An Evaluation of a Flight Deck Interval Management Algorithm including Delayed Target Trajectories

Swieringa, Kurt A.; Underwood, Matthew C.; Barmore, Bryan E.; Leonard, Robert D.

doi:10.2514/6.2014-3148

Cited by 10 publications

(3 citation statements)

References 9 publications

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“…Both human-in-theloop simulations and fast-time simulations that investigated the performance of the ASTAR12 algorithm and its acceptability to pilots and controllers indicated that the ground speed term that was added to ASTAR12 improved compatibility with TMA-TM and CMS; however, there were cases where the ground speed term contributed to undesirable speed reversals. 14,22,23 The ASTAR12 algorithm was updated to support the Capture, Cross, and Maintain operations that were investigated in this simulation; the updated version of ASTAR is called ASTAR13. The primary modification was a new state-based CTD speed control law that was added to support the Capture and Maintain operations, and the "maintain" phase of the Cross operation; that is, the phase that occurs after the IM aircraft crosses the achieve-by point.…”

Section: Interval Management (Im)mentioning

confidence: 99%

System Performance of an Integrated Airborne Spacing Algorithm with Ground Automation

Swieringa

Wilson

Baxley

2016

16th AIAA Aviation Technology, Integration, and Operations Conference

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The National Aeronautics and Space Administration's (NASA's) first Air Traffic Management (ATM) Technology Demonstration (ATD-1) was created to facilitate the transition of mature ATM technologies from the laboratory to operational use. The technologies selected for demonstration are the Traffic Management Advisor with Terminal Metering (TMA-TM), which provides precise time-based scheduling in the Terminal airspace; Controller Managed Spacing (CMS), which provides controllers with decision support tools to enable precise schedule conformance; and Interval Management (IM), which consists of flight deck automation that enables aircraft to achieve or maintain precise spacing behind another aircraft. Recent simulations and IM algorithm development at NASA have focused on trajectory-based IM operations where aircraft equipped with IM avionics are expected to achieve a spacing goal, assigned by air traffic controllers, at the final approach fix. The recently published IM Minimum Operational Performance Standards describe five types of IM operations. This paper discusses the results and conclusions of a human-in-the-loop simulation that investigated three of those IM operations. The results presented in this paper focus on system performance and integration metrics. Overall, the IM operations conducted in this simulation integrated well with ground-based decisions support tools and certain types of IM operational were able to provide improved spacing precision at the final approach fix; however, some issues were identified that should be addressed prior to implementing IM procedures into real-world operations.

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Section: Interval Management (Im)mentioning

confidence: 99%

System Performance of an Integrated Airborne Spacing Algorithm with Ground Automation

Swieringa

Wilson

Baxley

2016

16th AIAA Aviation Technology, Integration, and Operations Conference

Self Cite

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“…The method is extended to more general scenarios by means of a binary tree. The paper [6] studies the efficient merging for a pair of aircraft in the presence of uncertainty. The paper [7] also studies the spacing problem for pairs of aircraft given the lack of accurate trajectory information.…”

Section: Literature Surveymentioning

confidence: 99%

An Elementary Algorithm for Autonomous Air Terminal Merging and Interval Management

White

2017

AIAA Modeling and Simulation Technologies Conference

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A central element of air traffic management is the safe merging and spacing of aircraft during the terminal area flight phase. This paper derives and examines an algorithm for the merging and interval managing problem for Standard Terminal Arrival Routes. It describes a factor analysis for performance based on the distribution of arrivals, the operating period of the terminal, and the topology of the arrival routes; then presents results from a performance analysis and from a safety analysis for a realistic topology based on typical routes for a runway at Phoenix International Airport. The heart of the safety analysis is a statistical derivation on how to conduct a safety analysis for a local simulation when the safety requirement is given for the entire airspace I. INTRODUCTION This study develops and examines an algorithm for the merging and interval managing problem for Standard Terminal Arrival Routes (STARS). Once the algorithm is derived, there are two parts to this study. The first part consists of a factor analysis for performance that considers the distribution of arrivals, the operating period of the terminal, and the topology of the arrival routes. The second part conducts a performance analysis and a safety analysis for a realistic topology based on the routes for one of the runways at Phoenix International Airport. The overall objective is to increase understanding of air traffic by creating algorithms and models. The merging and interval management of aircraft in a terminal appears to be a topic that can evolve into other areas. Secondary objectives include:  Simulation for part of or all of the national airspace-such simulations can contribute to more efficient and safer routing and operations, and such simulations depend on accurate and efficient models of terminal operations.  Operation of autonomous aircraftautonomous aircraft can perform routine and boring tasks such as monitoring a forest for fires, and they can perform dangerous tasks such as crop dusting. We are more willing to send an autonomous craft instead of a piloted airplane into hazardous conditions. In the near future, we may see the autonomous operation of cargo carriers.  Layout of arrival routesthere may or may not be two different criteria. One is the efficient and safe conduction of autonomous procedures. The other is to ease the burden of an air traffic controller. For air traffic controllers, our conjecture is that easing the burden leads to more efficient and safer operation.  Extension to traffic outside the terminal areain the material below, when an aircraft arrives at the terminal area, the algorithm computes whether or not it can enter the queue. One possibility is the converse of this where the algorithm computes the range of arrival speeds and arrival times that allow it to enter the queue. This can be applied to aircraft in transit. It can possibly be extended to cover the entire flight including takeoff time.

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“…The National Aeronautics and Space Administration (NASA) conducted a computer simulation to assess the performance of a recently modified airborne spacing algorithm used in a suite of integrated air/ground technologies that allow Interval Management (IM) operations to occur in high-density terminal environments (Swieringa et al 2014). IM consists of flight deck automation that enables aircraft to achieve or maintain precise spacing behind a preceding aircraft, which is referred to as the target aircraft.…”

Section: Introductionmentioning

confidence: 99%

Inference for under-dispersed data: Assessing the performance of an airborne spacing algorithm

Wilson

Leonard

Edwards

et al. 2018

Quality Engineering

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Poisson regression is a commonly used tool for analyzing rate data; however, the assumption that the mean and variance of a process are equal rarely holds true in practice. When this assumption is violated, a quasi-Poisson distribution can be used to account for the existing over-or under-dispersion. This paper presents an analysis of a study conducted by NASA to assess the performance of a new airborne spacing algorithm. A deterministic computer simulation was conducted to examine the algorithm in various conditions designed to simulate real-life scenarios, and two measures of algorithm performance were modeled using both continuous and categorical factors. Due to the presence of under-dispersion, tests for significance of main effects and twofactor interactions required bias adjustment. This paper presents a comparison of tests of effects for the Poisson and quasi-Poisson models, details of fitting these models using common statistical software packages, and calculation of dispersion tests.

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An Evaluation of a Flight Deck Interval Management Algorithm including Delayed Target Trajectories

Cited by 10 publications

References 9 publications

System Performance of an Integrated Airborne Spacing Algorithm with Ground Automation

System Performance of an Integrated Airborne Spacing Algorithm with Ground Automation

An Elementary Algorithm for Autonomous Air Terminal Merging and Interval Management

Inference for under-dispersed data: Assessing the performance of an airborne spacing algorithm

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