Abstract

The paper is research exploring the importance of Artificial Intelligence (AI) and Data Analytics to optimize airport operations and the passenger experience in the environment of the expanding air traffic in the world and the smart city movement. With increasing tasks that airports currently experience, congestion, flight delays, mishandling of baggage, and limited capacity, nowadays AI-based technologies integration is crucial to the operational efficiency, sustainability, and customer satisfaction. This study discusses the use of predictive analytics, machine learning, and clustering models to enhance passenger flows, resource allocation, and performance in general at airports. The research is based on the working efficiency of operations and Smart Airport 4.0 that focus on the use of data to make decisions and the combination of AI, IoT, and analytics used in transport infrastructure. The context of the research is based on the boom in air travels in the period 2005-2018 when passenger numbers are continuously growing and there is need to be smarter in operational approaches. The study will be informed by four following questions: How can artificial intelligence be used effectively to optimize the daily procedures of the airport? What quantifiable changes have been induced by the introduction of AI in terms of elimination of flight delays and enhancement of throughput? What AI solutions can best be used to improve the efficiency and predict demand? Which are the major paradoxes to the introduction of AI into the complicated airport systems? The data collection and analysis strategy involved these questions using secondary data derived out of the Federal Aviation Administration (FAA) and TartanAviation, which contains 18,885 records of air traffic and passenger statistics across 13 years. The study is based on a quantitative data-focused approach that corresponds to a positivist paradigm. They prepared data by cleaning, transformation, and feature engineering to be able to perform advanced analysis. The models such as Linear Regression, random forests, and a Gradient Boosting predictive model have been created to predict passenger volumes, and the K-Means clustering algorithm has been used to identify trends of airline activities. Moving averages and time-series decomposition have also been used to bring out the seasonal variation and growth trends in the long-term. The strength and accuracy of the models were proved with the help of such evaluation metrics as R 2, R MSE, and Silhouette scores. The results show that AI has a greater impact on the capacity to anticipate the demand in passengers, minimize congestion, and foresee operations choke points. Random Forest model (R2 = 0.995) was found to have better predictive performance and hence it is worth considering it in real-time predictive passenger forecasting. Clustering also based on the operational categories of airlines showed four key categories of airlines, whereas the time-series analysis provided some ideas of peaks in relation to the season around July and August. This research comes to the conclusion that AI is not only capable of increasing the predictive capacity but also helps to make proactive decisions and optimize resources.

Library of Congress Subject Headings

Airports--Management--Automation; Internet of things; Predictive analytics; Machine learning

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Khaled Ezzeldeen

Campus

RIT Dubai

Plan Codes

PROFST-MS

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