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114 records · Page 7
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NASA NTRS2014

NextGen Technologies on the FAA's Standard Terminal Automation Replacement System

This paper describes the integration, evaluation, and results from a high-fidelity human-in-the-loop (HITL) simulation of key NASA Air Traffic Management Technology Demonstration - 1 (ATD- 1) technologies implemented in an enhanced version of the FAA's Standard Terminal Automation Replacement System (STARS) platform. These ATD-1 technologies include: (1) a NASA enhanced version of the FAA's Time-Based Flow Management, (2) a NASA ground-based automation technology known as controller-managed spacing (CMS), and (3) a NASA advanced avionics airborne technology known as flight-deck interval management (FIM). These ATD-1 technologies have been extensively tested in large-scale HITL simulations using general-purpose workstations to study air transportation technologies. These general purpose workstations perform multiple functions and are collectively referred to as the Multi-Aircraft Control System (MACS). Researchers at NASA Ames Research Center and Raytheon collaborated to augment the STARS platform by including CMS and FIM advisory tools to validate the feasibility of integrating these automation enhancements into the current FAA automation infrastructure. NASA Ames acquired three STARS terminal controller workstations, and then integrated the ATD-1 technologies. HITL simulations were conducted to evaluate the ATD-1 technologies when using the STARS platform. These results were compared with the results obtained when the ATD-1 technologies were tested in the MACS environment. Results collected from the numerical data show acceptably minor differences, and, together with the subjective controller questionnaires showing a trend towards preferring STARS, validate the ATD-1/STARS integration.

air traffic management↗
NASA NTRS2016

Conducting Safe and Efficient Airport Surface Operations in a NextGen Environment

The Next Generation Air Transportation System (NextGen) vision proposes many revolutionary operational concepts, such as surface trajectory-based operations (STBO) and technologies, including display of traffic information and movements, airport moving maps (AMM), and proactive alerts of runway incursions and surface traffic conflicts, to deliver an overall increase in system capacity and safety. A piloted simulation study was conducted at the National Aeronautics and Space Administration (NASA) Langley Research Center to evaluate the ability of a flight crew to conduct safe and efficient airport surface operations while utilizing an AMM. Position accuracy of traffic was varied, and the effect of traffic position accuracy on airport conflict detection and resolution (CD&R) capability was measured. Another goal was to evaluate the crew's ability to safely conduct STBO by assessing the impact of providing traffic intent information, CD&R system capability, and the display of STBO guidance to the flight crew on both head-down and head-up displays (HUD). Nominal scenarios and off-nominal conflict scenarios were conducted using 12 airline crews operating in a simulated Memphis International Airport terminal environment. The data suggest that all traffic should be shown on the airport moving map, whether qualified or unqualified, and conflict detection and resolution technologies provide significant safety benefits. Despite the presence of traffic information on the map, collisions or near-collisions still occurred; when indications or alerts were generated in these same scenarios, the incidents were averted. During the STBO testing, the flight crews met their required time-of-arrival at route end within 10 seconds on 98 percent of the trials, well within the acceptable performance bounds of 15 seconds. Traffic intent information was found to be useful in determining the intent of conflicting traffic, with graphical presentation preferred. The CD&R system was only minimally effective during STBO because the prevailing visibility was sufficient for visual detection of conflicting traffic. Overall, the pilots indicated STBO increased general situation awareness but also negatively impacted workload, reduced the ability to watch for other traffic, and increased head-down time.

Jones, Denise R.↗
NASA NTRS2013

An Evaluation of Operational Airspace Sectorization Integrated System (OASIS) Advisory Tool

In January 2013, a human-in-the-loop evaluation of the Operational Airspace Sectorization Integrated System (OASIS) was conducted in the Airspace Operations Laboratory of the Human Systems Integration Division (Code TH) in conjunction with the Aviation Systems Division (Code AF). The development of OASIS is a major activity of the Dynamic Airspace Configuration (DAC) research focus area within the Aeronautics Research Mission Directorate (ARMD) Airspace Systems Program. OASIS is an advisory tool to assist Federal Aviation Administration (FAA) En Route Area Supervisors in their planning of sector combinedecombine operations as well as opening closing of Data-side (D-side) control positions. These advisory solutions are tailored to the predicted traffic demand over the next few hours. During the experiment, eight retired FAA personnel served as participants for a part-task evaluation of OASIS functionality, covering the user interface as well as the underlying algorithm. Participants gave positive feedback on both the user interface and the algorithm solutions for airspace configuration, including an excellent average rating of 94 on the tool usability scales. They also suggested various enhancements to the OASIS tool, which will be incorporated into the next tool development cycle for the full-scale human-in-the-loop evaluation to be conducted later this year.

