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Crossref起降场与空中交通论文索引·· 2026-05-19

Vertiport终端调度与吞吐量分析:多方向地表操作

Vertiport terminal scheduling and throughput analysis for multiple surface directions

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该研究提出了针对多方向地表操作的垂直港口调度优化方法,通过MILP模型减少延误并提升吞吐量,对eVTOL行业中的基础设施规划和运营效率具有参考价值。研究结果表明,优化调度可显著提升垂直港口的运营能力,为未来城市空中交通系统的建设提供理论支持。

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Abstract

Vertical take-off and landing (VTOL) vehicles are gaining traction in both the delivery drone market and passenger transportation, driving the development of urban air mobility (UAM) systems. UAM seeks to alleviate road congestion in dense urban areas by leveraging urban airspace. To handle UAM traffic, vertiport terminals (vertiminals) play a critical role in supporting VTOL vehicle operations such as take-offs, landings, taxiing, passenger boarding, refuelling or charging and maintenance. Efficient scheduling algorithms are essential to manage these operations and optimise vertiminal throughput while ensuring safety protocols. Unlike fixed-wing aircraft, which rely on runways for take-off and climbing in fixed directions, VTOL vehicles can utilise multiple surface directions for climbing and approach. This flexibility necessitates specialised scheduling methods. We propose a mixed integer linear programme (MILP) formulation to holistically optimise vertiminal operations, including taxiing, climbing (or approach) using multiple directions and turnaround at gates. The proposed MILP reduces delays by up to 50%. Additionally, we derive equations to compute upper bounds of the throughput capacity of vertiminals, considering its core elements: the touchdown and lift-off (TLOF) pad system, taxiway system and gate system. Our results demonstrate that the MILP achieves throughput levels consistent with the theoretical maximum derived from these equations. As a case study, we applied our throughput analysis on a vertiminal topology in the literature and used our MILP to find the optimal configuration. This dual approach, MILP and throughput analysis, allows for comprehensive capacity analysis without requiring simulations while enabling efficient scheduling through the MILP formulation.

References

Cohen, A.P., Shaheen, S.A. and Farrar, E.M. Urban air mobility: history, ecosystem, market potential, and challenges, IEEE Trans. Intell. Transp. Syst., 2021, 22, (9), pp 6074–6087.CrossRefGoogle Scholar

NASA. UAM vision concept of operations (conops) UAM maturity level (UML) 4.Google Scholar

Aweiss, A.S., Owens, B.D., Rios, J., Homola, J.R. and Mohlenbrink, C.P. Unmanned aircraft systems (UAS) traffic management (UTM) national campaign ii, 2018 AIAA Information Systems-AIAA Infotech@ Aerospace, AIAA, 2018, p 1727.CrossRefGoogle Scholar

McKinsey. Future air mobility: major developments in 2022 and significant milestones ahead, 2023.Google Scholar

Everett, H. Italy’s first vertiport deployed at Fiumicino Airport, 2022.Google Scholar

FutureFlight. Volocopter concludes European urban air mobility airspace integration flight trials, 2022.Google Scholar

FutureFlight. Advanced air mobility flight test site to open at German airport, 2023.Google Scholar

Rimjha, M. and Trani, A. Urban air mobility: Factors affecting vertiport capacity, 2021 Integrated Communications Navigation and Surveillance Conference (ICNS), 2021, pp 1–14.CrossRefGoogle Scholar

EASA. Prototype Technical Specifications for the Design of VFR Vertiports for Operation with Manned VTOL-Capable Aircraft Certified in the Enhanced Category.Google Scholar

Watkins, L., Sarfaraz, N., Zanlongo, S., Silbermann, J., Young, T. and Sleight, R. An investigative study into an autonomous UAS traffic management system for congested airspace safety, 2021 IEEE International Conference on Communications Workshops (ICC Workshops), 2021, pp 1–6.CrossRefGoogle Scholar

Mueller, E.R., Kopardekar, H.P. and Goodrich, H.K. Enabling airspace integration for high-density on-demand mobility operations, 17th AIAA Aviation Technology, Integration, and Operations Conference, 2017, p 3086.CrossRefGoogle Scholar

Jiang, Y., Xu, X., Zhang, H. and Luo, Y. Taxiing route scheduling between taxiway and runway in hub airport, Math. Probl. Eng., 2015, 2015, p 925139.Google Scholar

Lee, H. and Balakrishnan, H. A comparison of two optimization approaches for airport taxiway and runway scheduling, 2012 IEEE/AIAA 31st Digital Avionics Systems Conference (DASC). IEEE, 2012, pp 4E1–1.CrossRefGoogle Scholar

Deng, W., Xu, J., Zhao, H. and Song, Y. A novel gate resource allocation method using improved PSO-based QEA, IEEE Transactions on Intelligent Transportation Systems, 2020.Google Scholar

Vázquez, H. et al. Vertiport sizing and layout planning through integer programming in the context of urban air mobility, 2021.Google Scholar

Zelinski, S. Operational analysis of vertiport surface topology, 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC). IEEE, 2020, pp 1–10.CrossRefGoogle Scholar

Shao, Q., Shao, M. and Lu, Y. Terminal area control rules and evtol adaptive scheduling model for multi-vertiport system in urban air mobility, Transp. Res. Part C Emerg. Technol., 2021, 132, p 103385.CrossRefGoogle Scholar

