Nested Parallel Integrated Arrival Solution: Compatible Arrival Sequencing and Flight Trajectory Solution

Abstract

This thesis introduces the Nested-Parallel Integrated Arrival Solution (NPIAS), a new approach for the simultaneous derivation of compatible arrival sequencing and trajectory solutions within an enhanced computational framework. Designed to improve arrival management in congested terminal maneuvering areas, NPIAS addresses air traffic management (ATM)-induced inefficiencies, including separation deviation, back propagation effect, and the inaccurate use of traffic flow guidance strategies, as identified in the arrival efficiency analysis. To address the deteriorating TMA arrival performance and limitations in existing trajectory generation methods, this thesis employs trajectory grafting as a core trajectory generation tool within the NPIAS framework. Trajectory grafting leverages historical trajectory data to generate high-fidelity, feature-embedded trajectories that inherently satisfy safety and operational constraints, thereby reducing the reliance on explicit constraint modeling. This heuristic approach is less sensitive to problem complexity and achieves a reduction in average arrival transit time by 3.05% and separation deviation by 90.8% in traffic simulations. Building on an enhanced trajectory grafting method, including reactive grafting, refined solution sets, and ETA-relaxation, NPIAS delivers improved computational efficiency and adaptively increases re-sequencing flexibility. NPIAS integrates trajectory grafting with trajectory-driven arrival sequencing to address persistent performance challenges in ATM, including arrival delays, underutilized arrival capacity, and elevated controller workload. The nested-parallel computational architecture accelerates solution derivation, enabling reliable and agile arrival sequencing and trajectory generation under dynamic conditions. In extensive traffic simulations, based on historical arrival data of Hong Kong International Airport, NPIAS achieves an average reduction of 24.8% in arrival transit time and a 56.6% decrease in computational time compared to sequential approaches. This framework advances ATM automation by providing a scalable, high-fidelity, and computationally efficient platform for integrated arrival management, with potential applications in high-density, complex airspace environments.

Date of Award	2025
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Stephane REDONNET (Supervisor) & Rhea Patricia LIEM (Supervisor)