Trajectory-Based Optimization for Air Traffic Control in the Terminal Maneuvering Area
For air traffic control practitioners, this work provides a practical optimization method that improves arrival sequencing efficiency and reduces fuel burn compared to traditional scheduling models.
The paper presents a trajectory-based optimization framework for arrival sequencing in the terminal maneuvering area that computes implementable speed profiles and path extensions. Monte Carlo experiments show that FOFFS outperforms FEFS in delay and fuel burn, and CPS reduces separation violations with near real-time computation.
We present a trajectory-based optimization framework for arrival sequencing and scheduling in the terminal maneuvering area (TMA). Unlike node-link scheduling models that reduce trajectories to time-delay variables, the proposed method computes implementable per-aircraft speed profiles and path extensions that achieve required landing separation through terminal air traffic control actions. The framework combines an analytic TMA path model, consisting of a tangent leg, a radius-to-fix turn, and a final-approach segment, with a nonlinear program (NLP) that jointly optimizes path stretch and segment speeds under a weighted objective. Three landing-order policies are examined: First-Entry-First-Serve (FEFS), First-on-Final-First-Serve (FOFFS), and FOFFS with Constrained Position Shifting (CPS) up to $k$ positions. CPS is implemented through a two-phase approach coupling mixed-integer linear programming (MILP) with NLP to select an optimized landing order before trajectory optimization. The aircraft population follows a realistic weight-class fleet mix with pair-specific wake-turbulence separation, and each scenario is perturbed by a Gaussian wind sample projected onto each segment to convert commanded airspeeds into ground speeds. An online rolling-horizon formulation commits each aircraft trajectory irrevocably upon entry, enabling real-time decision-making. Monte Carlo experiments on the simplified A80 TMA show that: (i) FOFFS consistently outperforms FEFS in delay, path stretch, and fuel burn by exploiting geometric asymmetries among arrival streams; (ii) CPS further reduces separation violations and path stretch, though with diminishing returns and rapidly increasing solver cost; (iii) fuel estimates from BADA 3 and OpenAP show consistent qualitative trends; and (iv) per-entry optimization completes in near real-time, supporting practical deployment.