Predicting Flight Departure Delay at Porto Airport: A Preliminary Study

Hugo Alonso, António Loureiro

2015

Abstract

Managing an airport is very complex. Decisions are often based on common sense and influence several variables, such as flight delay. This paper considers the problem of predicting flight departure delay at Porto Airport. As far as we know, this the first study on the subject. The problem is treated as an ordinal classification task and a suitable approach, based on the so-called unimodal model, is used to predict the delay. The unimodal model is implemented using neural networks and, for comparison purposes, also using trees.

References

  1. Cristianini, N. and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, United Kingdom, 1st edition.
  2. El-Rabbany, A. (2006). Introduction to GPS: The Global Positioning System. Artech House, Norwood, 2nd edition.
  3. Fernández-Navarro, F., Riccardi, A., and Carloni, S. (2015). Ordinal regression by a generalized forcebased model. IEEE Transactions on Cybernetics, 45(4):844-857.
  4. Frank, E. and Hall, M. (2001). A simple approach to ordinal classification. In Proceedings of the 12th European Conference on Machine Learning (ECML 2001), volume 1, pages 145-156.
  5. Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, New York, 2nd edition.
  6. Haykin, S. (2009). Neural Networks and Learning Machines. Prentice Hall, New Jersey, 3rd edition.
  7. Kewley, R., Embrechts, M., and Breneman, C. (2000). Data strip mining for the virtual design of pharmaceuticals with neural networks. IEEE Transactions on Neural Networks, 11(3):668-679.
  8. Pinto da Costa, J. F., Alonso, H., and Cardoso, J. S. (2008). The unimodal model for the classification of ordinal data. Neural Networks, 21:78-91.
  9. Pinto da Costa, J. F., Alonso, H., and Cardoso, J. S. (2014). Corrigendum to “The unimodal model for the classification of ordinal data” [Neural Netw. 21 (2008) 78- 79]. Neural Networks, 59:73-75.
  10. Rao, S. S. (2009). Engineering Optimization: Theory and Practice. John Wiley & Sons, Inc., New Jersey, 4th edition.
  11. Rebollo, J. J. and Balakrishnan, H. (2014). Characterization and prediction of air traffic delays. Transportation Research Part C: Emerging Technologies, 44:231-241.
  12. Tu, Y., Ball, M. O., and Jank, W. S. (2008). Estimating flight departure delay distributions - A statistical approach with long-term trend and short-term pattern. Journal of the American Statistical Association, 103(481):112-125.
  13. Wong, J.-T. and Tsai, S.-C. (2012). A survival model for flight delay propagation. Journal of Air Transport Management, 23:5-11.
Download


Paper Citation


in Harvard Style

Alonso H. and Loureiro A. (2015). Predicting Flight Departure Delay at Porto Airport: A Preliminary Study . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015) ISBN 978-989-758-157-1, pages 93-98. DOI: 10.5220/0005587700930098


in Bibtex Style

@conference{ncta15,
author={Hugo Alonso and António Loureiro},
title={Predicting Flight Departure Delay at Porto Airport: A Preliminary Study},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015)},
year={2015},
pages={93-98},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005587700930098},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015)
TI - Predicting Flight Departure Delay at Porto Airport: A Preliminary Study
SN - 978-989-758-157-1
AU - Alonso H.
AU - Loureiro A.
PY - 2015
SP - 93
EP - 98
DO - 10.5220/0005587700930098