Prediction of Taxi-in Time and Analysis of Influencing Factors for Arrival Flights at Airport with a Decentralised Terminal Layout

Xiaowei TANG; Mengfan YE; Shengrun ZHANG; Kurt FUELLHART

doi:10.7307/ptt.v36i4.437

Authors

Xiaowei TANG Nanjing University of Aeronautics and Astronautics, College of Civil Aviation
Mengfan YE Nanjing University of Aeronautics and Astronautics, College of Civil Aviation
Shengrun ZHANG Nanjing University of Aeronautics and Astronautics, College of Civil Aviation
Kurt FUELLHART Ghent University, Department of Geography; Unison Consulting, Inc., Laguna Hills

DOI:

https://doi.org/10.7307/ptt.v36i4.437

Keywords:

air transportation, taxi-in time prediction, terminal layout, airport surface movement, lasso, GBRT, airport management

Abstract

Accurately predicting taxi-in times for arrival flights is crucial for efficient ground handling resource allocation, impacting flight departure timeliness. This study investigates terminal layout characteristics, specifically decentralised layouts, to predict and analyse arrival flight taxi-in times. We develop a surface traffic flow calculation method considering arrival and departure flights, eliminating fixed thresholds. We introduce runway-crossing operations for decentralised airports, creating new prediction variables. We consider factors like runway, aircraft type, airline, taxi distance, and time periods. Gradient Boosting Regression Tree predicts taxi-in times, while Lasso analyses factor impact. Our approach yields highly accurate predictions for decentralised airports, with Surface traffic flow and Runway-crossing variables significantly influencing taxi-in times. This research informs airport managers in decentralised layouts, enabling tailored management strategies.

References

Balakrishna P, Ganesan R, Sherry L. Accuracy of reinforcement learning algorithms for predicting aircraft taxiout times: A case-study of Tampa Bay departures. Transportation Research Part C: Emerging Technologies. 2010;18(6):950-962. DOI: 10.1016/j.trc.2010.03.003.

Diana T. Can machines learn how to forecast taxi-out time? A comparison of predictive models applied to the case of Seattle/Tacoma International Airport. Transportation Research Part E: Logistics and Transportation Review. 2018;119:149-164. DOI: 10.1016/j.tre.2018.10.003.

Lian G, et al. RETRACTED: A new dynamic pushback control method for reducing fuel-burn costs: Using predicted taxi-out time. Chinese Journal of Aeronautics. 2019;32(3):660-673. DOI: 10.1016/j.cja.2018.12.013.

Tang X, et al. Taxi-in time prediction of arrival flight. Journal of Beijing University of Aeronautics and Astronautics. 2022. DOI: 10.13700/j.bh.1001-5965.2022.0625 [Accessed 26th Sept. 2023].

Xia Z, Huang L. Prediction of departure flights’ taxi-out time based on intelligent algorithm optimized BP. Mathematical Problems in Engineering. 2022;2022:1-12. DOI: 10.1155/2022/6254251.

Park D, Kim J. Influential factors to aircraft taxi time in airport. Journal of Air Transport Management. 2023;106:102321. DOI: 10.1016/j.jairtraman.2022.102321.

Andersson K, Carr F, Feron E, Hall WD. Analysis and modeling of ground operations at hub airports. 3rd Air Traffic Management Research and Development Seminar, 3-6 Jun. 2000, Napoli, Italy. https://www.atmseminar.org/past-seminars/3rd-seminar/papers/ [Accessed 13th Jan. 2024].

Andersson K. Potential benefits of information sharing during the arrival process at hub airports. Masters thesis. Massachusetts Institute of Technology; 2000.

Jiao Q, Li N. Taxi time prediction by using data driven approach: A new perspective. SSRN Journal. 2022. DOI: 10.2139/ssrn.4084964.

Feng X, Meng J. Flight taxi-out time prediction based on KNN and SVR. Journal of Southwest Jiaotong University. 2017;52(5):1008-1014. DOI: 10.3969/j.issn.0258-2724.2017.05.023.

Herrema F, et al. Taxi-out time prediction model at Charles de Gaulle airport. Journal of Aerospace Information Systems. 2018;15(3):120-130. DOI: 10/gc6dnh.

Jordan R, Ishutkina MA, Reynolds T. AG statistical learning approach to the modeling of aircraft taxi time. 29th Digital Avionics Systems Conference,3-7 Oct. 2010, Salt Lake City, UT, USA. IEEE; 2010. p. B.1-1-1.B.1-10. DOI: 10/fk68gp.

