The objective of the research is to develop tools for comprehensive design and optimization of air traffic flow management capabilities at multiple spatial and temporal resolutions: a national airspace-wide scale and one-day time horizon (strategic time-frame); and at a regional scale (of one or a few Centers) and a two-hour time horizon (tactical time-frame). The approach is to develop a suite of tools for designing complex multi-scale dynamical networks, and in turn to use these tools to comprehensively address the strategic-to-tactical traffic flow management problem.
The two directions in tool development include 1) the meshed modeling/design of flow- and queueing-networks under network topology variation for cyber- and physical- resource allocation, and 2) large-scale network simulation and numerical analysis. This research will yield aggregate modeling, management design, and validation tools for multi-scale dynamical infrastructure networks, and comprehensive solutions for national-wide strategic-to-tactical traffic flow management using these tools.
The broader impact of the research lies in the significant improvement in cost and equity that may be achieved by the National Airspace System customers, and in the introduction of systematic tools for infrastructure-network design that will have impact not only in transportation but in fields such as electric power network control and health-infrastructure design. The development of an Infrastructure Network Ideas Cluster will enhance inter-disciplinary collaboration on the project topics and discussion of their potential societal impact. Activities of the cluster include cross-university undergraduate research training, seminars on technological and societal-impact aspects of the project, and new course development.
Managing the United States' air traffic system is becoming increasingly complicated, as traffic densities increase, non-traditional vehicles like drones enter the airspace, and security concerns become more diverse. At the same time, the pervasive integration of cyber- (communication, control, computing) technologies can permit revolutionary advances in management. The aim of this project was to understand how these new cyber- technologies could be harnessed to better model traffic flow under weather uncertainty in the United States National Airspace System, and in turn to aid human managers in strategizing flow contingencies in a 2-24 hour time horizon. The translation of these wide area strategies to detailed operational solutions for traffic-control facilities was also studied. The research project has led to concrete software prototypes for the air transportation industry. Specifically, the weather-impact forecasting techniques developed in the project, which give futures or scenarios of weather impact on airspace and airport capacities, are being prototyped in air traffic control facilities and are tentatively slated for deployment in an upcoming Federal Aviation Adminstration work package. Alos, the queueing-network model for predicting and managing traffic flows under weather uncertainty has been implemented by industrial collaborators as part of the Flow Contingency Management tool, and also is being prototyped in traffic-control facilities. The software tools contribute to the effort to build a Next-Generation Air Traffic Management in which cyber- tools comprehensively assist human decision makers in traffic management as well as control. These research efforts are leading to a more flexible, resilient, and efficient air transportation system. The project also has contributed to the core science of networks, by providing practical tools for uncertainty evaluation and management in large-scale networks. These tools include methods for evaluating uncertainties via smart simulation, as well as myriad techniques for designing and evaluating sparse control capabilities for networks. Applications of these methods in airspace-system operations have been demonstrated. Toward achieving impact in academic scholarship and education, the described technical outcomes have been published in conference proceedings and journal papers, described to academic and industrial partners in talks (including at the Cyber-Physical-Systems PI meeting), and integrated into graduate courses at Washington State University. The project has contributed to the training of four graduate students and two undergraduate students.