decision support tools↗
NASA NTRS2015

Methodology to Define Delivery Accuracy Under Current Day ATC Operations

In order to enable arrival management concepts and solutions in a NextGen environment, ground- based sequencing and scheduling functions have been developed to support metering operations in the National Airspace System. These sequencing and scheduling algorithms as well as tools are designed to aid air traffic controllers in developing an overall arrival strategy. The ground systems being developed will support the management of aircraft to their Scheduled Times of Arrival (STAs) at flow-constrained meter points. This paper presents a methodology for determining the undelayed delivery accuracy for current day air traffic control operations. This new method analyzes the undelayed delivery accuracy at meter points in order to understand changes of desired flow rates as well as enabling definition of metrics that will allow near-future ground automation tools to successfully achieve desired separation at the meter points. This enables aircraft to meet their STAs while performing high precision arrivals. The research presents a possible implementation that would allow delivery performance of current tools to be estimated and delivery accuracy requirements for future tools to be defined, which allows analysis of Estimated Time of Arrival (ETA) accuracy for Time-Based Flow Management (TBFM) and the FAA's Traffic Management Advisor (TMA). TMA is a deployed system that generates scheduled time-of-arrival constraints for en- route air traffic controllers in the US. This new method of automated analysis provides a repeatable evaluation of the delay metrics for current day traffic, new releases of TMA, implementation of different tools, and across different airspace environments. This method utilizes a wide set of data from the Operational TMA-TBFM Repository (OTTR) system, which processes raw data collected by the FAA from operational TMA systems at all ARTCCs in the nation. The OTTR system generates daily reports concerning ATC status, intent and actions. Due to its availability, ease of use, and vast collection of data across several airspaces it was determined that the OTTR data set would be the best method to utilize moving forward with this analysis. The particular variables needed for further analysis were determined along with the necessary OTTR reports, by working closely with the repository team additional analysis reports were developed that provided key ETA and STA information at the freeze horizon. One major benefit of the OTTR data is that using the correct reports the data across several airports could be analyzed over large periods of time. The OTTR data processes the TBFM data daily and is stored in various formats across several airspaces. This allowed us to develop our own parsing methods and raw data processing that would not rely on other computationally expensive tools that perform more in depth analysis of similar sets of data. The majority of this work consisted of the development of the ability to filter flights to create a subset of flights that could be considered undelayed, which is defined as a flight at the freeze horizon with an ETA and STA difference that was minimal or close to zero. This was a broad method that allowed the consideration of a large data set which consisted of all the traffic across a two month period in 2013, the hottest and coldest months, arriving into four airports: George Bush Intercontinental, Denver International, Los Angeles International, and Phoenix Sky Harbor.

delivery accuracy↗
NASA NTRS2016

Assessment of Delivery Accuracy in an Operational-Like Environment

In order to enable arrival management concepts and solutions in a Next Generation Air Transportation System (NextGen) environment, ground-based sequencing and scheduling functions were developed to support metering operations in the National Airspace System. These sequencing and scheduling tools are designed to assist air traffic controllers in developing an overall arrival strategy, from enroute down to the terminal area boundary. NASA developed a ground system concept and protoype capability called Terminal Sequencing and Spacing (TSAS) to extend metering operations into the terminal area to the runway. To demonstrate the use of these scheduling and spacing tools in an operational-like environment, the FAA, NASA, and MITRE conducted an Operational Integration Assessment (OIA) of a prototype TSAS system at the FAA's William J. Hughes Technical Center (WJHTC). This paper presents an analysis of the arrival management strategies utilized and delivery accuracy achieved during the OIA. The analysis demonstrates how en route preconditioning, in various forms, and schedule disruptions impact delivery accuracy. As the simulation spanned both enroute and terminal airspace, the use of Ground Interval Management - Spacing (GIM-S) enroute speed advisories was investigated. Delivery accuracy was measured as the difference between the Scheduled Time of Arrival (STA) and the Actual Time of Arrival (ATA). The delivery accuracy was computed across all runs conducted during the OIA, which included deviations from nominal operations which are known to commonly occur in real operations, such as schedule changes and missed approaches. Overall, 83% of all flights were delivered into the terminal airspace within +/- 30 seconds of their STA and 94% of flights were delivered within +/- 60 seconds. The meter fix delivery accuracy standard deviation was found to be between 36 and 55 seconds across all arrival procedures. The data also showed when schedule disruptions were excluded, the percentage of aircraft delivered within +/- 30 seconds was between 85 and 90% across the various arrival procedures at the meter fix. This paper illustrates the ability to meet new delivery accuracy requirements in an operational-like environment using operational systems and NATCA controller participants, while also including common events that might cause disruptions to the schedule and overall system.

Air Traffic Scheduling↗
NASA NTRS2009

A Mixed Integer Linear Program for Airport Departure Scheduling

Aircraft departing from an airport are subject to numerous constraints while scheduling departure times. These constraints include wake-separation constraints for successive departures, miles-in-trail separation for aircraft bound for the same departure fixes, and time-window or prioritization constraints for individual flights. Besides these, emissions as well as increased fuel consumption due to inefficient scheduling need to be included. Addressing all the above constraints in a single framework while allowing for resequencing of the aircraft using runway queues is critical to the implementation of the Next Generation Air Transport System (NextGen) concepts. Prior work on airport departure scheduling has addressed some of the above. However, existing methods use pre-determined runway queues, and schedule aircraft from these departure queues. The source of such pre-determined queues is not explicit, and could potentially be a subjective controller input. Determining runway queues and scheduling within the same framework would potentially result in better scheduling. This paper presents a mixed integer linear program (MILP) for the departure-scheduling problem. The program takes as input the incoming sequence of aircraft for departure from a runway, along with their earliest departure times and an optional prioritization scheme based on time-window of departure for each aircraft. The program then assigns these aircraft to the available departure queues and schedules departure times, explicitly considering wake separation and departure fix restrictions to minimize total delay for all aircraft. The approach is generalized and can be used in a variety of situations, and allows for aircraft prioritization based on operational as well as environmental considerations. We present the MILP in the paper, along with benefits over the first-come-first-serve (FCFS) scheme for numerous randomized problems based on real-world settings. The MILP results in substantially reduced delays as compared to FCFS, and the magnitude of the savings depends on the queue and departure fix structure. The MILP assumes deterministic aircraft arrival times at the runway queues. However, due to taxi time uncertainty, aircraft might arrive either earlier or later than these deterministic times. Thus, to incorporate this uncertainty, we present a method for using the MILP with "overlap discounted rolling planning horizon". The approach is based on valuing near-term decision results more than future ones. We develop a model of taxitime uncertainty based on real-world data, and then compare the baseline FCFS delays with delays using the above MILP in a simple rolling-horizon method and in the overlap discounted scheme.

Gupta, Gautam↗