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Airport surface traffic management decision support - perspectives based on tower flight data manager prototype

May 20, 2013
  |
Project Report
Author:
Vineet Mehta
…
Published in:
MIT Lincoln Laboratory Report ATC-398
Topic:
decision support
R&D area:
R&D group:

Summary

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.
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Summary

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...

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Airport surface traffic management decision support - perspectives based on tower flight data manager prototype

Tower Flight Data Manager benefits assessment: initial investment decision interim report

November 19, 2012
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Project Report
Author:
Thomas G. Reynolds
…
Published in:
MIT Lincoln Laboratory Report ATC-394
Topic:
air traffic control
R&D area:

Summary

This document provides an overview of MIT Lincoln Laboratory's activities in support of the interim stage of the Initial Investment Decision benefits assessment for the Tower Flight Data Manager. It outlines the rationale for the focus areas, and the background, methodology, and scope in the focus areas of departure metering, sequence optimization, airport configuration optimization, and safety assessment. Estimates of the potential benefits enabled by TFDM deployment are presented for each of these areas for a subset of airports and conditions considered within the scope of the analyses. These benefits are monetized where possible. Recommendations for follow-on work, for example, to support future benefits assessment efforts for TFDM, are also discussed.
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Summary

This document provides an overview of MIT Lincoln Laboratory's activities in support of the interim stage of the Initial Investment Decision benefits assessment for the Tower Flight Data Manager. It outlines the rationale for the focus areas, and the background, methodology, and scope in the focus areas of departure metering...

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Tower Flight Data Manager benefits assessment: initial investment decision interim report

Dallas/Fort Worth field demonstration #2 (DFW-2) final report for Tower Flight Data Manager (TFDM)

May 16, 2012
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Project Report
Author:
Hayley J. Reynolds
…
Published in:
MIT Lincoln Laboratory Report ATC-393
Topic:
air traffic control
R&D area:

Summary

The Tower Flight Data Manager (TFDM) is the next generation air traffic control tower (ATCT) information system that integrates surveillance, flight data, and other sources, which enables advanced decision support tools (DSTs) to improve departure and arrival efficiency and reduce fuel burn at the airport. TFDM was exercised as a prototype installed at the Dallas / Fort Worth International Airport (DFW) during a two-week demonstration in the spring of 2011 termed DFW-2. MIT Lincoln Laboratory conducted this demonstration for the FAA in coordination with DFW air traffic control (ATC) and the DFW airport authority. The objective of this TFDM field demonstration was to validate the operational suitability and refine production system requirements of the Tower Information Display System (TIDS) surface surveillance display and Flight Data Manager (FDM) electronic flight data display and to evaluate the first iteration of the Supervisor Display and DSTs. These objectives were met during the two-week field demonstration. Results indicated that the TIDS and FDM exhibited capabilities considered operationally suitable for the tower as an advisory system and as a primary means for control given surface surveillance that is approved for operational use. Human factors data indicated that TIDS and FDM could be beneficial. The prototype Supervisor Display and DSTs met a majority of the technical performance criteria, but fewer than half of the human factors success criteria were met. As this was the first iteration of the Supervisor Display and DST capabilities, subsequent prototype iterations to achieve the target concept of operations, functionality and information presentation with accompanying field demonstrations to evaluate these honed capabilities were recommended and expected. FLM/TMC feedback will help refine subsequent system design.
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Summary

The Tower Flight Data Manager (TFDM) is the next generation air traffic control tower (ATCT) information system that integrates surveillance, flight data, and other sources, which enables advanced decision support tools (DSTs) to improve departure and arrival efficiency and reduce fuel burn at the airport. TFDM was exercised as a...

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Dallas/Fort Worth field demonstration #2 (DFW-2) final report for Tower Flight Data Manager (TFDM)

A tree-based ensemble method of the prediction and uncertainty quantification of aircraft landing times

January 22, 2012
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Conference Paper
Author:
Yan Glina
…
Published in:
10th Conf. on Artificial and Computational Intelligence, 22 January 2012.
Topic:
air traffic control
R&D area:
R&D group:

Summary

Accurate aircraft landing time predictions provide situational awareness for air traffic controllers, enable decision support algorithms and gate management planning. This paper presents a new approach for estimation of landing times using a tree-based ensemble method, namely Quantile Regression Forests. This method is suitable for real-time applications, provides robust and accurate predictions of landing times, and yields prediction intervals for individual flights, which provide a natural way of quantifying uncertainty. The approach was tested for arrivals at Dallas/Fort Worth International Airport over a range of days with a variety of operational conditions.
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Summary

Accurate aircraft landing time predictions provide situational awareness for air traffic controllers, enable decision support algorithms and gate management planning. This paper presents a new approach for estimation of landing times using a tree-based ensemble method, namely Quantile Regression Forests. This method is suitable for real-time applications, provides robust and...

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A tree-based ensemble method of the prediction and uncertainty quantification of aircraft landing times

A statistical learning approach to the modeling of aircraft taxi time

October 3, 2010
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Conference Paper
Author:
Richard K. Jordan
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Published in:
29th Digital Avionics Systems Conf., 3 October 2010.
Topic:
aircraft
R&D area:
R&D group:

Summary

Modeling aircraft taxi operations is an important element in understanding current airport performance and where opportunities may lie for improvements. A statistical learning approach to modeling aircraft taxi time is presented in this paper. This approach allows efficient identification of relatively simple and easily interpretable models of aircraft taxi time, which are shown to yield remarkably accurate predictions when tested on actual data.
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Summary

Modeling aircraft taxi operations is an important element in understanding current airport performance and where opportunities may lie for improvements. A statistical learning approach to modeling aircraft taxi time is presented in this paper. This approach allows efficient identification of relatively simple and easily interpretable models of aircraft taxi time...

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A statistical learning approach to the modeling of aircraft taxi time

Benefits assessment methodology for an air traffic control tower advanced automation system

September 13, 2010
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Conference Paper
Author:
Thomas G. Reynolds
…
Published in:
ATIO 2010: 10th AIAA Aviation Technology Integration and Operations Conf., 13-15 September 2010.
Topic:
air traffic control
R&D area:
R&D group:

Summary

This paper presents a benefits assessment methodology for an air traffic control tower advanced automation system called the Tower Flight Data Manager (TFDM), which is being considered for development by the FAA to support NextGen operations. The standard FAA benefits analysis methodology is described, together with how it has been tailored to the TFDM application to help inform the development process and the business case for system deployment. Parts of the methodology are illustrated through data analysis and modeling, and insights are presented to help prioritize TFDM capability development.
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Summary

This paper presents a benefits assessment methodology for an air traffic control tower advanced automation system called the Tower Flight Data Manager (TFDM), which is being considered for development by the FAA to support NextGen operations. The standard FAA benefits analysis methodology is described, together with how it has been...

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Benefits assessment methodology for an air traffic control tower advanced automation system
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