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NASA NTRS2014

Functional Analysis for an Integrated Capability of Arrival/Departure/Surface Management with Tactical Runway Management

The runway is a critical resource of any air transport system. It is used for arrivals, departures, and for taxiing aircraft and is universally acknowledged as a constraining factor to capacity for both surface and airspace operations. It follows that investigation of the effective use of runways, both in terms of selection and assignment as well as the timing and sequencing of the traffic is paramount to the efficient traffic flows. Both the German Aerospace Center (DLR) and NASA have developed concepts and tools to improve atomic aspects of coordinated arrival/departure/surface management operations and runway configuration management. In December 2012, NASA entered into a Collaborative Agreement with DLR. Four collaborative work areas were identified, one of which is called "Runway Management." As part of collaborative research in the "Runway Management" area, which is conducted with the DLR Institute of Flight Guidance, located in Braunschweig, the goal is to develop an integrated system comprised of the three DLR tools - arrival, departure, and surface management (collectively referred to as A/D/S-MAN) - and NASA's tactical runway configuration management (TRCM) tool. To achieve this goal, it is critical to prepare a concept of operations (ConOps) detailing how the NASA runway management and DLR arrival, departure, and surface management tools will function together to the benefit of each. To assist with the preparation of the ConOps, the integrated NASA and DLR tools are assessed through a functional analysis method described in this report. The report first provides the highlevel operational environments for air traffic management (ATM) in Germany and in the U.S., and the descriptions of the DLR's A/D/S-MAN and NASA's TRCM tools at the level of details necessary to compliment the purpose of the study. Functional analyses of each tool and a completed functional analysis of an integrated system design are presented next in the report. Future efforts to fully develop the ConOps will include: developing scenarios to fully test environmental, procedural, and data availability assumptions; executing the analysis by a walk-through of the integrated system using these scenarios; defining the appropriate role of operators in terms of their monitoring requirements and decision authority; executing the analysis by a walk-through of the integrated system with operator involvement; characterizing the environmental, system data requirements, and operator role assumptions for the ConOps.

Phojanamongkolkij, Nipa↗

NASA NTRS2013

Effects of Airport Tower Controller Decision Support Tool on Controllers Head-Up Time

Despite that aircraft positions and movements can be easily monitored on the radar displays at major airports nowadays, it is still important for the air traffic control tower (ATCT) controllers to look outside the window as much as possible to assure safe operations of traffic management. The present paper investigates whether an introduction of the NASA's proposed Spot and Runway Departure Advisor (SARDA), a decision support tool for the ATCT controller, would increase or decrease the controllers' head-up time. SARDA provides the controller departure-release schedule advisories, i.e., when to release each departure aircraft in order to minimize individual aircraft's fuel consumption on taxiways and simultaneously maximize the overall runway throughput. The SARDA advisories were presented on electronic flight strips (EFS). To investigate effects on the head-up time, a human-in-the-loop simulation experiment with two retired ATCT controller participants was conducted in a high-fidelity ATCT cab simulator with 360-degree computer-generated out-the-window view. Each controller participant wore a wearable video camera on a side of their head with the camera facing forward. The video data were later used to calculate their line of sight at each moment and eventually identify their head-up times. Four sessions were run with the SARDA advisories, and four sessions were run without (baseline). Traffic-load levels were varied in each session. The same set of user interface - EFS and the radar displays - were used in both the advisory and baseline sessions to make them directly comparable. The paper reports the findings and discusses their implications.

airport surface operations↗

NASA NTRS2016

Operational Characteristics Identification and Simulation Model Verification for Incheon International Airport

