Projecte matriculat
Títol: Taxi-in time and approach delays prediction for Barcelona-El Prat Airport using machine learning techniques
Director/a: ERITJA OLIVELLA, ANTONI-JOSEP
Departament: FIS
Títol: Taxi-in time and approach delays prediction for Barcelona-El Prat Airport using machine learning techniques
Data inici oferta: 13-01-2026 Data finalització oferta: 13-09-2026
Estudis d'assignació del projecte:
GR ENG SIS TELECOMUN- GR ENG SIST AEROESP
- GR ENG TELEMÀTICA
| Tipus: Individual | |
| Lloc de realització: Fora UPC | |
| Supervisor/a extern: Marta Sánchez Cidoncha | |
| Institució/Empresa: CRIDA | |
| Titulació del Director/a: Enginyer/a Aeronàutic/a | |
| Paraules clau: | |
| Machine Learning, Airport Operations, Data Analysis, Regression Models | |
| Descripció del contingut i pla d'activitats: | |
| Efficient airport operations are essential for reducing delays, fuel consumption, and emissions. One component of these operations is the taxi-in time, defined as the time between aircraft landing and arrival at the assigned gate. This duration can vary significantly due to runway configuration, traffic density, airport layout, aircraft characteristics, and operational procedures.
In this bachelor's thesis, you will develop a machine learning model to predict aircraft taxi-in and approach times at Barcelona-El Prat Airport using historical flight and airport operational data. You will analyse operational factors influencing taxi-in performance, touch-down times, and approach delays, and translate them into features usable by machine-learning algorithms. The work combines knowledge of aviation operations with data-driven modelling, providing insight into how advanced analytics can support airport decision-making. |
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| Orientació a l'estudiant: | |
| Background:
- Basic knowledge of aircraft operations - Basic knowledge of airport and airside operations - Basic programming skills (preferably Python) - Introductory knowledge of statistics or data analysis is an advantage Hardware: No special hardware required; a standard personal computer or laptop is sufficient Software: - Python - Data analysis and machine learning libraries (e.g., pandas, NumPy, scikit-learn) - Visualisation tools (e.g., matplotlib, seaborn, altair) - Optional: PyCharm, Jupyter Notebook, VS Code |
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| Requereix activitats hardware: No | |
| Requereix activitats software: Sí Sistema operatiu: Disc (Gb): | |
| Horari d'atenció a estudiants per a l'assignació de projecte: |
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