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Flight Delays Prediction using Supervised Learning Algorithm
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The ceaseless development in the interest for air transportation surpasses the limit of existing foundation, generally prompting questionable flight plans, long flight delays and uncertainties in landing/takeoff and taxi times. In light of the multi-target streamlining, a heuristic calculation thinking about vulnerabilities in flight landing/takeoff time is intended to accomplish an improvement in airplane terminal throughput and a decrease in flight delay. We are analyzing the forecasts, timings to make these delays reduce by small amount. With our future proposal, we can make the datasets real-time and reduces flight delay by huge hunk of time. The supervised machine learning algorithm helps us to find the prediction with more accuracy.
Keywords
Flight Delays Prediction, Hadoop, Takeoff Time
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- M. Güvercin, N. Ferhatosmanoglu, and B. Gedik, “Forecasting flight delays using clustered models based on airport networks,” 2019.
- M. Hansen, and C. Y. Hsiao, “Going South? An econometric analysis of US airline flight delays from 2000 to 2004,” Presented at the 84th Annual Meeting of the Transportation Research Board (TRB), Washington D.C., 2005.
- S. S. Allan, J. A. Beesley, J. E. Evans, and S. G. Gaddy, “Analysis of delay causality at network international airport,” 2001.
- A. Rosen, “Flights delays on US airlines: The impact of congestion externalities in hub and spoke networks,” 2002.
- P. Chandraa, Prabakaran N., and Kannadasan R., “Airline delay predictions using supervised machine learning,” International Journal of Pure and Applied Mathematics, vol. 119, no. 7, pp. 329-337, 2018.
- S. Shaik, and K. P. Surya Teja, “Flight delay prediction using machine learning algorithm XGBoost,” Journal of Advanced Research in Dynamical and Control Systems, vol. 11, no. 5, pp. 379-388, 2019.
- A. Barrat, M. Barthelemy, R. Pastor-Satorras, and A. Vespignani, “The architecture of complex weighted networks,” PNAS, vol. 101, no. 11, pp. 3747-3752, 2004.
- S. Li, Y. Xu, M. Zhu, S. Ma, and H. Tang, “Remote sensing airport detection based on end-to-end deep transferable convolutional neural networks,” IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 10, pp. 1640-1644, Oct. 2019.
- U. Bhatia, D. Kumar, E. Kodra, and A. R. Ganguly, “Network science based quantification of resilience demonstrated on the Indian Railways network,” PLoS ONE, vol. 10, no. 11, e0141890, 2015.
- P. Fleurquin, J. J. Ramasco, and V. M. Eguiluz,“Systemic delay propagation in the US airport
- network,” Scientific Reports, vol. 3, 2013, Art. no. 1159.
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