Incheon International Airport (ICN) is one of the hub airports in East Asia. Airport operations at ICN have been growing more than 5 percent per year in the past five years. According to the current airport expansion plan, a new passenger terminal will be added and the current cargo ramp will be expanded in 2018. This expansion project will bring 77 new stands without adding a new runway to the airport. Due to such continuous growth in airport operations and future expansion of the ramps, it will be highly likely that airport surface traffic will experience more congestion, and therefore, suffer from efficiency degradation. There is a growing awareness in aviation research community of need for strategic and tactical surface scheduling capabilities for efficient airport surface operations. Specific to ICN airport operations, a need for A-CDM (Airport - Collaborative Decision Making) or S-CDM (Surface - Collaborative Decision Making), and controller decision support tools for efficient air traffic management has arisen since several years ago. In the United States, there has been independent research efforts made by academia, industry, and government research organizations to enhance efficiency and predictability of surface operations at busy airports. Among these research activities, the Spot and Runway Departure Advisor (SARDA) developed and tested by National Aeronautics and Space Administration (NASA) is a decision support tool to provide tactical advisories to the controllers for efficient surface operations. The effectiveness of SARDA concept, was successfully verified through the human-in-the-loop (HITL) simulations for both spot release and runway operations advisories for ATC Tower controllers of Dallas-Fort Worth International Airport (DFW) in 2010 and 2012, and gate pushback advisories for the ramp controller of Charlotte-Douglas International Airport (CLT) in 2014. The SARDA concept for tactical surface scheduling is further enhanced and is being integrated into NASA's Airspace Technology Demonstration-2 (ATD-2) project for technology demonstration of Integrated Arrival-Departure-Surface (IADS) operations at CLT. This study is a part of the international research collaboration between KAIA (Korea Agency for Infrastructure Technology Advancement), KARI (Korea Aerospace Research Institute) and NASA, which is being conducted to validate the effectiveness of SARDA concept as a controller decision support tool for departure and surface management of ICN. This paper presents the preliminary results of the collaboration effort. It includes investigation of the operational environment of ICN, data analysis for identification of the operational characteristics of the airport, construction and verification of airport simulation model using Surface Operations Simulator and Scheduler (SOSS), NASA's fast-time simulation tool.

surface operations analysis↗

NASA NTRS2015

Mars Double-Flyby Free Returns

A subset of Earth-originating Mars double-flyby ballistic trajectories is documented. The subset consists of those trajectories that, after the first Mars flyby, perform a half-revolution transfer with Mars before returning to Earth. This class of free returns is useful for both human and robotic Mars missions because of its low geocentric energy at departure and arrival, and because of its extended stay time in the vicinity of Mars. Ballistic opportunities are documented over Earth departure dates ranging from 2015 through 2100. The mission is viable over three or four consecutive Mars synodic periods and unavailable for the next four, with the pattern repeating approximately every 15 years. Over the remainder of the century, a minimum Earth departure hyperbolic excess speed of 3.16 km/s, a minimum Earth atmospheric entry speed of 11.47 km/s, and a minimum flight time of 904 days are observed. The algorithm used to construct these trajectories is presented along with several examples.

Jesick, Mark↗

NASA NTRS2017

Optimization of Airport Surface Traffic: A Case-Study of Incheon International Airport

This study aims to develop a controllers' decision support tool for departure and surface management of ICN. Airport surface traffic optimization for Incheon International Airport (ICN) in South Korea was studied based on the operational characteristics of ICN and airspace of Korea. For surface traffic optimization, a multiple runway scheduling problem and a taxi scheduling problem were formulated into two Mixed Integer Linear Programming (MILP) optimization models. The Miles-In-Trail (MIT) separation constraint at the departure fix shared by the departure flights from multiple runways and the runway crossing constraints due to the taxi route configuration specific to ICN were incorporated into the runway scheduling and taxiway scheduling problems, respectively. Since the MILP-based optimization model for the multiple runway scheduling problem may be computationally intensive, computation times and delay costs of different solving methods were compared for a practical implementation. This research was a collaboration between Korea Aerospace Research Institute (KARI) and National Aeronautics and Space Administration (NASA).

surface management↗

NASA NTRS2017

Optimization of Airport Surface Traffic: A Case-Study of Incheon International Airport

This study aims to develop a controllers decision support tool for departure and surface management of ICN. Airport surface traffic optimization for Incheon International Airport (ICN) in South Korea was studied based on the operational characteristics of ICN and airspace of Korea. For surface traffic optimization, a multiple runway scheduling problem and a taxi scheduling problem were formulated into two Mixed Integer Linear Programming (MILP) optimization models. The Miles-In-Trail (MIT) separation constraint at the departure fix shared by the departure flights from multiple runways and the runway crossing constraints due to the taxi route configuration specific to ICN were incorporated into the runway scheduling and taxiway scheduling problems, respectively. Since the MILP-based optimization model for the multiple runway scheduling problem may be computationally intensive, computation times and delay costs of different solving methods were compared for a practical implementation. This research was a collaboration between Korea Aerospace Research Institute (KARI) and National Aeronautics and Space Administration (NASA).

