Airport load modelling

Project Overview

In today’s ever-evolving aviation industry, where capacity planning and operational efficiency are paramount, the ability to predict passenger flight load with precision is crucial. At The Hague University’s Data Science research group, we’ve delved deep into this challenge, reviewing historical methodologies and crafting an innovative algorithm using XGBoost, a state-of-the-art machine learning technique.

Objective and Background

The core aim of this research was to develop a reliable prediction algorithm that capitalizes on extensive data from Rotterdam The Hague airport, spanning seven years from 2014 to 2021. Earlier research in this domain examined various prediction methodologies, each offering variable performance results. Our study builds on this foundation, introducing a fresh perspective with the XGBoost approach.


Dataset: The project leverages a comprehensive dataset from Rotterdam The Hague airport. It incorporates distinct features like:

  • Future flight bookings
  • Passenger numbers from prior days
  • Cyclical monthly patterns

These cyclical monthly patterns are uniquely represented using Repeating Radial Functions.

Algorithm Functionality:

Initial Forecasting: The algorithm initially predicts daily passenger volumes, considering the above mentioned features.As can be seen in Figure 1, by overlapping the available data of each year, it is possible to see clear trends in the data driven by seasonality.

Figure 1: Seasonal patterns in passenger flow from 2014 to 2018

Refinement: After forecasting daily volumes, the algorithm hones this data, adapting the daily prediction to an empirical distribution of passenger flow throughout the day. This results in a granular, hourly forecasting format, enabling stakeholders to get a more detailed view of expected traffic. Figure 2 shows an example of the hourly prediction of passenger flow one month in advance.

Figure 2: One month in advance, hourly passenger predictions for upcoming days, based on historical data

Results and Impact

Our rigorous cross-validation of the prediction algorithm over the full dataset returned a Root Mean Square Error (RMSE) of just 300 passengers per day. This result underscores the algorithm’s remarkable precision and dependability. Such innovations mark a transformative phase for the aviation industry, driving data-centric methods to optimize operational efficiency and strategic capacity management. The implications of our study extend beyond mere predictions, emphasizing a sustainable vision for the future of air travel.

Future Endeavors

While the results have been promising, the project, as a part of the ATL (Airport Technology Lab) project funded by Kansen Poor West, remains committed to refining the prediction model even further. With the evolving demands of the aviation industry and the constant influx of data, our research team is poised to enhance and adapt our methodologies, ensuring that they remain relevant, precise, and actionable.


The fusion of intricate data analysis and machine learning in our research project charts a path for the future of aviation. As we harness the power of data to shape a more efficient and sustainable air travel ecosystem, we invite stakeholders, scholars, and enthusiasts to delve into our findings, critique, collaborate, and co-create the future.

Showcase Event Highlights: Airport Technology Lab Showcase Event at Albeda Rotterdam The Hague Airport College

On the 8th and 9th of June, The Hague University’s Data Science research group had the distinct honor of participating in the Airport Technology Lab Showcase event held at Albeda Rotterdam The Hague Airport College. This event was a confluence of innovation, collaboration, and future-oriented thinking, with a focus on presenting the results of the ATL projects and brainstorming the future of digital interactive airports.

Day 1: Showcasing the Future of Aviation:

On the first day, our esteemed colleague, Hani Al-Ers from Haagse Hogeschool, took the stage among other industry stalwarts to present our groundbreaking project on passenger load prediction for the Rotterdam The Hague airport. Utilizing XGBoost, our innovative machine learning technique, Hani shared the meticulous research and impressive results that our team achieved, highlighting its potential to revolutionize capacity planning and operational efficiency in the aviation sector.

The day was packed with other enlightening presentations, each unveiling the pioneering work and results of their respective ATL projects. With a keen emphasis on the outcomes and values achieved in the past years, the presentations provided a holistic view of how the aviation sector is poised for a digital transformation.

Day 2: Envisioning the Next Steps in Aviation Innovation:

The subsequent day was all about looking ahead. Discussions centered on identifying new avenues for innovation and understanding how an expanded ATL consortium could spearhead relevant digitization projects. The interactive sessions and theme tables provided a platform for all attendees to share their insights, thereby fostering an environment of collaborative growth.

Outcome of the event:

The two days were not just about showcasing what has been achieved but also about envisioning what the future holds. As part of The Hague University’s Data Science research group, we are thrilled to be at the forefront of such transformative initiatives. Our participation in this event reaffirms our commitment to harnessing data for the betterment of the aviation industry and beyond.

We extend our heartfelt gratitude to all the organizers, participants, and attendees who made this event a resounding success. As we continue our journey of innovation and research, we eagerly look forward to many more such events that provide a platform for knowledge exchange and collaborative growth.

Published results

We are pleased to announce that this significant research was showcased at the Annual Conference OR65, hosted by the University of Bath from 12th to 14th September 2023. The presentation, titled “Predicting Passenger Flight Load in Aviation using XGBoost,” was delivered by Prof. Lampros Stergioulas, elucidating the methodology and its potential benefits for the aviation industry. The conference, organized by The OR Society, is a renowned platform that gathers esteemed researchers and practitioners in the field of operational research. Further details about the conference and The OR Society can be found on their official website.

Dr. Mathis Mourey
Senior Researcher
I am a Lecturer in Finance/Statistics at THUAS and hold a PhD in Finance from the University Grenoble Alpes (UGA). My research mainly focuses on Systemic Risk measurement. I also have research interests in Data Science and Cryptocurrencies.