Abstract
Over the last few decades, the air transport growth has shown good numbers in comparison with the other years. In fact, most of tourists, who crossed international borders last years, did so by airways. Over the next two decades, the demand for air transport is expected to double. Hence, the density of traffic is going to increase which will result in traffic delays. Delay is one of the most memorable performance indicators in the air transport system. It hurts passengers, airports and airlines. Pilots, air traffic controllers and other aviation personnel were questioned in this study and a survey was established to identify the importance of flight delay reduction. Thus, delays prediction turns out very useful. Flight delay prediction studies have been modeled in different ways. The approach of this work is based on machine learning algorithms. Our model is able to predict whether a scheduled flight will be on-time or delayed. We used relevant and filtered features that, to the best of our knowledge, some of them were not adopted in the previous studies. Holidays, seasons, day of week and the importance of the airport used were added as new features to enhance the accuracy of the prediction system. The resulting model was deployed and used as a flight delay prediction tool. The aim of the deployed application is to inform airport personnel and airlines about flight delays in advance to avoid losses and terminal crowdedness.
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References
Musaddi, R., Jaiswal, A., Girdonia, M., Sanjudharan, M.S.M.: Flight delay prediction using binary classification. Int. J. Emerg. Technol. Eng. Res. (IJETER) 6, 34–38 (2018)
Sternberg, A., Soares, J., Carvalho, D., Ogasawara, E.: A review on flight delay prediction, arXiv preprint arXiv:1703.06118: Computers and society (2017)
Nogueira, K.B., Aguiar, P.H.C., Weigang, L.: Using ant algorithm to arrange taxiway sequencing in airport. Int. J. Comput. Theory Eng. 6, 357 (2014)
Hao, L., Hansen, M., Zhang, Y., Post, J.: New York, New York: two ways of estimating the delay impact of New York airports. Transp. Res. Part E: Logist. Transp. Rev. 70, 245–260 (2014)
Evans, A., Schafer, A., Dray, L.: Modelling airline network routing and scheduling under airport capacity constraints. In: The 26th Congress of ICAS and 8th AIAA ATIO, p. 8855 (2008)
Kotegawa, T., DeLaurentis, D., Noonan, K., Post, J.: Impact of commercial airline network evolution on the US air transportation system. In: Proceedings of the 9th USA/Europe Air Traffic Management Research and Development Seminar (ATM 2011) (2011)
Zhong, Z.W., Varun, D., Lin, Y.J.: Studies for air traffic management R&D in the ASEAN-region context. J. Air Transp. Manag. 64, 15–20 (2017)
Baspinar, B., Koyuncu, E.: A data-driven air transportation delay propagation model using epidemic process models. Int. J. Aerosp. Eng. 2016, 11 p. (2016). ID 4836260
Wong, J.-T., Tsai, S.-C.: A survival model for flight delay propagation. J. Air Transp. Manag. 23, 5–11 (2012)
Mofokeng, T.J., Marnewick, A.: Factors contributing to delays regarding aircraft during A-check maintenance. In: IEEE Technology and Engineering Management Conference (TEMSCON), pp. 185–190 (2017)
Thiagarajan, B., Srinivasan, L., Sharma, A.V., Sreekanthan, D., Vijayaraghavan, V.: A machine learning approach for prediction of on-time performance of flights. In: 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC), vol. 6, pp. 1–6 (2017)
Priyanka, G.: Prediction of airline delays using K-nearest neighbor algorithm. Int. J. Emerg. Technol. Innov. Eng. 4(5), 87–90 (2018). ISSN: 2394 – 6598
Kuhn, N., Jamadagni, N.: Application of machine learning algorithms to predict flight arrival delays, CS229 (2017)
Manna, S., Biswas, S., Kundu, R., Rakshit, S., Gupta, P., Barman, S.: A statistical approach to predict flight delay using gradient boosted decision tree. In: International Conference on Computational Intelligence in Data Science (ICCIDS), vol. 6, pp. 1–5 (2017)
Dand, A., Saeed, K., Yildirim, B.: Prediction of Airline Delays based on Machine Learning Algorithms. In: AMCIS (2019)
Chakrabarty, N., Kundu, T., Dandapat, S., Sarkar, A., Kole, D.K.: Flight arrival delay prediction using gradient boosting classifier. In: Emerging Technologies in Data Mining and Information Security, pp. 651–659 (2019)
Ebenezer, K., Brahmaji Rao, K.N.: Machine learning approach to predict flight delays. Int. J. Comput. Sci. Eng. 6, 231–234 (2018)
Lowden, A., kerstedt, T.: Eastward long distance flights sleep and wake patterns in air crews in connection with a two-day layover. J. Sleep Res. 8, 15–24 (1999)
Panesar, A.: Machine learning algorithms. In: Machine Learning and AI for Healthcare, pp. 119–188. Apress, Berkeley (2019)
Berry, M.W., Mohamed, A., Yap, B.W.: Supervised and Unsupervised Learning for Data Science. Springer, Heidelberg (2019)
Ayyadevara, V.K.: Linear regression. In: Pro Machine Learning Algorithms, pp. 17–47. Apress, Berkeley (2018)
Ayyadevara, V.K.: Decision tree. In: Pro Machine Learning Algorithms, pp. 71–103. Apress, Berkeley (2018)
Ayyadevara, V.K.: Random forest. In: Pro Machine Learning Algorithms, pp. 105–116. Apress, Berkeley (2018)
Ayyadevara, V.K.: Gradient boosting machine. In: Pro Machine Learning Algorithms, pp. 117–134. Apress, Berkeley (2018)
Ayyadevara, V.K.: Pro Machine Learning Algorithms. Apress, New York (2018)
Osisanwo, F.Y., Akinsola, J.E.T., Awodele, O., Hinmikaiye, J.O., Olakanmi, O., Akinjobi, J.: Supervised machine learning algorithms: classification and comparison. Int. J. Comput. Trends Technol. (IJCTT) 48, 128–138 (2017)
Willmott, C.J., Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res. 30(1), 79–82 (2005)
Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci. Model Dev. 7(3), 1247–1250 (2014)
Metrics and scoring: quantifying the quality of predictions. https://www.scikit-learn.org
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Alla, H., Moumoun, L., Balouki, Y. (2021). Flight Arrival Delay Prediction Using Supervised Machine Learning Algorithms. In: Gherabi, N., Kacprzyk, J. (eds) Intelligent Systems in Big Data, Semantic Web and Machine Learning. Advances in Intelligent Systems and Computing, vol 1344. Springer, Cham. https://doi.org/10.1007/978-3-030-72588-4_16
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