taxi scheduler↗

NASA NTRS2016

Exact and Heuristic Algorithms for Runway Scheduling

This paper explores the Single Runway Scheduling (SRS) problem with arrivals, departures, and crossing aircraft on the airport surface. Constraints for wake vortex separations, departure area navigation separations and departure time window restrictions are explicitly considered. The main objective of this research is to develop exact and heuristic based algorithms that can be used in real-time decision support tools for Air Traffic Control Tower (ATCT) controllers. The paper provides a multi-objective dynamic programming (DP) based algorithm that finds the exact solution to the SRS problem, but may prove unusable for application in real-time environment due to large computation times for moderate sized problems. We next propose a second algorithm that uses heuristics to restrict the search space for the DP based algorithm. A third algorithm based on a combination of insertion and local search (ILS) heuristics is then presented. Simulation conducted for the east side of Dallas/Fort Worth International Airport allows comparison of the three proposed algorithms and indicates that the ILS algorithm performs favorably in its ability to find efficient solutions and its computation times.

safe & surface operations↗

NASA NTRS2018

Simulation Evaluations of an Autonomous Urban Air Mobility Network Management and Separation Service

This paper presents an initial implementation of an autonomous Urban Air Mobility network management and aircraft separation service for urban airspace that does 1) departure and arrival scheduling across the network, 2) continuous trajectory management to ensure safe separation between aircraft, and 3) seamless integration with traditional operations. The highly-autonomous AutoResolver algorithm developed for traditional aviation was extended to provide these capabilities. An evaluation of this initial implementation was conducted in fast-time simulations using a dense, two-hour traffic scenario with Urban Air Mobility aircraft flying between a network of 20 vertiports in the Dallas-Fort Worth metroplex. When the spatial separation was reduced from 0:3nmi to 0:1nmi, the total de- lay decreased by 7:3%; when the temporal separation was reduced from 60s to 45s, the total delay decreased by 28:4%. The total number of conflict resolutions decreased by 26% and 17%, respectively. Furthermore, when a scheduling horizon greater than the duration of UAM flights was used (50min), most conflicts were resolved pre-departure producing ground delay. By comparison, when a shorter scheduling horizon was used (8min), most conflicts were resolved post-departure generating airborne delay. For all scheduling and separation constraints tested, AutoResolver prevented loss of separation from occurring. Urban Air Mobility operations have the ability to revolutionize how people and goods are transported and this paper presents initial research focusing on the high levels of autonomy required for an airspace system capable of scaling to handle significantly higher densities of aircraft.

Bosson, Christabelle S.↗

NASA NTRS2018

Tactical Surface Metering Procedures for Charlotte Douglas International Airport

NASA has been collaborating with the Federal Aviation Administration (FAA) and aviation industry partners to develop and demonstrate new concepts and technologies for the Integrated Arrival, Departure, and Surface (IADS) traffic management capabilities under the Airspace Technology Demonstration 2 (ATD-2) project. The primary goal of the ATD-2 project is to improve the predictability and the operational efficiency of the air traffic system in metroplex environments while maintaining or improving throughput by enhancing and integrating arrival, departure and surface prediction, scheduling, and management systems. In the Phase 1 Baseline IADS Demonstration, the tactical surface scheduling capability and the user interfaces for ramp controllers and ramp traffic managers were implemented for ramp operations. The purpose of the tactical surface scheduling capability is to provide the airline ramp controller with aircraft pushback advisories that prevent surface congestion and to respond to surface and airspace constraints that become known over relatively short time horizons. For this purpose, the tactical surface metering tool first estimates the capacity of current and near-future runway resources from flight schedule and surveillance data. With demand forecasts and predicted taxi trajectories, this tool computes an efficient runway schedule of aircraft in the planning horizon based on their readiness, Earliest Off-Block Times (EOBTs), and a ration by schedule (RBS) rule. Details on the implementation of the Tactical Surface Metering tool will be provided in the full paper. Both pushback and recommended hold times advisories provided by this surface metering tool are shown on the user interfaces for the ramp controller and the ramp traffic manager, called Ramp Traffic Console (RTC) and Ramp Manager Traffic Console (RMTC), respectively. There is excess queue time in the system due to demand capacity imbalance, this time can be taken as a hold on the runway queue or at the gate and was referred to as the Metering Value. This metering value can be adjusted by the Ramp Manager in collaboration with Air Traffic Controller-Tower Traffic Management Coordinator (TMC). They selected a set of metering values as default values for the tool during human-in-the-loop simulation. As the metering value increases, there is a decrease in the gate hold and increase in the queue time at the runway. Procedures and Information needs related to managing the surface metering procedures were researched in the simulated environment. These procedures will be compared to the procedures adopted at Charlotte Douglas International Airport when the tools were deployed and adopted in November 2017 for one departure push bank per day. Feedback regarding initial issues, information needs such as the need to see EOBTs on the flight data tags and how they compare to scheduled times will also be discussed in the full paper. Initial results will be provided regarding the choice of the metering value and how it was adjusted on a daily basis and what procedures evolved will also be presented in the paper.

