IWAC2022 Program & Abstracts
Day 2 (Wednesday, 26 October) 11:15 - 12:45, Hall C

Technical Session 6

Air Traffic Management 1

T6-1-A
Considering TMA holding uncertainty into in-flight trajectory optimisation

Luis Delgado (University of Westminster), David de la Torre Sangrà, Jovana Kuljanin, Xavier Prats (UniversitatPolitècnica de Catalunya)
Crew are aware of the delay they have experienced at departure. However, uncertainties ahead, and in particularholdings at arrival, can have an impact on the performance of their operations. When optimising a trajectory theexpected cost at the arrival gate should be considered and consequently, taking into account potential congestion andextra delay at the arrival airspace is paramount to avoid sub-optimal decisions. This paper presents a framework totactically optimise trajectories considering expected delays at arrival. A flight from Athens (LGAV) to London Heathrow(EGLL) is used for simulate with a systematic range of departing delays and expected holdings.

T6-2-A
A Study of Robustness Between Two Strategic 4D Trajectory Plannings

Paveen Juntama (Ecole Nationale d'Aviation Civile), Sameer Alam (Air Traffic Management ResearchInstitute), Daniel Delahaye (Ecole Nationale d'Aviation Civile)
Strategic 4D trajectory planning is a promising technology for next-generation air traffic management and systems. Someapproaches attempt to satisfy the capacity constraint to reduce traffic congestion, while others aim to reduce potential
conflicts between trajectories. This paper investigates two approaches to organizing the real traffic in the Frenchairspace at the strategic level. The first approach minimizes interaction between trajectories, while the second reduces
traffic congestion so that the controller maintains the traffic without much effort. The associated optimization problems
are formulated and resolved by an approximative approach based on simulated annealing. The departure time
perturbation was introduced to study the robustness of the two proposed methods. The evaluation of the robustness
is performed by Monte Carlo simulation. According to the results, the strategic deconfliction method completelysolved all interactions between trajectories, and the strategic decongestion method reduced traffic congestion by99.94%. Furthermore, the comparative study shows that the method reducing congestion is more robust against thedeparture time perturbation than the method minimizing interaction between trajectories. These findings encourage theappropriate use of proposed methods in the strategic 4D trajectory planning framework.

T6-3-A
Slot allocation in a multi-airport system under flying time uncertainty

Chang Liu, Yanjun Wang, Shenzhi Wu (Nanjing University of Aeronautics and Astronautics), Daniel
Delahaye (Ecole Nationale de l'Aviation Civile)
Slot allocation in a single airport aims to maximize the utilization of airport declared capacity, while slot allocation
in a multi-airport system (MAS) has to take airspace capacity into account. Because the limited capacity of certain
departure/arrival fixes in the terminal airspace can cause unnecessary flight delays. The uncertainty of flying time
between airport and congested fixes makes it even more complicated for slot alloca-tion in a MAS. Traffic flow may beover capacity when the flying times of flights change. In this paper, we propose a mixed integer-programming model
for slot allocation in a MAS. The objective of the model is to minimize the total displacements of flights in the MAS
while considering all the capacity constraints as well as the uncertainty of flying time. The constraints at departure/arrival fixes are transformed into chance constraints, and Lyapunov theorem is applied for the transformation. To test
the proposed model, a case study of schedule optimization in the MAS of Guangdong-Hong Kong-Macao Greater Bay ispresented. Specifically, the impact of the uncertainty of flying time from five airports to airspace fix YIN is investigated.
Results show that the total displacements increased if the uncertainty of flying time was considered. The optimizedschedule, however, is more robust which can satisfy capacity constraints in various scenarios.

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