Predicting customer purpose of travel in a low-cost travel environment—A Machine Learning Approach

Samunderu, Eyden; Farrugia, Michael

doi:10.1016/j.mlwa.2022.100379

Cited by 5 publications

(2 citation statements)

References 69 publications

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“…Deleting duplicate and unneeded data from the passenger satisfaction dataset speeds up and simplifies the building of the prediction mode [42].…”

Section: Step 1: Removing Redundant Datamentioning

confidence: 99%

An Optimized Deep Learning Approach for Improving Airline Services

Ouf¹

2023

Computers, Materials &Amp; Continua

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The aviation industry is one of the most competitive markets. The most common approach for airline service providers is to improve passenger satisfaction. Passenger satisfaction in the aviation industry occurs when passengers' expectations are met during flights. Airline service quality is critical in attracting new passengers and retaining existing ones. It is crucial to identify passengers' pain points and enhance their satisfaction with the services offered. The airlines used a variety of techniques to improve service quality. They used data analysis approaches to analyze the passenger point data. These solutions have focused simply on surveys; consequently, deeplearning approaches have received insufficient attention. In this study, deep neural networks with the adaptive moment estimation Adam optimization algorithm were applied to enhance classification performance. In previous studies, the quality of the dataset has been ignored. The proposed approach was applied to the airline passenger satisfaction dataset from the Kaggle repository. It was validated by applying artificial neural networks (ANNs), random forests, and support vector machine techniques to the same dataset. It was compared with other research papers that used the same dataset and had a similar problem. The experimental results showed that the proposed approach outperformed previous studies. It has achieved an accuracy of 99.3%.

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“…Deleting duplicate and unneeded data from the passenger satisfaction dataset speeds up and simplifies the building of the prediction mode [42].…”

Section: Step 1: Removing Redundant Datamentioning

confidence: 99%

An Optimized Deep Learning Approach for Improving Airline Services

Ouf¹

2023

Computers, Materials &Amp; Continua

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“…Nations with a strong aerospace presence enjoy benefits like boosted tourism, enhanced employment opportunities, technological advancements, and overall economic prosperity [2,3]. A comprehensive study of airline markets is essential, which benefits not only frequent flyers and industry stakeholders but broader intellectual and economic spheres [4,5].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Aspect Informed GRU: A Hybrid Model of Flight Fare Forecasting with Sentiment Analysis

Degife,

Lin

2024

Applied Sciences

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This paper presents an advanced method for forecasting flight fares that combines aspect-based sentiment analysis (ABSA) with deep learning techniques, particularly the gated recurrent unit (GRU) model. This approach leverages historical airline ticket transaction data and customer reviews to better understand airline fare dynamics and the impact of customer sentiments on pricing. The aspect analysis extracts key service aspects from customer feedback and provides insightful correlations with airfare. These were further categorized into nine groups for sensitivity analysis, which offered a deeper understanding of how each group influences customers’ attitudes. This ABSA-driven forecasting method marks a departure from traditional models by utilizing sentiment data alongside airline transaction data to improve the predictive accuracy. Its effectiveness is demonstrated through metrics including a root mean square error (RMSE) of 0.0071, a mean absolute error (MAE) of 0.0137, and a coefficient of determination (R2) of 0.9899. Additionally, this model shows strong prediction performance in both short- and long-term fare predictions. It not only advances airfare forecasting methods but provides valuable insights for decision makers of airline industry to refine the pricing strategies or make improvements when it is indicated some services require further attention.

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