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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Modeling and Simulating Airport Surface Operations with Gate Conflicts

The Surface Operations Simulator and Scheduler (SOSS) is a fast-time simulation platform used to develop and test future surface scheduling concepts such as NASA's Air Traffic Demonstration 2 of time-based surface metering at Charlotte Douglass International Airport (CLT). Challenges associated with CLT surface operations have driven much of SOSS development. Recently, SOSS functionality for modeling harsdstand operations was developed to address gate conflicts, which occur when an arrival and departure wish to occupy the same gate at the same time. Because surface metering concepts such as ATD2 have the potential to increase gates conflicts as departures are held at their gates, it is important to study the interaction between surface metering and gate conflict management. Several approaches to managing gate conflicts with and without the use of hardstands were simulated and their effects on surface operations and scheduler performance compared.

Zelinski, Shannon↗
NASA NTRS2016

Analyzing Double Delays at Newark Liberty International Airport

When weather or congestion impacts the National Airspace System, multiple different Traffic Management Initiatives can be implemented, sometimes with unintended consequences. One particular inefficiency that is commonly identified is in the interaction between Ground Delay Programs (GDPs) and time based metering of internal departures, or TMA scheduling. Internal departures under TMA scheduling can take large GDP delays, followed by large TMA scheduling delays, because they cannot be easily fitted into the overhead stream. In this paper we examine the causes of these double delays through an analysis of arrival operations at Newark Liberty International Airport (EWR) from June to August 2010. Depending on how the double delay is defined between 0.3 percent and 0.8 percent of arrivals at EWR experienced double delays in this period. However, this represents between 21 percent and 62 percent of all internal departures in GDP and TMA scheduling. A deep dive into the data reveals that two causes of high internal departure scheduling delays are upstream flights making up time between their estimated departure clearance times (EDCTs) and entry into time based metering, which undermines the sequencing and spacing underlying the flight EDCTs, and high demand on TMA, when TMA airborne metering delays are high. Data mining methods (currently) including logistic regression, support vector machines and K-nearest neighbors are used to predict the occurrence of double delays and high internal departure scheduling delays with accuracies up to 0.68. So far, key indicators of double delay and high internal departure scheduling delay are TMA virtual runway queue size, and the degree to which estimated runway demand based on TMA estimated times of arrival has changed relative to the estimated runway demand based on EDCTs. However, more analysis is needed to confirm this.

traffic management advisor↗
NASA NTRS2018

Tactical Surface Metering Procedures and Information Needs for Charlotte Douglas International Airport

NASA has been working with the FAA and aviation industry partners to develop and demonstrate new concepts and technologies that integrate arrival, departure, and surface traffic management capabilities. In the fall of 2017, NASA began deployment of their technologies in a phased manner to assist with the integrated surface and airspace operations at Charlotte Douglas International Airport (Charlotte, NC). Initial technologies included a tactical surface metering tool and data exchange elements between the airline-controlled ramp and Federal Aviation Administration controlled ATC Tower. In this paper, we focus on the procedures associated with the tactical surface metering tool used in the ramp control tower. This tool was first calibrated in Human-In-the-Loop simulations and was further developed when it was used in the operational world. This paper describes the collaborative procedures that the users exercised in their respective operational worlds to enable surface metering and how several metrics were used to improve the metering algorithm.

Human Factors↗
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 NTRS2018

Evolution of Electronic Approval Request Procedures at Charlotte Douglas International Airport

A departure approval request, or APREQ, establishes a later runway departure time for a flight, allowing it to absorb tactical delay on the ground. APREQ times are traditionally coordinated by a process known as "call-for-release" whereby an airport surface traffic manager calls an airspace traffic manager on the telephone. This research examines new electronic APREQ coordination enabled by the NASA Airspace Technology Demonstration-2 system and compares it to the traditional call-for-release method of coordination. During the initial deployment period, electronic APREQ coordination was used for more than half of eligible flights. A majority of electronic requests were approved in less than one minute on average. Both the average tactical delay and compliance with the electronically coordinated departure times did not differ significantly from departure times coordinated using call-for-release.

