Abstract
The fast growth of global air travel made the accurate prediction of passenger flow a challenge for airport operations. Traditional forecasting models, like regression and SARIMA, have been useful in stable conditions but usually do not show the non-linear and dynamic changes driven by weather, infrastructure, and behaviour of passengers. Therefore, recent studies showed the potential of machine learning and big data to improve the accuracy of forecasting. However, most of the work is still limited to specific airports, datasets, or operations. This study develops a machine learning–based predictive framework for short-interval passenger throughput at Dubai International Airport. Using available flight movement data from Dubai Pulse, high-granularity 15-minute operational datasets were constructed following extensive cleaning, temporal alignment, and feature engineering. Multiple models were implemented and evaluated. Model performance was assessed using accuracy, precision, recall, and F1-score derived from multi-class confusion matrices. The experimental results show that machine learning, particularly LSTM networks, improves forecasting accuracy compared to conventional approaches. The framework gives reliable short-interval predictions that can support proactive staff planning, passenger queue management, and operational decision-making.
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
Khalil Al Hussaeni
Recommended Citation
Alfalasi, Khalifa, "A Machine Learning Framework for Forecasting Passenger Flow at Dubai International Airport" (2025). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12479
Campus
RIT Dubai
