An online speed profile generation approach for efficient airport ground movement

Tianci Zhang, Meng Ding, Hongfu Zuo, Jun Chen, Michal Weiszer, Xiaoyan Qian and Edmund K. Burke 2018. An online speed profile generation approach for efficient airport ground movement. Transportation Research Part C: Emerging Technologies. 93, pp. 256-272. https://doi.org/10.1016/j.trc.2018.05.030

TitleAn online speed profile generation approach for efficient airport ground movement
TypeJournal article
AuthorsTianci Zhang, Meng Ding, Hongfu Zuo, Jun Chen, Michal Weiszer, Xiaoyan Qian and Edmund K. Burke
Abstract

The precise guidance and control of taxiing aircraft based on four-dimensional trajectories (4DTs) has been recognised as a promising means to ensure safe and efficient airport ground movement in the context of ever growing air traffic demand. In this paper, a systematic approach for online speed profile generation is proposed. The aim is to generate fuel-efficient speed profiles respecting the timing constraints imposed by routing and scheduling, which ensures conflict-free movement of aircraft in the planning stage. The problem is first formulated as a nonlinear optimisation model, which uses a more flexible edge-based speed profile definition. A decomposed solution approach (following the framework of matheuristic) is then proposed to generate feasible speed profiles in real time. The decomposed solution approach reduces the nonlinear optimisation model into three tractable constituent problems. The control point arrival time allocation problem is solved using linear programming. The control point speed allocation problem is solved using particle swarm optimisation. And the complete speed profile between control points is determined using enumeration. Finally, improved speed profiles are generated through further optimisation upon the feasible speed profiles. The effectiveness and advantages of the proposed approach are validated using datasets of real-world airports.

JournalTransportation Research Part C: Emerging Technologies
Journal citation93, pp. 256-272
ISSN0968-090X
1879-2359
Year2018
PublisherElsevier
Digital Object Identifier (DOI)https://doi.org/10.1016/j.trc.2018.05.030
Web address (URL)https://doi.org/10.1016/j.trc.2018.05.030
Publication dates
PublishedAug 2018

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