Abstract
Flight Take-Off Time (TOT) delay prediction is essential to optimizing capacity-related tasks in Air Traffic Management (ATM) systems. Recently, the ATM domain has put afforded to predict TOT delays using machine learning (ML) algorithms, often seen as “black boxes”, therefore it is difficult for air traffic controllers (ATCOs) to understand how the algorithms have made this decision. Hence, the ATCOs are reluctant to trust the decisions or predictions provided by the algorithms. This research paper explores the use of explainable artificial intelligence (XAI) in explaining flight TOT delay to ATCOs predicted by ML-based predictive models. Here, three post hoc explanation methods are employed to explain the models’ predictions. Quantitative and user evaluations are conducted to assess the acceptability and usability of the XAI methods in explaining the predictions to ATCOs. The results show that the post hoc methods can successfully mimic the inference mechanism and explain the models’ individual predictions. The user evaluation reveals that user-centric explanation is more usable and preferred by ATCOs. These findings demonstrate the potential of XAI to improve the transparency and interpretability of ML models in the ATM domain.
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References
Bardach, M., Gringinger, E., Schrefl, M., Schuetz, C.G.: Predicting flight delay risk using a random forest classifier based on air traffic scenarios and environmental conditions. In: 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), pp. 1–8. IEEE (2020)
Biecek, P.: Dalex: explainers for complex predictive models in R. J. Mach. Learn. Res. 19(1), 3245–3249 (2018)
Breiman, L.: Bagging predictors. Mach. Learn. 24, 123–140 (1996)
Busa-Fekete, R., Szarvas, G., Elteto, T., Kégl, B.: An apple-to-apple comparison of learning-to-rank algorithms in terms of normalized discounted cumulative gain. In: ECAI 2012-20th European Conference on Artificial Intelligence: Preference Learning: Problems and Applications in AI Workshop, vol. 242. IOS Press (2012)
Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)
Dalmau, R., Ballerini, F., Naessens, H., Belkoura, S., Wangnick, S.: An explainable machine learning approach to improve take-off time predictions. J. Air Transp. Manag. 95, 102090 (2021)
Dalmau Codina, R., Belkoura, S., Naessens, H., Ballerini, F., Wagnick, S.: Improving the predictability of take-off times with machine learning: a case study for the maastricht upper area control centre area of responsibility. In: Proceedings of the 9th SESAR Innovation Days, pp. 1–8 (2019)
Degas, A., et al.: A survey on artificial intelligence (AI) and explainable AI in air traffic management: current trends and development with future research trajectory. Appl. Sci. 12(3), 1295 (2022)
Guo, Z., et al.: SGDAN-a spatio-temporal graph dual-attention neural network for quantified flight delay prediction. Sensors 20(22), 6433 (2020)
Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Metrics for explainable AI: challenges and prospects. arXiv abs/1812.04608 (2018)
Islam, M.R., Ahmed, M.U., Barua, S., Begum, S.: A systematic review of explainable artificial intelligence in terms of different application domains and tasks. Appl. Sci. 12(3), 1353 (2022)
Joshi, A., Kale, S., Chandel, S., Pal, D.K.: Likert scale: explored and explained. Br. J. Appl. Sci. Technol. 7(4), 396 (2015)
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Ribeiro, M.T., Singh, S., Guestrin, C.: “why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)
Sanaei, R., Pinto, B.A., Gollnick, V.: Toward ATM resiliency: a deep CNN to predict number of delayed flights and ATFM delay. Aerospace 8(2), 28 (2021)
Shapley, L.S.: A value for n-person games. In: Classics in Game Theory, vol. 69 (1997)
Wang, Y., Wang, L., Li, Y., He, D., Liu, T.Y.: A theoretical analysis of NDCG type ranking measures. In: Conference on Learning Theory, pp. 25–54. PMLR (2013)
Yu, B., Guo, Z., Asian, S., Wang, H., Chen, G.: Flight delay prediction for commercial air transport: a deep learning approach. Transp. Res. Part E Logist. Transp. Rev. 125, 203–221 (2019)
Acknowledgements
This work was financed by the European Union’s Horizon 2020 within the framework SESAR 2020 research and innovation program under grant agreement N. 894238, project Transparent Artificial Intelligence and Automation to Air Traffic Management Systems (ARTIMATION) and BrainSafeDrive, co-funded by the Vetenskapsrådet - The Swedish Research Council and the Ministero dell’Istruzione dell’Università e della Ricerca della Repubblica Italiana under Italy-Sweden Cooperation Program.
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Jmoona, W. et al. (2023). Explaining the Unexplainable: Role of XAI for Flight Take-Off Time Delay Prediction. In: Maglogiannis, I., Iliadis, L., MacIntyre, J., Dominguez, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 676. Springer, Cham. https://doi.org/10.1007/978-3-031-34107-6_7
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