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Study on electric towing tractor quantity based on cellular automata

Published online by Cambridge University Press:  31 October 2024

Y. Liu Open the ORCID record for Y. Liu [Opens in a new window] ,
X. Cheng ,
D. Tang ,
J. Zhang Open the ORCID record for J. Zhang [Opens in a new window]  and
X. Luo Open the ORCID record for X. Luo [Opens in a new window]
Y. Liu*
Affiliation:
Department of Airport, School of Traffic Science and Engineering, Civil Aviation University of China, Tianjin, People’s Republic of China
X. Cheng
Affiliation:
Department of Airport, School of Traffic Science and Engineering, Civil Aviation University of China, Tianjin, People’s Republic of China
D. Tang
Affiliation:
Department of Airport, School of Traffic Science and Engineering, Civil Aviation University of China, Tianjin, People’s Republic of China
J. Zhang
Affiliation:
Department of Airport, School of Traffic Science and Engineering, Civil Aviation University of China, Tianjin, People’s Republic of China
X. Luo
Affiliation:
Department of Airport, School of Traffic Science and Engineering, Civil Aviation University of China, Tianjin, People’s Republic of China
*
Corresponding author: Y. Liu; Email: yx-liu@cauc.edu.cn
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Abstract

Aircraft ground taxiing contributes significantly to carbon emissions and engine wear. The electric towing tractor (ETT) addresses these issues by towing the aircraft to the runway end, thereby minimising ground taxiing. As the complexity of ETT towing operations increases, both the towing distance and time increase significantly, and the original method for estimating the number of ETTs is no longer applicable. Due to the substantial acquisition cost of ETT and the need to reduce waste while ensuring operational efficiency, this paper introduces for the first time an ETT quantity estimation model that combines simulation and vehicle scheduling models. The simulation model simulates the impact of ETT on apron operations, taxiing on taxiways and takeoffs and landings on runways. Key timing points for ETT usage by each aircraft are identified through simulation, forming the basis for determining the minimum number of vehicles required for airport operations using a hard-time window vehicle scheduling model. To ensure the validity of the model, simulation model verification is conducted. Furthermore, the study explores the influence of vehicle speed and airport scale on the required number of ETTs. The results demonstrate the effective representation of real-airport operations by the simulation model. ETT speed, airport runway and taxiway configurations, takeoff and landing frequencies and imbalances during peak periods all impact the required quantity of ETTs. A comprehensive approach considering these factors is necessary to determine the optimal number of ETTs.

Keywords

airport surface operationcellular automatonelectric towing tractorstime window model

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 128 , Issue 1330 , December 2024 , pp. 2980 - 2994
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

