Abstract
As the air traffic volume has increased significantly over the world, the great mass of traffic management data, named as Big Data, have also accumulated day by day. This factor presents more opportunities and also challenges as well in the study and development of Air Traffic Management (ATM). Usually, Decision Support Systems (DSS) are developed to improve the efficiency of ATM. The main problem for these systems is the data analysis to acquisition sufficient knowledge for the decision. This paper introduces the application of the methods of Data Mining to get the knowledge from air traffic Big Data in management processes. The proposed approach uses a Bayesian network for the data analysis to reduce the costs of flight delay. The process makes possible to adjust the flight plan such as the schedule of arrival at or departure from an airport and also checks the airspace control measurements considering weather conditions. An experimental study is conducted based on the flight scenarios between Los Angeles International Airport (LAX) and Miami International Airport (MIA).
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Pozzi S, Valbonesi C, Beato V, Volpini R, Giustizieri FM, Lieutaud F, Licu A (2011) Safety monitoring in the age of big data. In: Ninth USA/Europe air traffic management research and development seminar (ATM2011)
Chung HM, Gray P (1999) Special section: data mining. J Manage Inf Syst 16(1):11–17
Agrawal R, Shafer JC (1996) Parallel mining of association rules. IEEE Eng Med Biol Mag Trans Knowl Data Eng 8:962–969
Fayyad U, Piatetsky-Shapiro G, Smith P, Uthurusamy R (1996) Advances in knowledge discovey and data mining. In: Association for the advancement of artificial intelligence conference (AAAI). MIT Press
Berry MJA, Linoff G (1997) Data mining techniques. Wiley, New York (1997)
Groth R (1998) Data mining. Prentice Hall, Saddle River
Goebel M, Gruenwald L (1999) A survey of data mining and knowledge discovery software tools. Association for computing machinery’s special interest group on knowledge discovery and data mining (SIGKDD) explorations
Hand D, Mannila H, Smyth P (2001) Principles of data mining. MIT Press, Cambridge
Schaffer C (1994) A conservation law for generalization performance. In: The 1994 international conference on machine learning. Morgan Kaufmann
Kibler D, Langley P (1988) Machine learning as an experimental science. In: Proceedings of the third European working session on learning. Glasgow Pittman, vol 1, pp 81–92
Laney D (2014) 3D data management: controlling data volume, velocity, and variety. Meta Group (2001) Available via Gartner Group. http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf. Accessed 10 Jul 2014
Laney D (2014) The importance of ‘big data’: a definition. (2012) Available via Gartner Group. https://www.gartner.com/doc/2057415/importance-big-data-definition.Cited. Accessed 03 Jul 2014
Pozzi S, Valbonesi C, Beato V, Volpini R, Giustizieri FM, Lieutaud F, Licu A (2011) Safety monitoring in the age of big data: from description to intervention. In: Ninth USA/Europe air traffic management research and development seminar (ATM2011)
Lavalle S, Hopkins MS, Lesser E, Shockley R, Kruschwitz N (2010) Big data, analytics and the path from insights to value. MIT Sloan Manage Rev
Pozzi S, Lotti G, Matrella G, Save L (2008) Turning information into knowledge: the case of automatic safety data gathering. EUROCONTROL annual safety R&D seminar
Jordan MI (2007) Learning in graphical models. SAE technical paper, MIT Press
Pearl J (1987) Evidential reasoning using stochastic simulation of causal models. Artif Int 32(2):245–258
Ye X, Kamath G, Osadciw LA (2009) Using bayesian inference for sensor management of air traffic control systems. In: Computational intelligence in multi-criteria decision-making (MCDM), pp 23–29
Han S, DeLaurentis D (2011) Air traffic demand forecast at a commercial airport using bayesian networks. In: 11th AIAA aviation technology, integration and operations (ATIO) conference, Virginia Beach, VA
Jensen FV (2001) Bayesian networks and decision graphs. Springer, Berlin
Alba E, Mendoza M (2007) Bayesian forecasting methods for short time series. Int J Appl Forecast 8:41–44
Agogino A, Tumer K (2009) Learning indirect actions in complex domains: action suggestions for air traffic control. Adv Complex Syst 12(4–5):493–512 (World Scientific Company)
Agogino A, Tumer K (2008) Regulating air traffic flow with coupled agents. Advances in complex systems. In: Proceedings of 7th international conference on autonomous agents and multiagent systems
DECEA—Air Traffic Control Department of the Brazilian Air Force: Regras do ar e serviços de tráfego aéreo: ICA 100–12 (2012). Available via DECEA. http://publicacoes.decea.gov.br/?i=publicacao&id=2558. Accessed 19 Jun 2014
Piatetsky-shapiro G, Brachman R, Khabaza T, Kloesgen W, Simoudis E (1996) An overview of issues in developing industrial data mining and knowledge discovery applications. In: Proceedings of knowledge discovery in databases 96. AAAI Press, Menlo
Cheng T, Cui D, Cheng P (2003) Data mining for air traffic flow forecasting: a hybrid model of neural network and statistical analysis. In: Proceedings 2003 IEEE intelligent transportation systems, vol 1, pp 211–215
Weigang L, Dib MVP, Cardoso DA (2004) Grid service agents for real time traffic synchronization. In: Proceedings of the 2004 IEEE/WIC/ACM international conference on web intelligence, pp 619–623
Kulkarni D (2007) Integrated use of data mining and statistical analysis methods to analyze air traffic delays. SAE technical paper
Crespo AMF, Weigang L, Barros A (2012) Reinforcement learning agents to tactical air traffic flow management. Int J Aviat Manage 1(3):145–161
Zanin M, Perez D, Kolovos D, Paige R, Chatterjee K, Horst A, Rumpe B (2011) On demand data analysis and filtering for inaccurate flight trajectories. In: Proceedings of the SESAR innovation days, EUROCONTROL
Acknowledgments
This work has been partially supported by the Brazilian National Council for Scientific and Technological Development - CNPq by the processes of No. 304903/2013-2 and No. 232494/2013-4. This paper is dedicated to the memory of Professor M.G. Karlaftis for his friendship and professional exemplar to the community.
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Cruciol, L.L.B.V., Weigang, L., Clarke, JP., Li, L. (2015). Air Traffic Flow Management Data Mining and Analysis for In-flight Cost Optimization. In: Lagaros, N., Papadrakakis, M. (eds) Engineering and Applied Sciences Optimization. Computational Methods in Applied Sciences, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-319-18320-6_5
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