Journal of Airline Operations and Aviation Management

Journal of Airline Operations and Aviation Management https://jaoam.com/index.php/jaoam Journal of Airline Operations and Aviation Management (ISSN: 2949-7698) is an open access scientific journal. The aim of JAOAM is to publish theoretical and empirical articles aimed to contrast, extend and build scientific knowledge that contributes to advance our understanding of air transport from three perspectives: airline operations & globalization, airline management, and airport management. Please not: There is NO submission fee/subscription fee/readeship fee/ Article-processing fee. All the services provided by this Journal are fully-free of cost. CCNEIR en-US Journal of Airline Operations and Aviation Management 2949-7698 Correlation Between Cabin Airborne Pathogens and Acute Respiratory Infections in Passengers https://jaoam.com/index.php/jaoam/article/view/109 Cabin airflow has a greater impact on passenger health than long assumed. Recent CFD modelling and flight data show pathogens spread far beyond the two-row rule, carried five to seven rows and across aisles by passenger heat plumes and ventilation flows. Seat-specific exposure reveals a strong dose–response link, with high-exposed passengers facing over triple the infection risk. Machine learning models further pinpoint clusters with high accuracy. These findings demand a rethink of in-flight health protocols: airflow design, not seat proximity, drives infection risk. Integrating CFD, epidemiology, and predictive analytics offers aviation medicine a shift from reactive measures to proactive passenger safety. Sergey Gupalo Copyright (c) 2025 2025-10-02 2025-10-02 4 2 1 14 10.64799/jaoam.V4.I2.1 Advanced Machine Learning Approaches for Predictive Inventory Management in Aviation: A Comprehensive Synthetic Data-Driven Analysis https://jaoam.com/index.php/jaoam/article/view/110 The airline industry confronts increasingly sophisticated challenges in inventory management on board their aircraft fleet, demanding advanced predictive models capable of navigating the complex interdependencies of resource allocation across heterogeneous operational environments. This comprehensive research introduces a groundbreaking methodological framework for inventory efficiency prediction, integrating cutting-edge machine learning techniques with innovative synthetic data generation strategies. This study's primary contributions are threefold: (1) the creation of a high-fidelity synthetic dataset capturing the intricate nuances of aviation operational dynamics, (2) the implementation of advanced machine learning algorithms for unprecedented predictive accuracy, and (3) the development of a holistic analytical approach that provides actionable strategic insights for industry stakeholders. The synthetic dataset generated in this research represents a significant methodological innovation, meticulously constructed to simulate realistic aviation operational conditions. By incorporating a comprehensive array of multidimensional features—including flight duration, route complexity, aircraft specifications, passenger demographic profiles, maintenance histories, seasonal fluctuations, and geospatial variations—we establish an unparalleled foundation for predictive modeling. Employing a sophisticated ensemble of machine learning methodologies, including advanced regression techniques, probabilistic classification algorithms, and hybrid predictive models, we achieved exceptional computational performance. Our regression models demonstrated extraordinary explanatory power, while classification models exhibited near-perfect risk assessment capabilities. The research presents several critical methodological innovations: (1) a novel synthetic data generation protocol that preserves statistical distributions and complex interdependencies, (2) advanced feature engineering and preprocessing techniques that enhance model interpretability and generalizability, and (3) a hybrid machine learning approach that integrates probabilistic reasoning with empirical predictive modeling. Our findings provide transformative, data-driven strategies for aviation inventory management, offering unprecedented insights into resource optimization, operational risk mitigation, and efficiency enhancement. The proposed framework not only advances academic understanding of complex inventory systems but also presents practical, implementable solutions for airline industry stakeholders seeking to leverage advanced analytical methodologies. Parth Purohit Thomas Feuerle Peter Hecker Copyright (c) 2025 2025-10-02 2025-10-02 4 2 15 40 10.64799/jaoam.V4.I2.2 Modeling U.S. Air Carriers’ Profitability Utilizing Hierarchical Multiple Regression: Financial Predictors of Net Income https://jaoam.com/index.php/jaoam/article/view/111 Hierarchical multiple regression (HRM) is applied to examine the incremental influence of aviation market identity, revenue generation, and cost structures on airline net income across U.S.-based carriers from 2022 to 2024. This study utilizes archival data obtained from an aviation database that complies with the U.S. Department of Transportation Form 41 airline financial disclosures (AviationDB, 2024). The analysis categorizes ten independent variables into three theory-informed sets aligned with the income statement equation: Set A (Aviation Market Identity), Set B (Revenues and Incomes), and Set C (Expenses and Costs). All regression assumptions were rigorously assessed and met, with model stability confirmed via multicollinearity diagnostics, linearity, reliability, validity, and residual analysis. Results demonstrated that aviation market identity alone explained a modest portion of the variance (R² = 0.01). The inclusion of revenue-related variables added 0.13 explanatory power, while the final step of introducing expense variables contributed an additional 0.81, resulting in a highly predictive model (R² = 0.95). Operating revenues and expenses emerged as the strongest predictors. The findings emphasize the dominant role of cost management in driving air carrier profitability and net income, reinforcing the strategic value of HMR in disentangling financial drivers within complex commercial systems. This research contributes to aviation financial modeling by offering empirical insight into income dynamics and supporting data-driven decision-making for air carrier executives, regulators, and aviation financial policy strategists. Abdulaziz Alaqil Gender Peng Brooke Wheeler Vivek Sharma Copyright (c) 2025 2025-10-02 2025-10-02 4 2 41 53 10.64799/jaoam.V4.I2.3