IATA. Resolution On The Implementation Of The Aviation “CNG2020” Strategy, 2013. Retrieved from https://www.iata.org/policy/Documents/agm-resolution-on-climate-change.pdf Google Scholar
ICAO. Consolidated Statement of Continuing Icao Policies and Practices Related to Environmental Protection – CLIMATE CHANGE, 2010. Retrieved from http://www.icao.int/ Google Scholar
Kesgin, U. Aircraft emissions at Turkish airports. 2012, Cambridge University Press. Energy, 2006, 31, (2–3), pp 372–384. https://doi.org/10.1016/j.energy.2005.01.012 CrossRefGoogle Scholar
Khammash, L., Mantecchini, L. and Reis, V. Micro-simulation of airport taxiing procedures to improve operation sustainability: Application of semi-robotic towing tractor, 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), IEEE, 2017, Naples, Italy, pp 616–621. https://doi.org/10.1109/MTITS.2017.8005587 CrossRefGoogle Scholar
Salihu, A.L., Lloyd, S.M. and Akgunduz, A. Electrification of airport taxiway operations: A simulation framework for analyzing congestion and cost, Transp. Res. Part D Transp. Environ., 2021, 97,p 102962. https://doi.org/10.1016/j.trd.2021.102962 CrossRefGoogle Scholar
Dantzig, G.B. and Ramser, J.H. The truck dispatching problem, Manag. Sci., 1959, 6, (1), pp 80–91. https://doi.org/10.1287/mnsc.6.1.80 CrossRefGoogle Scholar
Belhaiza, S. A game theoretic approach for the real-life multiple-criterion vehicle routing problem with multiple time windows, IEEE Systems Journal, 2018, 12, (2), pp 1251–1262. https://doi.org/10.1109/JSYST.2016.2601058 CrossRefGoogle Scholar
Errico, F., Desaulniers, G., Gendreau, M., Rei, W. and Rousseau, L.-M. The vehicle routing problem with hard time windows and stochastic service times, EURO J. Transp. Logistics, 2018, 7, (3), pp 223–251. https://doi.org/10.1007/s13676-016-0101-4 CrossRefGoogle Scholar
Manisri, T., Mungwattana, A. and Janssens, G.K. Minimax optimisation approach for the robust vehicle routing problem with time windows and uncertain travel times, Int. J. Logistics Syst. Manag., 2011, 10, (4), p 461. https://doi.org/10.1504/IJLSM.2011.043105 CrossRefGoogle Scholar
Gutierrez, A., Dieulle, L., Labadie, N. and Velasco, N. A multi-population algorithm to solve the VRP with stochastic service and travel times, Comput. Ind. Eng., 2018, 125, pp 144–156. https://doi.org/10.1016/j.cie.2018.07.042 CrossRefGoogle Scholar
Pérez-Rodrguez, R. and Hernández-Aguirre, A. A hybrid estimation of distribution algorithm for the vehicle routing problem with time windows, Comput. Ind. Eng., 2019, 130, pp 75–96. https://doi.org/10.1016/j.cie.2019.02.017 Google Scholar
Zhao, P., Han, X. and Wan, D. Evaluation of the airport ferry vehicle scheduling based on network maximum flow model, Omega, 2021, 99, p 102178. https://doi.org/10.1016/j.omega.2019.102178 CrossRefGoogle Scholar
Mori, R. Aircraft ground-taxiing model for congested airport using cellular automata, IEEE Trans. Intell. Transp. Syst., 2013, 14, (1), pp 180–188. https://doi.org/10.1109/TITS.2012.2208188 CrossRefGoogle Scholar
Yang, L., Yin, S., Han, K., Haddad, J. and Hu, M. Fundamental diagrams of airport surface traffic: Models and applications. Transp. Res. Part B Methodol., 2017, 106, pp 29–51. https://doi.org/10.1016/j.trb.2017.10.015 CrossRefGoogle Scholar
Mazur, F. and Schreckenberg, M. Simulation and optimization of ground traffic on airports using cellular automata, Collective Dyn., 2018, 3, A14. https://doi.org/10.17815/CD.2018.14 CrossRefGoogle Scholar
Yin, S., Han, K., Ochieng, W.Y. and Sanchez, D.R. Joint apron-runway assignment for airport surface operations, Transp. Res. Part B Methodol., 2022, 156, pp 76–100. https://doi.org/10.1016/j.trb.2021.12.011 CrossRefGoogle Scholar
Okuniek, N. and Beckmann, D. Towards higher level of A-SMGCS: Handshake of electric taxi and trajectory-based taxi operations, 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC), IEEE, 2017, St. Petersburg, FL, pp 1–10. https://doi.org/10.1109/DASC.2017.8102047 Google Scholar
Kawagoe, Y., Chino, R., Tsuzuki, S., Itoh, E. and Okabe, T. Analyzing stochastic features in airport surface traffic flow using cellular automaton: Tokyo international airport, IEEE Access, 2022, 10, pp 95344–95355. https://doi.org/10.1109/ACCESS.2022.3204819 CrossRefGoogle Scholar
Li, N. and Wang, L. The research on airport ground capacity evaluation based on 3-dimensional airport TWR simulation system, 2009 International Conference on Measuring Technology and Mechatronics Automation, IEEE, 2009, Zhangjiajie, Hunan, China, pp 412–415. https://doi.org/10.1109/ICMTMA.2009.177 CrossRefGoogle Scholar
Du, J.Y., Brunner, J.O. and Kolisch, R. Obtaining the optimal fleet mix: a case study about towing tractors at airports, Omega, 2016, 64, pp 102–114. https://doi.org/10.1016/j.omega.2015.11.005 CrossRefGoogle Scholar
Board, T.R. National academies of sciences, engineering, and medicine, in Use of Towbarless Tractors at Airports–Best Practices, The National Academies Press, 2012, Washington, DC.https://doi.org/10.17226/14649 Google Scholar
IAI. IAI’s TaxiBot in Final Stages of Certification for the Airbus 320. IAI, 2016. Retrieved from IAI website: https://www.iai.co.il/iai-taxibot-final-stages-certification-airbus-320 Google Scholar
Vaishnav, P. Costs and benefits of reducing fuel burn and emissions from taxiing aircraft, Transp. Res. Record, 2014, 2400, pp 65–77. https://doi.org/10.3141/2400-08 CrossRefGoogle Scholar
Khadilkar, H. and Balakrishnan, H. Estimation of aircraft taxi fuel burn using flight data recorder archives, Transp. Res. Part D Transp. Environ., 2012, 17, (7), pp 532–537. https://doi.org/10.1016/j.trd.2012.06.005 CrossRefGoogle Scholar
The ICAO. Introduction to the ICAO Engine Emissions Databank, 2024. Retrieved from Postfach 10 12 53 5053, Cologne, Germany; or visiting address: Konrad-Adenauer-Ufer 3 5068 Cologne, Germany.Google Scholar