Publications

This report describes accomplishments and insights gathererd during the development of decision support tools as part of the Terminal Flight Data Manager (TFDM) program. This work was performed by MIT Lincoln Laboratory and sponsored by the Federal Aviation Administration (FAA). The TFDM program integrated flight data, aircraft surveillance, information on weather and traffic flow constraints, and other data required to optimize airport conguration and arrival/departure management functions. The prototype has been evaluated in both human-in-the-loop simulations, and during operational tests at Dallas/Fort Worth (DFW) International Airport. In parallel, the Laboratory estimated future national operational benefits for TFDM decision support functions, using analysis and performance data gathered from major airports in the US. This analysis indicated that the greatest potential operational benefits would come from decision support tools that facilitate: i) managing runway queues and sequences, ii) tactical management of flight routes and times, impacted by weather and traffic constraints, and iii) managing airport configuration changes. Evaluation of TFDM prototype decision support functions in each of these areas provided valuable insights relative to the maturity of current capabilities and research needed to close performance gaps.

In this paper we seek to characterize traffic in the U.S. air transportation system, and to subsequently develop improved models of traffic demand. We model the air traffic within the U.S. national airspace system as dynamic weighted network. We employ techniques advanced by work in complex networks over the past several years in characterizing the structure and dynamics of the U.S. airport network. We show that the airport network is more dynamic over successive days than has been previously reported. The network has some properties that appear stationary over time, while others exhibit a high degree of variation. We characterize the network and its dynamics using structural measures such as degree distributions and clustering coefficients. We employ spectral analysis to show that dominant eigenvectors of the network are nearly stationary with time. We use this observation to suggest how low dimensional models of traffic demand in the airport network can be fashioned.

The Tower Flight Data Manager (TFDM) will serve as the next generation air traffic control tower automation platform for surface and local airspace operations. TFDM provides three primary enhancements over current systems: consolidation of diverse data and information sources into a single platform; electronic data exchange, including flight data entries, within and outside the tower cab; and a suite of decision support capabilities leveraging TFDM's access to external data sources and systems. This paper describes a TFDM prototype system that includes integrated surveillance, flight data, and decision support display components. Enhancements in airport configuration management, runway assignment, taxi routing, sequencing and scheduling, and departure route assurance are expected to yield significant benefits in delay reduction, fuel savings, additional capacity, improved access, enhanced safety, and reduced environmental impact. Data are provided on system performance and air traffic controller acceptance from simulation studies and a preliminary field demonstration at Dallas / Ft. Worth International Airport.