Scenario Analysis for Probabilistic Airport Departure Capacity

Airport departure capacity is essential for airport operation management, which can be used to identify congestion of airport and adopt optimal control strategy. However, due to the uncertainty of flight off-block time, taxi-out time, and weather condition, embedded in actual airport operation process, there are challenges in estimating airport departure capacity reliably. To address this problem, a novel probabilistic estimation approach for airport departure capacity is proposed. First, we define the airport departure capacity estimation problem as a least squares problem, that minimizes the sum of error squares between the target estimation and the average sequence of departure throughput corresponding to each value of departure demand. Then, the least squares problem is formulated into a Chance-Constrained Optimization Program (C-COP), while a predefined probabilistic guarantee is adopted to ensure the robustness of the estimation. Finally, a scenario-based approach is introduced to solve the C-COP by converting to standard convex optimization problem. Case study, using one year of real-world data set collected from Chengdu Shuangliu International Airport, shows that the proposed method satisfies the requirement of probabilistic guarantee and has smaller estimation error than the existing method.