a PESTLE (Political, Economic, Social, Technological, Legal, and Environmental) analysis to identify the factors that have delayed operations and those that will affect the success of future operations. As such, 26 factors are examined. It was found that the Greek debt crisis and the COVID-19 pandemic were impediments to operations. The potential of using electric seaplanes is discussed. Recent developments in using drone inspection capabilities for aviation safety are examined. Management strategies for the Etesian winds and other environmental issues are presented. Overall, seaplane operations have enormous potential, while the Greek economic recovery provides favorable conditions for completing the project. The critical issue determining success is executing a multi-faceted business model to ensure seaplane operations’ financial viability. The network can act in synergy with other modes of transportation to help achieve social cohesion, improve tourism services, and foster economic development. Full article

Remote sensing target detection technology in cloud and mist scenes is of great significance for applications such as marine safety monitoring and airport traffic management. However, the degradation and loss of features caused by the obstruction of cloud and mist elements still pose a challenging problem for this technology. To enhance object detection performance in adverse weather conditions, we propose a novel target detection method named CM-YOLO that integrates background suppression and semantic context mining, which can achieve accurate detection of targets under different cloud and mist conditions. Specifically, a component-decoupling-based background suppression (CDBS) module is proposed, which extracts cloud and mist components based on characteristic priors and effectively enhances the contrast between the target and the environmental background through a background subtraction strategy. Moreover, a local-global semantic joint mining (LGSJM) module is utilized, which combines convolutional neural networks (CNNs) and hierarchical selective attention to comprehensively mine global and local semantics, achieving target feature enhancement. Finally, the experimental results on multiple public datasets indicate that the proposed method realizes state-of-the-art performance compared to six advanced detectors, with mAP, precision, and recall indicators reaching 85.5%, 89.4%, and 77.9%, respectively. Full article

Background: In recent decades, the size and passenger capacity of cruise/passenger ships has been associated with noticeable growth; in turn, this has created significant concerns regarding the adequacy of existing evacuation protocols during an “abandon the ship” situation (life threatening emergency). This study provides a systematic overview of related weaknesses and challenges, identifying critical factors that influence evacuation efficiency, and also proposes innovative/interdisciplinary solutions to address those challenges. It further emphasizes the growing complexity of cruise/passenger ship evacuations due to increased vessel size/heavy density of human population, as well as identifying the necessity of addressing both technical and human-centered elements to enhance safety and efficiency of those specific operations. Methods: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, a comprehensive systematic literature search was conducted across academic databases, including Scopus, Science Direct, Google Scholar, and a limited number of academic journals that are heavily maritime-focused in their mission. Emphasis was placed on peer-reviewed articles and certain gray studies exploring the impacts of ship design, human behavior, group dynamics, and environmental conditions on evacuation outcomes. This review prioritized research incorporating advanced simulation models, crowd management solutions (applied in various disciplines, such as stadiums, airports, malls, and ships), real-world case studies, and established practices aligned with contemporary maritime safety standards. Results: The key findings identify several critical factors influencing the overall evacuation efficiency, including ship heeling angles, staircase configurations, and passenger (physical) characteristics (with their mobility capabilities and related demographics clearly standing out, among others). This effort underscores the pivotal role of group dynamics, including the influence of group size, familiarity among the group, and leader-following behaviors, in shaping evacuation outcomes. Advanced technological solutions, such as dynamic wayfinding systems, real-time monitoring, and behavior-based simulation models, emerged as essential tools for optimizing an evacuation process. Innovative strategies to mitigate identified challenges, such as phased evacuations, optimized muster station placements, and tailor made/strategic passenger cabin allocations to reduce congestion during an evacuation and enhance the overall evacuation flow, are also highlighted. Conclusions: Protecting people facing a life-threatening situation requires timely preparations. The need for a holistic evacuation strategy that effectively integrates specific ship design considerations and human factors management, along with inputs related to advanced information technology-related solutions, is the best way forward. At the same time, the importance of real-time adaptive management systems and interdisciplinary approaches to address the challenges of modern cruise/passenger ship evacuations clearly stands out. These findings provide a robust foundation for future research and practical applications, contributing to advancements in maritime safety and the development of efficient evacuation protocols for large-in-size cruise/passenger vessels. Full article