An online speed profile generation approach for efficient airport ground movement. (August 2018)

• Record Type: Journal Article
• Authors: Zhang, Tianci; Ding, Meng; Zuo, Hongfu; Chen, Jun; Weiszer, Michal; Qian, Xiaoyan; Burke, Edmund K.
• Is Part Of: Transportation research. Volume 93(2018)
• Page Start: 256
• Page End: 272
• Publication Date: 2018-08
• Subjects: Airport ground movement -- Fuel consumption -- Matheuristics -- Nonlinear optimisation -- Speed profile; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2018.05.030
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Fuel-efficient speed profile is generated considering arrival time interval constraint. Edge-based speed profile with variable control point speed improves the flexibility. Matheuristic based solution approach enables fast response to unprecedented events. Satisfactory performance is observed for varied taxiway layouts and traffic densities. 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.