Song, K. and Yeo, H. Development of optimal scheduling strategy and approach control model of multicopter VTOL aircraft for urban air mobility (UAM) operation, Transp. Res. Part C Emerg. Technol., 2021, 128, p 103181.CrossRefGoogle Scholar

Furini, F., Kidd, M.P., Persiani, C.A. and Toth, P. Improved rolling horizon approaches to the aircraft sequencing problem, J. Sched., 2015, 18, pp 435–447.CrossRefGoogle Scholar

Saxena, R.R., Joshi, T., Yashashav, K.D., Prabhakar, V.T. and Kuri, J. Integrated taxiing and tlof pad scheduling using different surface directions with fairness analysis, 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2023, pp 1747–1752.CrossRefGoogle Scholar

Ahn, B. and Hwang, H. Design criteria and accommodating capacity analysis of vertiports for urban air mobility and its application at Gimpo airport in Korea, Appl. Sci., 2022, 12, (12), p 6077.CrossRefGoogle Scholar

Gotteland, J.-B. and Durand, N. Genetic algorithms applied to airport ground traffic optimization, The 2003 Congress on Evolutionary Computation, 2003. CEC’03., volume 1. IEEE, 2003, pp 544–551.CrossRefGoogle Scholar

Liu, C. and Guo, K. Airport taxi scheduling optimization based on genetic algorithm, 2010 International Conference on Computational Intelligence and Security. IEEE, 2010, pp 205–208.CrossRefGoogle Scholar

Clare, G. and Richards, G.A. Optimization of taxiway routing and runway scheduling, IEEE Trans. Intell. Transp. Syst., 2011, 12, (4), pp 1000–1013.CrossRefGoogle Scholar

Simaiakis, I. Analysis, modelling and control of the airport departure process. PhD thesis, Massachusetts Institute of Technology, 2013.Google Scholar

Cheng, P., Zou, X. and Liu, W. Airport surface trajectory optimization considering runway exit selection, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC). IEEE, 2014, pp 2656–2662.CrossRefGoogle Scholar

Herrema, F., Curran, R., Hartjes, S., Ellejmi, M., Bancroft, S. and Schultz, M. A machine learning model to predict runway exit at vienna airport, Transp. Res. Part E Logist. Transp. Rev., 2019, 131, pp 329–342.CrossRefGoogle Scholar

Morgan, C.C., Ellejmi, M., Herrema, F. and Curran, R. Validation of the runway utilisation concept. 9th SESAR Innovation Days, 2019.Google Scholar

Behrends, A.J. and Usher, M.J. Aircraft gate assignment: using a deterministic approach for integrating freight movement and aircraft taxiing, Comput. Ind. Eng., 2016, 102, pp 44–57.CrossRefGoogle Scholar

Deng, W., Sun, M., Zhao, H., Li, B. and Wang, C. Study on an airport gate assignment method based on improved aco algorithm. Kybernetes, 2017, 47, (1), pp 20–43.CrossRefGoogle Scholar

Glomb, L., Liers, F. and Rösel, F. A novel decomposition approach for holistic airline optimization, 2022.Google Scholar

Bosson, C., Xue, M. and Zelinski, S. Optimizing integrated arrival, departure and surface operations under uncertainty, ATM Seminar, number ARC-E-DAA-TN20175, 2015.Google Scholar

Weiszer, M., Chen, J. and Locatelli, G. An integrated optimisation approach to airport ground operations to foster sustainability in the aviation sector, Appl. Energy, 2015, 157, pp 567–582.CrossRefGoogle Scholar

Kleinbekman, I.C., Mitici, M.A. and Wei, P. evtol arrival sequencing and scheduling for on-demand urban air mobility, 2018 IEEE/AIAA 37th Digital Avionics Systems Conference (DASC). IEEE, 2018, pp 1–7.CrossRefGoogle Scholar

Vascik, P.D. and Hansman, J.R. Development of vertiport capacity envelopes and analysis of their sensitivity to topological and operational factors, AIAA Scitech 2019 Forum, 2019, p 0526.CrossRefGoogle Scholar

Preis, L. and Hornung, M. A vertiport design heuristic to ensure efficient ground operations for urban air mobility. Appl. Sci., 2022, 12, (14), p 7260.CrossRefGoogle Scholar

Bertsimas, D. and Stock Patterson, S. The traffic flow management rerouting problem in air traffic control: a dynamic network flow approach, Transp. Sci., 2000, 34, (3), pp 239–255.CrossRefGoogle Scholar

Jacob Tsao, H.-S., Wei, W., Pratama, A. and Tsao, J.R. Integrated taxiing and take-off scheduling for optimization of airport surface operations, Proc. 2nd Annual Conference of Indian Subcontinent Decision Science Institute (ISDSI 2009), 2009, pp 3–5.Google Scholar

Saxena, R.R., Pal, J., Iyengar, S., Chhaglani, B., Ghosh, A., Padmanabhan, V.N. and Venkata, P.T. Holistic energy awareness and robustness for intelligent drones, ACM Trans. Sen. Netw., 2024. Just Accepted.CrossRefGoogle Scholar

De Neufville, R. Airport systems planning, design, and management, Air Transport Management. Routledge, 2020, pp 79–96.CrossRefGoogle Scholar

Leighton, T. and Rao, S. Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms, J. ACM, 1999, 46, (6), pp 787–832.CrossRefGoogle Scholar

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