Data-driven modeling of air traffic flows for advanced Air Traffic Management

Abstract

The Air Traffic Management (ATM) system enables air transportation by ensuring a safe and orderly air traffic flow. As the air transport demand has grown, ATM has become increasingly challenging, resulting in high levels of congestion, flight delays and environmental impacts. To sustain the industry growth foreseen and enable more efficient air travel, it is important to develop mechanisms for better understanding and predicting the air traffic flow behavior and performance in order to assist human decision-makers to deliver improved airspace design and traffic management solutions. This thesis presents a data-driven approach to modeling air traffic flows and analyzes its contribution to supporting system level ATM decision-making. A data analytics framework is proposed for high-fidelity characterization of air traffic flows from large-scale flight tracking data. The framework incorporates a multi-layer clustering analysis to extract spatiotemporal patterns in aircraft movement towards the identification of trajectory patterns and traffic flow patterns. The outcomes and potential impacts of this framework are demonstrated with a detailed characterization of terminal area traffic flows in three representative multi-airport (metroplex) systems of the global air transportation system: New York, Hong Kong and Sao Paulo. As a descriptive tool for systematic analysis of the flow behavior, the framework allows for cross-metroplex comparisons of terminal airspace design, utilization and traffic performance. Novel quantitative metrics are created to summarize metroplex efficiency, capacity and predictability. The results reveal several structural, operational and performance differences between the metroplexes analyzed and highlight varied action areas to improve air traffic operations at these systems. Finally, the knowledge derived from flight trajectory data analytics is leveraged to develop predictive and prescriptive models for metroplex configuration and capacity planning decision support. Supervised learning methods are used to create prediction models capable of translating weather forecasts into probabilistic forecasts of the metroplex traffic flow structure and airport capacity for strategic time horizons. To process these capacity forecasts and assist the design of traffic flow management strategies, a new optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal Airport Acceptance Rates in Ground Delay Programs, an overall delay reduction of up to 9.7% is achieved.