surface metering↗

NASA NTRS2014

Coordination Between Sectors in Shared Airspace Operations

Recent studies have shown that a more efficient use of airspace may involve shared airspace operations, i.e., temporal as well as spatial separation of arrival and departure flows [1][2]. Temporal separation would permit a departure aircraft to fly through an arrival flow, depending on an available gap. This would necessitate careful and precise coordination between controllers in different sectors. Three methods of coordination which permit the penetration of a controller's airspace by another controller's aircraft are described: point out, look-and-go, and prearranged coordination procedure. Requirements of each method are given, along with associated problems that have surfaced in the field as described by Aviation Safety and Reporting System (ASRS) and other reports. A Human-in-the-Loop simulation was designed to compare two of the methods: point out and prearranged coordination procedures. In prearranged coordination procedures (P-ACP), the controllers control an aircraft in another controller's airspace according to specified prearranged procedures, without coordinating each individual aircraft with another controller, as is done with point outs. In the simulation, three experienced controllers rotated through two arrival sectors and a non-involved arrival sector of a Terminal Radar Approach Control (TRACON) airspace. Results of eighteen one-hour simulation runs (nine in each of the two conditions) showed no impact of the coordination method on separation violations nor on arrival times for 208 departing aircraft crossing an arrival stream. Participant assessment indicated that although both coordination conditions were acceptable, the prearranged coordination procedure condition was slightly safer, more efficient, timely, and overall, worked better operationally. Problems arose in the point out condition regarding controllers noticing acceptance of point outs. Also, in about half of the point-out runs, time pressure was felt to have had an impact on when and if the departures could cross an arrival stream. An additional problem with point outs may be confusion in the field about which controller has responsibility for separating point-out aircraft from other aircraft.

