A Study of Robustness between Two Strategic 4D Trajectory Plannings

Abstract

Strategic 4D trajectory planning is a promising technology for next-generation air traffic management and systems. Some approaches 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 French airspace 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 using an approximative approach based on simulated annealing. The departure time perturbation was introduced to study the robustness of the two proposed methods. An evaluation of the robustness is performed using Monte Carlo simulation. According to the results, the strategic deconfliction method completely solved all interactions between trajectories, and the strategic decongestion method reduced traffic congestion by 99.94%. Furthermore, a comparative study shows that the method reducing congestion is more robust against the departure time perturbation than the method minimizing interaction between trajectories. These findings encourage appropriate use of the proposed methods in the strategic 4D trajectory planning framework.