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Enhancing airport services: data-driven analysis of passenger satisfaction and service quality in Southeast Asia

Pholsook Thitinan, Ramjan Sarawut, Wipulanusat Warit

Engineering Management in Production and Services

2025

Vol. 17, no. 2

37--62

Airports encompass a range of service touchpoints that directly impact passenger satisfaction and, consequently, the likelihood of service recommendation. This study investigates the service quality of Southeast Asian airports by applying five supervised machine learning classification models — decision trees, random forests, support vector machines, neural networks, and gradient boosting machines — on passenger satisfaction data extracted from the Skytrax website. The dataset includes evaluations of various service dimensions, such as staff behaviour, queuing time, and overall experience. This study incorporates cross-validation and hyperparameter tuning to identify the most suitable model for classifying passenger satisfaction. Among the models tested, the random forest classifier achieved the highest accuracy (0.91), demonstrating strong robustness and interpretability. Model performance was assessed using confusion matrices, balanced accuracy, the Matthews correlation coefficient (MCC), and ROC curves. Furthermore, SHAP values were used to identify the most influential service touchpoints, highlighting airport staff performance and queue management as key factors. These findings align with existing literature emphasising the pivotal role of well-trained airport employees and efficient queuing systems in shaping positive passenger experiences. Studies have shown that courteous staff interactions, efficient conflict resolution, and reduced waiting times significantly contribute to customer satisfaction and loyalty. Additionally, the integration of smart technologies such as self-service kiosks, automated security systems, and touchless check-in and baggage solutions enhances operational efficiency and aligns with sustainability initiatives. This study offers a data-driven approach for airport managers to optimise service delivery, increase passenger experiences, and tailor improvements to specific airport environments.

An efficient deep learning network for MIMO detection using bayesian optimization

Saini Mehak, Grewal Surender K.

Przegląd Elektrotechniczny

2024

R. 100, nr 12

18--20

Despite the many advantages of Multiple Input Multiple Output (MIMO) Wireless Communication systems, their receiver design is quite a challenging task as there is always a trade-off between the receiver performance and the computational complexity. If performance is optimum, the computational complexity is exceptionally high and vice-versa. In this paper by using Bayesian Optimization, the performance of an AI-based MIMO receiver algorithm, called DetNet is improved. The results show an improvement in detection performance without any increase in time complexity.

Pomimo wielu zalet systemów komunikacji bezprzewodowej MIMO (Multiple Input Multiple Output), ich konstrukcja odbiornika jest dość trudnym zadaniem, ponieważ zawsze istnieje kompromis między wydajnością odbiornika a złożonością obliczeniową. Jeśli wydajność jest optymalna, złożoność obliczeniowa jest wyjątkowo wysoka i odwrotnie. W tym artykule, dzięki zastosowaniu optymalizacji bayesowskiej, wydajność algorytmu odbiornika MIMO opartego na sztucznej inteligencji, zwanego DetNet, została poprawiona. Wyniki pokazują poprawę wydajności wykrywania bez żadnego wzrostu złożoności czasowej.