Ravizza S, Atkin JAD, Maathuis MH, Burke EK. A combined statistical approach and ground movement model for improving taxi time estimations at airports. Journal of the Operational Research Society. 2013;64(9):1347-1360. DOI: 10/f467nd.

Yin J, et al. Machine learning techniques for taxi-out time prediction with a macroscopic network topology. 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC), 23-27 Sept. 2018, London, England, UK. 2018. p. 1–8. DOI: 10.1109/DASC.2018.8569664.

Li N, Jiao Q, Zhang L, Fan R. Taxi time prediction of departure aircraft. Journal of Chongqing Jiaotong University (Natural Science). 2021;40(3):1-6.

Wang X, et al. Aircraft taxi time prediction: Feature importance and their implications. Transportation Research Part C: Emerging Technologies. 2021;124:102892. DOI: 10/gq2mpk.

Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association. 2001;96(456):1348-1360. DOI: 10.1198/016214501753382273.

Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction. New York, NY: Springer; 2009.

Wu X, et al. Top 10 algorithms in data mining. Knowl Inf Syst. 2008;14(1):1-37. DOI: 10.1007/s10115-007-0114-2.

Xu N, Sherry L, Laskey KB. Multifactor model for predicting delays at U.S. airports. Transportation Research Record. 2008;2052(1):62-71. DOI: 10.3141/2052-08.

Srivastava A. Improving departure taxi time predictions using ASDE-X surveillance data. 2011 IEEE/AIAA 30th Digital Avionics Systems Conference, 16-20 Oct. 2011, Seattle, WA, USA. IEEE; 2011. p. 2B5-1-2B5-14. DOI: 10.1109/DASC.2011.6095989.

Ravizza S, et al. Aircraft taxi time prediction: Comparisons and insights. Applied Soft Computing. 2014;14:397–406. DOI: 10/gq2mpm.

Lee H, Malik W, Jung YC. Taxi-out time prediction for departures at Charlotte airport using machine learning techniques. 16th AIAA Aviation Technology, Integration, and Operations Conference, 13-17 Jun. 2016, Washington, D.C. 2016. DOI: 10/gq2mph.

Chen Z, Tang X, Lin Y, Ren S. Prediction method and model of aircraft taxi-out time based on decision tree. Journal of Wuhan University of Technology (Transportation Science & Engineering). 2021;45(3):448–453.

Zhao Z, et al. Prediction method of aircraft dynamic taxi time based on XGBoost. Advances in Aeronautical Science and Engineering. 2022;13(1):76–85. DOI: 10.16615/j.cnki.1674-8190.2022.01.08.

Idris H, Clarke JP, Bhuva R, Kang L. Queuing model for taxi-out time estimation. Air Traffic Control Quarterly. 2002;10(1):1–22. DOI: 10/gq2mpn.

Gilbo EP, Center V, Howard KW, Corp A. Collaborative optimization of airport arrival and departure traffic flow management strategies for CDM. 3rd Air Traffic Management Research and Development Seminar, 3-6 Jun. 2000, Napoli, Italy. https://www.atmseminar.org/past-seminars/3rd-seminar/papers/ [Accessed 20th Jan. 2024].

Günther Y, et al. Total airport management (operational concept & logical architecture) version 1.0. 2006.

Yoo HS, et al. Benefit assessment of the integrated demand management concept for multiple New York metroplex airports. AIAA Scitech 2020 Forum, 6-10 Jan. 2000, Orlando, FL, USA. 2020. DOI: 10.2514/6.2020-1400.

Pina P, Pablo JMD. Benefits obtained from the estimation and distribution of realistic taxi times. 6th Air Traffic Management Research and Development Seminar, 27-30 Jun. 2005, Baltimore, MD, USA. https://www.atmseminar.org/past-seminars/6th-seminar/papers/ [Accessed 20th Jan. 2024].

Tang X, Chen Z, Zhang S, Ding Y. Impact of apron spatial configuration on flight departure taxi time at busy airports. Journal of Transportation Systems Engineering and Information Technology. 2022;22(5):309–317. DOI: 10.16097/j.cnki.1009-6744.2022.05.032.

Schreiber-Gregory D, Jackson HM. Regulation Techniques for multicollinearity: lasso, ridge, and elastic nets. Proceedings of the SAS Conference Proceedings: Western Users of SAS Software, 5-7 Sept. 2018, Denver, CO, USA. 2018. p. 8-11.

Iversen GR, Gergen M. Statistics: The conceptual approach. New York, NY: Springer; 1997.

Viering T, Loog M. The shape of learning curves: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2022. http://arxiv.org/abs/2103.10948. [Accessed 26th Sept. 2023].