Parke, Bonny↗

NASA NTRS2019

Cloud Computing Methods for Near Rectilinear Halo Orbit Trajectory Design

Complicated mission design problems require innovative computational solutions. As spacecraft depart from a proposed Gateway in a Near Rectilinear Halo Orbit (NRHO), recontact analysis is required to avoid risk of collision and ensure safe operations. Escape dynamics from NRHOs are governed by multiple gravitational bodies, yielding a trajectory design space that is exhaustively large. This paper summarizes the recontact analysis for departure from the NRHO and describes how the Deep Space Trajectory Explorer (DSTE) trajectory design software incorporates high performance cloud computing to compute and visualize the orbit design space. Recent focus on exploration missions to cislunar space has kindled accelerated interest in multibody orbit solutions. Trajectory analysis in the presence of multiple gravity fields is complex, and innovative computational tools are needed to simplify complicated design spaces, to generate large quantities of data quickly, and to visualize the output for user accessibility. The Gateway mission is a prime example. The Gateway1 is proposed as a human outpost in deep space. The current baseline orbit for the Gateway is a Near Rectilinear Halo Orbit (NRHO) near the Moon.2 The NRHO exists in a regime that experiences the gravitational effects of the Earth and the Moon simultaneously, complicating orbit analysis. The mission design process benefits greatly from updated computational tools for multibody missions like the Gateway. As an example, consider the problem of assessing the risk of collision in an NRHO. As a staging location to missions to the lunar surface and beyond the Earth-Moon system, the Gateway will experience spacecraft and other objects regularly arriving and departing. Departing objects potentially include spent logistics modules, visiting crew vehicles, debris objects, wastewater particles, and cubesats. Each departure is governed by the dynamics of the Gateway orbit and the surrounding dynamical environment. Over time, any unmaintained object in such an orbit eventually departs due to the small instabilities associated with the NRHOs. A separation maneuver speeds the departure from the NRHO, but the effects of the maneuver on the spacecraft behavior depend on the location, magnitude, and direction of the burn. Escape dynamics from the NRHO with regard to these maneuver options open up an enormous potential trajectory design space where subtle changes in input can produce dramatically large changes in the results. Any departing object must avoid recontacting the Gateway as it leaves the lunar vicinity, and a recontact analysis thus involves a significant number of computations and extensive output data. To explore the dynamics of this extensive design space, the Deep Space Trajectory Explorer3 (DSTE) trajectory design software incorporates new High Performance Computing (HPC) services and novel interactive visualizations. This paper details the HPC and cloud infrastructure techniques that are implemented in the DSTE, applying the new capabilities to analysis of recontact risk with the Gateway in NRHO. NEAR RECTILINEAR HALO ORBITS The Gateway is planned to fly in a lunar NRHO as its baseline orbit. The NRHO families of orbits are subsets of the larger halo families, which originate from planar orbits near the L1 and L2 libration points; the Earth-Moon L2 halo family appears in Figure 1. Each halo orbit is perfectly periodic in the Circular Restricted 3-Body Problem (CR3BP) and becomes a quasi-periodic orbit in a higher fidelity ephemeris force model. The NRHOs are defined as those members of the halo family with bounded stability properties;2 they pass near the Moon at perilune and are nearly polar. Families exist with apolunes located both above the lunar north pole and above the lunar south pole; the Gateway is planned to reside in a southern L2 NRHO in a 9:2 resonance with the lunar synodic period. The 9:2 NRHO is characterized by a period of about 6.5 days, a perilune radius of about 3,500 km, and an apolune radius of about 71,000 km; it is strongly affected by the gravity of both the Earth and the Moon simultaneously. This NRHO offers extended communications with assets on the south pole of the Moon,4 as well as low-cost orbit maintenance and attitude control,5 favorable eclipse avoidance properties,6 and inexpensive transfers from Earth and to other destinations.5,7 The NRHO portion of the southern L2 halo family is highlighted in black in Figure 1, and the 9:2 NRHO appears in blue.

Phillips, Sean M.↗

NASA NTRS2022

NASA’s Strategic Analysis Cycle 2021 (SAC21) Human Mars Architecture

The National Aeronautics and Space Administration’s (NASA) Mars Architecture Team (MAT) was challenged to develop a mission architecture capable of transporting humans to the surface of Mars and back as fast—and as soon—as practical. This challenge represented a significant departure from previous approaches that minimized Earth-launched mass and maximized in-space transportation efficiency, often resulting in roundtrip missions of three years or more in duration. In the interest of crew health, MAT’s cross-Agency team of subject matter experts was challenged to develop an architecture capable of shortening crew time away from Earth to about two years. MAT was given specific mission constraints, such as number of crew, as well as mandates to minimize surface infrastructure as much as possible and to incorporate nuclear transportation options. The resulting MAT-developed concept, referred to here as the Strategic Analysis Cycle 2021 (SAC21) architecture, leverages Artemis elements and emerging commercial capabilities for cargo and logistics launches, and features a hybrid Nuclear Electric Propulsion (NEP)/Chemical transportation system able to complete the 1.8 billion kilometer round-trip journey to Mars and back in 760 to 850 days transit time for the 2039 Earth departure opportunity. Three Mars Descent Systems (MDS), each capable of landing about 25 metric tons of useful cargo on the surface of Mars, would be pre-deployed in advance of crew departure from Earth; two of these MDS’s would deliver a partially fueled Mars Ascent Vehicle (MAV), a fission power system, surface mobility, and additional MAV propellant. To minimize surface infrastructure, only two of the four Mars crew would descend and live in an MDS-landed pressurized rover, exploring the martian surface for 30 martian days, or sols, before returning to Mars orbit aboard their MAV and rejoining the other two crew on the Deep Space Transport for the Earth return voyage. Specifics of many of these architecture elements are detailed in separate technical publications; this paper outlines the end-to-end integrated architecture performance and concept of operations, including synergies with Artemis lunar architecture elements. It is important to note that NASA does not have a formal human Mars program and no decisions have been made; the architecture described here is intended to fill in an often-overlooked corner of the trade space, helping to complete the menu of options available to decision-makers as they chart the course for humans to Mars.

