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http://dx.doi.org/10.12985/ksaa.2021.29.2.084

A Study on the Visualization of an Airline's Fleet State Variation  

Lee, Yonghwa (한국항공대학교 항공교통물류학과)
Lee, Juhwan (한국항공대학교 항공교통물류학과)
Lee, Keumjin (한국항공대학교 항공교통물류학부)
Publication Information
Journal of the Korean Society for Aviation and Aeronautics / v.29, no.2, 2021 , pp. 84-93 More about this Journal
Abstract
Airline schedule is the most basic data for flight operations and has significant importance to an airline's management. It is crucial to know the airline's current schedule status in order to effectively manage the company and to be prepared for abnormal situations. In this study, machine learning techniques were applied to actual schedule data to examine the possibility of whether the airline's fleet state could be artificially learned without prior information. Given that the schedule is in categorical form, One Hot Encoding was applied and t-SNE was used to reduce the dimension of the data and visualize them to gain insights into the airline's overall fleet status. Interesting results were discovered from the experiments where the initial findings are expected to contribute to the fields of airline schedule health monitoring, anomaly detection, and disruption management.
Keywords
Airline Schedule; Airline Fleet; Machine Learning; Data Visualization; Health Monitoring;
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1 Provost, F., and Fawcett, T, "Data science and its relationship to big data and datadriven decision making", Mary Ann Liebert, Inc., 1(1), Feb. 13, 2013.
2 Lufthansa Systems, "Manage Your Airline Operations by Exception", Lufthansa Systems GmbH & Co. KG, 2015, https://www.lhsys tems.com/static/dde9d5c2f582d72ba75c3cf938346263/pb_netline_ops_0.pdf
3 Lu, H., Plataniotis, K. N., and Venetsanopoulos, A. N., "MPCA: Multilinear principal component analysis of tensor objects", IEEE Transactions on Neural Networks, 19(1), 2008.
4 Tenenbaum, J. B., Silva, V. D., and Langford, J. C., "A global geometric framework for nonlinear dimensionality reduction", Science, 290, 2000, pp.2319-2323.   DOI
5 Barratt, S. T., Kochenderfery, M. J., and Boyd, S. P., "Learning probabilistic trajectory models of aircraft in terminal airspace from position data", IEEE Transactions on Intelligent Transportation Systems, 2019, DOI: https://doi.org/10.1109/TITS.2018.2877572   DOI
6 Wattenberg, M., Viegas, F., and Johnson, I., "How to use t -SNE effectively", Distill, 2016, DOI: http://doi.org/10.23915/distill.00002
7 Cohen, J., Cohen, P., West, S. G., and Aiken, L. S., "Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences Third Edition", Lawrence Erlbaum Associates, Inc., Publishers, Mahwah, NJ, USA, 2003, pp.303-320.
8 Cao, Y., and Wang, L., "Automatic selection of t -SNE Perplexity", 2017, arXiv:1708.03229
9 Kang, D. H., "The Strongest Cold Wave in 15 Years, Gimpo, Gimhae Airport's Curfew Suspension, Historical Overnight Operations", Seoul Economy, 2016, URL: https://www.sedaily.com/NewsVIew/1KRCIE4WAQ
10 Platzer, A., "Visualization of SNPs with t - SNE", PLoS ONE 8(2), 2013, e56883, DOI: 10.1371/journal.pone.0056883   DOI
11 Kim, A. M., "Jeju Airport Resumes Operations at 14:48. Evacuation Will Take Three Days", Herald Economy, 2016, URL: http://news. heraldcorp.com/view.php?ud=20160125001029
12 Mitchell, T. M., "Machine Learning", McGrawHill Science, Engineering, Math, New York, NY, USA, 1997, pp.2.
13 Jolliffe, I. T., "Principal Component Analysis, Second Edition", Springer Verlag, New York, NY, 2002, pp.10-28.
14 Sammon Jr, J. W., "A nonlinear mapping for data structure analysis", IEEE Transactions on Computers, C-18(5), 1969.
15 Hinton, G. E., and Roweis, S. T., "Stochastic Neighbor Embedding", Advances in Neural Information Processing Systems, The MIT Press, Vol. 15, Cambridge, MA, USA, 2002, pp.833-840.
