Latest revision as of 13:41, 16 February 2021

Abstract

This paper proposes a collaborative air traffic flow management (ATFM) framework, in the scope of trajectory based operations, aiming to improve the cost-efficiency for airspace users (AUs) when facing ATFM regulations. The framework consists of four modules. The first one involves the AUs initially scheduling their preferred trajectories for all their flights. Based on this initial demand, the second module (assumed to be on the Network Manager -NM- side) detects time-varying hotspots (i.e. overloaded sectors along the day). In the third module, hotspot information is shared back to the AUs who plan alternative trajectory options to avoid crossing these congested airspace volumes (in the lateral and vertical domain); as well as providing to the NM different pre-tactical delay management preferences (including ground holding, linear holding, air holding and pre-tactical delay recovery); based on their internal cost breakdown structures. Incorporating all these potential combined options, the last module computes the best trajectory selections and the optimal distribution of delay assignments, such that the cost deviation from the initial status (all the user-preferred trajectories) is minimized. This model is formulated as mixed integer linear programming (MILP) and validated by a real-world case study focused on 24 h of traffic over the French airspace. Results using the proposed framework suggest a significant system delay reduction by nearly 97% over the existing method, whilst yielding an average of less than 100 kg extra fuel consumption and 50 Euro extra route charges for the 11% flights diverted to their alternative trajectories. Peer Reviewed

Original document

The different versions of the original document can be found in:

• http://hdl.handle.net/2117/183447 under the license http://creativecommons.org/licenses/by-nc-nd/3.0/es/

• https://api.elsevier.com/content/article/PII:S0191261518310464?httpAccept=text/xml,

https://api.elsevier.com/content/article/PII:S0191261518310464?httpAccept=text/plain,

http://dx.doi.org/10.1016/j.trb.2020.02.012 under the license https://www.elsevier.com/tdm/userlicense/1.0/

• https://doi.org/10.1016/j.trb.2020.02.012,

http://dspace.lib.cranfield.ac.uk/handle/1826/15236 under the license http://creativecommons.org/licenses/by-nc-nd/4.0/

• https://www.sciencedirect.com/science/article/pii/S0191261518310464,

https://upcommons.upc.edu/handle/2117/183447,

https://dspace.lib.cranfield.ac.uk/handle/1826/15236,

http://www.sciencedirect.com/science/article/pii/S0191261518310464,

https://academic.microsoft.com/#/detail/3009467613