Estimation of Number of Flight Using Particle Swarm Optimization and Artificial Neural Network

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ISSN online: 2255-2863

Prefijo Doi: 10.14201

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Ebru Pekel Özmen
Istanbul University-Cerrahpasa
Turkey
Engin Pekel
Hitit University
Turkey
Vol. 8 No. 3 (2019), Articles, pages 27-33
DOI: https://doi.org/10.14201/ADCAIJ2019832733
Accepted: Feb 25, 2020
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Abstract

The number of flight (NF) is one of the key factors for the administration of the airport to evaluate the apron capacity and airline companies to fix the size of the flight. This paper aims to estimate the monthly NF by performing particle swarm optimization (PSO) and artificial neural network (ANN). Performed PSO-ANN algorithm aims to minimize the proposed evaluation criterion in the training stage. PSO-ANN based on the proposed evaluation criterion offers satisfying fitness values with respect to correlation coefficient and mean absolute percentage error in the training and testing stage.

Keywords:

artificial neural network, airport, particle swarm optimization, estimation

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References

Farhadi, F., A. Ghoniem and M. Al-Salem (2014). “Runway capacity management-an empirical study with application to Doha International Airport”. Transportation Research Part E: Logistics and Transportation Review 68: 53-63.

Kim, S. and D. H. Shin (2016). “Forecasting short-term air passenger demand using big data from search engine queries”. Automation in Construction 70: 98-108.

Kim, Y. J., S. Choi, S. Briceno and D. Mavris (2016). A deep learning approach to flight delay prediction. 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC).

Reilly, D. L. and L. N. Cooper (1995). An overview of neural networks: early models to real world systems. How We Learn; How We Remember: Toward An Understanding Of Brain And Neural Systems: Selected Papers of Leon N Cooper, World Scientific: 300-321.

Suryani, E., S.-Y. Chou and C.-H. Chen (2010). “Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics framework”. Expert Systems with Applications 37(3): 2324-2339.

Xie, G., S. Wang and K. K. Lai (2014). “Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches”. Journal of Air Transport Management 37: 20-26.

Zhang, G., B. E. Patuwo and M. Y. Hu (1998). “Forecasting with artificial neural networks: The state of the art”. International journal of forecasting 14(1): 35-62.

Zhang, M., K. Liu, H. Yu and J. Yu (2016). Short-term Forecasting Method of Air Traffic Flow based Neural Network Ensemble. 2016 2nd Workshop on Advanced Research and Technology in Industry Applications (WARTIA-16), Atlantis Press.