16 Maaten, L. V. D., and Hinton, G., "Visualizing data using t -SNE", Journal of Machine Learning Research, 9, 2008, pp.2579-2605.
17 Kobak, D., and Berens, P., "The art of using t -SNE for Single-cell Transcriptomics", Nature Communications 10(5416), 2019, DOI: https: //doi.org/10.1038/s41467-019-13056-x   DOI
18 Hong, S., and Lee, K., "Trajectory prediction for vectored area navigation arrivals", Journal of Aerospace Informations Systems, 12(7), 2015.
19 Cerda, P., and Varoquaux, G., "Encoding High-Cardinality String Categorical Variables", ffhal02171256v1, 2019.
20 Moeyersoms, J., and Martens, D., "Including high-cardinality attributes in predictive models: A case study in churn prediction in the energy sector", Decision Support Systems, 72, 2015, pp.72-81.   DOI
21 Claesen, M., and De Moor, B., "Hyperparameter Search in Machine Learning", 2015, arXiv:1502.02127
22 Maaten, L. V. D., "Barnes-Hut-SNE", 2013, arXiv:1301.3342v2
23 Aggarwal, C. C., Hinneburg, A., and Keim, D. A., "On The Surprising Behavior of Distance Metrics in High Dimensional Space", Van den Bussche J., Vianu V. (Eds.) Database Theory, ICDT 2001, Berlin, Heidelberg, 2001, pp.420-434.
24 Wilson, J. M., "Gantt charts: A centenary appreciation", European Journal of Operational Research, 149, 2003, pp.430-437.   DOI
25 DeGiovanni, J. J., "Seeing the data: United airlines implements new methods of analyzing safety data and improving performance", Flight Safety Foundation, 2017, https://flightsafety.org/asw-article/seeing-the-data
26 Evler, J., Asadi, E., Preis, H., and Fricke, H., "Airline ground operations: Optimal schedule recovery with uncertain arrival times", Journal of Air Transport Management 92, 2021, DOI: 10.1016/j.jairtraman.2021.102021   DOI
27 Gurkan, H., Gurel, S., and Akturk, M. S., "An integrated approach for airline scheduling, aircraft fleeting and routing with cruise speed control", Transportation Research Part C 68, 2016, pp.38-57.   DOI
28 Barnhart, C., and Cohn, A., "Airline schedule planning: Accomplishments and opportunities", Manufacturing & Service Operations Management, 6(1) Winter, 2004, pp.3-22.   DOI
29 Clarke, M. D. D., "Irregular airline operations: A review of the state-of-the-practice in airline operations control centers", Journal of Air Transport Management 4, 1998, pp.67-76.   DOI
30 Mathaisel, D. F. X., "Decision support for airline system operations control and irregular operations", Computers & Operations Research, 23(11), 1996, pp.1083-1098.   DOI
31 Jo, J., Huh, J., Park, J., Kim, B., and Seo, J., "LiveGantt: Interactively visualizing a large manufacturing schedule", IEEE Transactions on Visualization and Computer Graphics, 20(12), 2014.
32 Shihab, S. A. M., Logemann, C., Thomas, D. G., and Wei, P., "Autonomous airline revenue management: A deep reinforcement learning approach to seat inventory control and overbooking", arXiv:1902.06824 [cs.AI], 2009.
33 Davenport, T. H., "At the big data crossroads: Turning towards a smarter travel experience", Amadeus IT Group, 2013, https://amadeus. com/documents/en/blog/pdf/2013/07/amadeus-big-data-report.pdf