Abstract
Due to increasing passenger and flight numbers, airports need to plan and schedule carefully to avoid wasting their resources, but also congestion and missed flights. In this paper, we present a deep learning framework for predicting the number of passengers arriving at an airport within a 15-min interval. To this end, a first neural network predicts the number of passengers on a given flight. These results are then being used with a second neural network to predict the number of passengers in each interval.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al.: Tensorflow: A system for large-scale machine learning. In: 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), pp. 265–283 (2016)
Chollet, F., Allaire, J.: Deep Learning mit R und Keras: Das Praxis-Handbuch von den Entwicklern von Keras und RStudio. mitp Professional, Bonn (2018)
Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control, Signal. Syst. 2(4), 303–314 (1989)
Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)
Jayalakshmi, T., Santhakumaran, A.: Statistical normalization and back propagation for classification. Int. J. Comput. Theory Eng. 3, 1793–8201 (2011)
Ketkar, N.: Introduction to keras. In: Deep Learning with Python, pp. 97–111. Springer, Berlin (2017)
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. Technical Report, Cornell University (2014)
Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. Technical Report, LIT AI Lab and Institute of Bioinformatics Johannes Kepler University Linz (2017)
Mamoshina, P., Vieira, A., Putin, E., Zhavoronkov, A.: Applications of deep learning in biomedicine. Mol. Pharm. 13(5), 1445–1454 (2016)
McKinney, W.: pandas: a foundational python library for data analysis and statistics. Python High Perform. Sci. Comput. 14, 1–9 (2011)
Nwankpa, C.E., Ijomah, W., Gachagan, A., Marshall, S.: Activation functions: Comparison of trends in practice and research for deep learning (2018). arXiv:1811.03378v1
Orr, G.B., Müller, K.R.: Efficient backprop. In: Neural Networks Tricks of the Trade. Springer, Berlin (1988)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence (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, 79 (2005). https://doi.org/10.3354/cr030079
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Moll, M., Berg, T., Ewers, S., Schmidt, M. (2020). Predictive Analytics in Aviation Management: Passenger Arrival Prediction. In: Neufeld, J.S., Buscher, U., Lasch, R., Möst, D., Schönberger, J. (eds) Operations Research Proceedings 2019. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-030-48439-2_81
Download citation
DOI: https://doi.org/10.1007/978-3-030-48439-2_81
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-48438-5
Online ISBN: 978-3-030-48439-2
eBook Packages: Business and ManagementBusiness and Management (R0)