exploration↗

NASA NTRSDate not supplied

NASA’s Strategic Analysis Cycle 2021 (SAC21) Human Mars Architecture

[Note: this is the presentation for a companion paper with the following abstract] The National Aeronautics and Space Administration’s (NASA) Mars Architecture Team (MAT) was challenged to develop a mission architecture capable of transporting humans to the surface of Mars and back as fast—and as soon—as practical. This challenge represented a significant departure from previous approaches that minimized Earth-launched mass and maximized in-space transportation efficiency, often resulting in roundtrip missions of three years or more in duration. In the interest of crew health, MAT’s cross-Agency team of subject matter experts was challenged to develop an architecture capable of shortening crew time away from Earth to about two years. MAT was given specific mission constraints, such as number of crew, as well as mandates to minimize surface infrastructure as much as possible and to incorporate nuclear transportation options. The resulting MAT-developed concept, referred to here as the Strategic Analysis Cycle 2021 (SAC21) architecture, leverages Artemis elements and emerging commercial capabilities for cargo and logistics launches, and features a hybrid Nuclear Electric Propulsion (NEP)/Chemical transportation system able to complete the 1.8 billion kilometer round-trip journey to Mars and back in 760 to 850 days transit time for the 2039 Earth departure opportunity. Three Mars Descent Systems (MDS), each capable of landing about 25 metric tons of useful cargo on the surface of Mars, would be pre-deployed in advance of crew departure from Earth; two of these MDS’s would deliver a partially fueled Mars Ascent Vehicle (MAV), a fission power system, surface mobility, and additional MAV propellant. To minimize surface infrastructure, only two of the four Mars crew would descend and live in an MDS-landed pressurized rover, exploring the martian surface for 30 martian days, or sols, before returning to Mars orbit aboard their MAV and rejoining the other two crew on the Deep Space Transport for the Earth return voyage. Specifics of many of these architecture elements are detailed in separate technical publications; this paper outlines the end-to-end integrated architecture performance and concept of operations, including synergies with Artemis lunar architecture elements. It is important to note that NASA does not have a formal human Mars program and no decisions have been made; the architecture described here is intended to fill in an often-overlooked corner of the trade space, helping to complete the menu of options available to decision-makers as they chart the course for humans to Mars. Note: Slide 9 contains a ~2 min video, best viewed when downloaded. Once downloaded to begin video you may be requested to hit options and Trust this document.

Long-duration spaceflight↗

NASA NTRSDate not supplied

Machine Learning based Aircraft Performance Model Estimation for Trajectory Prediction

The accurate prediction of aircraft trajectory by ground-based decision support tools is a critical component of air traffic management in the US National Airspace System (NAS). Accurate predictions of where the aircraft will be in the future or when they will arrive at specific locations (e.g., fixes) is a key enabler for sequencing and efficient arrival management of flights. Traditional physics based aircraft trajectory prediction relies on a simplified point-mass total energy model whose parameters are referred to as Aircraft Performance Model (APM) parameters. Even though the performance coefficients and weight of an aircraft are a vital part of the aircraft performance model’s predictions and accuracy, these coefficients are proprietary in nature and therefore, unavailable to decision-support tools. Current approaches freeze some coefficients to default base of aircraft data (BADA) values and optimize others. However, the APM parameters are highly coupled by the flight dynamics and prioritizing one parameter over others leads to bias and skewed predictions. To alleviate this problem, we provide a combined optimization framework to predict all the critical (thrust, drag and weight) APM parameters. This paper is focused on training Machine Learning (ML) models that map historical flights to optimized APM parameters that provide the best fit (in terms of prediction error). Our dataset obtained from NASA’s Sherlock data warehouse is comprised of thousands of historical flights and includes weather and track data collected from 2019. Using different subsets of relevant features (e.g., aircraft type), we trained several ML models to estimate the aircraft’s take off weight, drag polar coefficients (both parasitic and lift induced), and thrust settings (multiplier applied to the maximum engine thrust). The chosen flights are from three of the most common aircraft types (B738, B737, and A320) arriving at four airports (LAX, DEN, MSP, and DFW). Our ML approach is comprised of two different solutions: 1- using a subset of features that are known prior to the flight departure and do not change during flight (such as engine type, current temperature at departure & destination airports, aircraft type) and 2 - using a subset of temporal features of the flight trajectory (such as cruise altitude, Mach, airspeed, and rate of climb) in addition to the pre-departure features from the first solution. The labels or target variables are the APM parameters that were obtained by an optimized ordinary differential equations (ODE) fitting process (applied to individual flights). The ODE-fitting is very time intensive and is therefore performed offline. Thus, training an ML model to learn the relationship between the flight features and ODE-generated labels enables faster estimation of the APM parameters and is therefore amenable to real-time prediction. Various ML models including linear regression, random forest, XGBoost, and neural network were trained, and the results are compared. After model validation and hyperparameter-tuning, we observed that the Random Forest model outperformed the other three models by the overall mean square error (MSE) of 2% for the first solution and 1.5% for the second solution. Finally, the ML-derived parameters are compared against default BADA APM parameters using NASA’s Autonomy Development toolkit (ADK) simulation software. The simulation results for one of each aircraft type is shown and discussed.

