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