APREQ↗
NASA NTRS2018

Performance Evaluation of Conflict-Free Trajectory Taxiing in Airport Ramp Area Using Fast-Time Simulations

The German Aerospace Center (DLR) and the National Aeronautics and Space Administration (NASA) have been collaborating to conduct joint research addressing future surface traffic management challenges. The surface management tool from DLR, called Taxi Routing for Aircraft: Creation and Controlling (TRACC), was adapted to be integrated in NASA's fast-time simulation environment called Surface Operations Simulator and Scheduler (SOSS). The research described in this paper 1) applied TRACC to trajectory-based ramp traffic management, where TRACC generates conflict-free aircraft trajectories in a congested ramp area, 2) investigated the feasibility of the concept through the integrated TRACC-SOSS fast-time simulation, and 3) evaluated the performance of the integrated system. For this activity, TRACC was adapted for ramp operations at Charlotte Douglas International Airport, called TRACC_PB (TRACC for pushback optimization). TRACC_PB provides four-dimensional taxi trajectories with a command speed profile for each aircraft following standard taxi routes within the ramp area. In this study, departures are given the Target Movement Area entry Times (TMATs) provided by the baseline surface metering scheduler based on NASA's Spot and Runway Departure Advisor (SARDA). TRACC_PB also calculates optimal pushback times for departures, as well as the times when arrivals shall enter the ramp, the Target Movement area Exit Times (TMETs). The initial results showed that the TRACC_PB successfully generated conflict-free trajectories for the ramp area taxi operations and improved taxiing efficiency compared to the baseline results. TRACC_PB aimed to provide conflict-free taxi routes avoiding any stops while taxiing. This resulted in longer gate hold times for departures and postponed throughput values compared to the baseline simulation without trajectory optimization. Having conflict-free routes without stoppage also created shorter taxi times but required renegotiation of the given TMATs. TRACC_PB also achieved reductions in both fuel consumption and engine emissions (17% for departures and 10% for arrivals), which correlate with the ramp taxi time reduction.

trajectory-based taxi operations↗
NASA NTRS2016

Analyzing Double Delays at Newark Liberty International Airport (EWR)

When weather or congestion impacts the National Airspace System, multiple different Traffic Management Initiatives can be implemented, sometimes with unintended consequences. One particular perceived inequity that is commonly identified is in the interaction between Ground Delay Programs (GDPs) and time based scheduling of internal departures by the Traffic Management Advisor (TMA) (now operationally superseded by the FAA's the Time-Based Flow Management system). Internal departures under TMA scheduling can take large GDP delays, followed by large TMA scheduling delays, because they cannot easily fit into the arrival flow at the runway. In this paper we examine the causes of these double delays through an analysis of arrival operations at Newark Liberty International Airport (EWR) from June to August 2010. TMA scheduling delays are found to be generally higher than TMA airborne metering delays, regardless of prior GDP delays. Depending on how the double delay is defined, between 42 and 62 of all internal departures in GDP and TMA scheduling experienced double delays in this period. A deep dive into the data reveals that contributors to double delays include upstream flights departing before their Expect Departure Clearance Times (EDCTs); differences in the rates used for setting EDCTs and TMA Scheduled Times of Arrival; differences in the arrival demand expected based on EDCTs and the arrival demand entering TMA; and shorter en route times between takeoff and entry into TMA than assumed in the calculation of flight EDCTs, all of which undermine the sequencing and spacing underlying flight EDCTs. Double delays are also found to coincide with periods in which the virtual runway arrival queue being served by a TMA is large, there are periods of high demand relative to capacity, and there are high airborne metering delays. Data mining techniques are used to confirm that each of these factors contribute to the occurrence of double delay andor high internal departure scheduling delay across three months of data from June to August 2010. