Optimizing Large-Scale Demand and Capacity Balancing in Air Traffic Flow Management Using Deep Neural Networks

Abstract

Over the past forty years, air traffic flow management (ATFM) has garnered significant attention since the initial approach was introduced to address single-airport ground delay issues. Traditional methods for solving both single- and multi-airport ground delay problems primarily rely on operations research techniques and are typically formulated as mixed-integer problems (MIPs), with solvers employed to approximate optimal solutions. Despite their effectiveness in smaller-scale problems, these approaches struggle with the complexity and scalability required for large-scale, multi-sector ATFM, leading to suboptimal performance in real-time scenarios. To overcome these limitations, we propose a novel neural network-based demand and capacity balancing (NN-DCB) method that leverages neural branching and neural diving to efficiently solve the ATFM problem. Using data from 15,927 flight trajectories across 287 airspace sectors on a typical day in February 2024, our method re-allocates trajectory entry and exit times in each sector. The results demonstrate that large-scale ATFM problems can be solved within 15 min, offering a significant performance improvement over the state-of-the-art methods. This study confirms that neural network-based approaches are more effective for large-scale ATFM problem-solving.