Aida Sharif Rohani↗

NASA NTRS2016

Methods for Determining Aircraft Surface State at Lesser-Equipped Airports

Tactical departure scheduling within a terminal airspace must accommodate a wide spectrum of surveillance and communication capabilities at multiple airports. The success of such a scheduler is highly dependent upon the knowledge of a departure's state while it is still on the surface. Airports within a common Terminal RAdar CONtrol (TRACON) airspace possess varying levels of surface surveillance infrastructure which directly impacts uncertainties in wheels-off times. Large airports have access to surface surveillance data, which is shared with the TRACON, while lesser-equipped airports still rely solely on controllers in Air Traffic Control Towers (Towers). Coordination between TRACON and Towers can be greatly enhanced when the TRACON controller has access to the surface surveillance and the associated decision-support tools at well-equipped airports. Similar coordination at lesser-equipped airports is still based on verbal communications. This paper investigates possible methods to reduce the uncertainty in wheels-off time predictions at the lesser-equipped airports through the novel use of Over-the-Air (OTA) data transmissions. We also discuss the methods and equipment used to collect sample data at lesser-equipped airports within a large US TRACON, as well as the data evaluation to determine if meaningful information can be extracted from it.

departure scheduling↗

NASA NTRS2013

SARDA: An Integrated Concept for Airport Surface Operations Management

The Spot and Runway Departure Advisor (SARDA) is an integrated decision support tool for airlines and air traffic control tower enabling surface collaborative decision making (CDM) and departure metering in order to enhance efficiency of surface operations at congested airports. The presentation describes the concept and architecture of the SARDA as a CDM tool, and the results from a human-in-the-loop simulation of the tool conducted in 2012 at the FutureFlight Central, the tower simulation facility. Also, presented is the current activities and future plan for SARDA development. The presentation was given at the meeting with the FAA senior advisor of the Surface Operations Office.

surface collaborative decision making↗

NASA NTRS2017

Airspace Technology Demonstration 2 (ATD-2) Integrated Surface and Airspace Simulation - Experiment Plan

This presentation describes the objectives and high level setup for the human-in-the-loop simulation of the integrated surface and airsapce simulation of the ATD-2 Integrated Arrival, Departure, Surface (IADS) system. The purpose of the simulation is to evaluate the functionality of the IADS system, including tactical surface scheduler, negotiation of departure times for the flights under Traffic Management Initiatives (TMIs), and data exchange between ATC Tower and airline Ramp. The same presentation was presented to serve the experiment review prior to the simulation.

Surface↗

NASA NTRS2020

Strategic Surface Metering at Charlotte Douglas International Airport

NASA is conducting a field test of the Airspace Technology Demonstration2 (ATD-2) to evaluate an Integrated Arrival, Departure, and Surface (IADS) traffic management system. The IADS system was deployed to Charlotte Douglas International Airport (CLT) in 2017 for a three-year field evaluation. The Phase 1 field evaluation included tactical surface metering, which manages departure excess taxi time by tactically assigning gate holds. Phase 2 built upon the lessons learned from Phase 1 to extend surface metering into the strategic metering timeframes. In this paper, we describe the strategic metering capabilities of the ATD-2 IADS system and the operational results from CLT.

Airspace Technology Demonstration 2↗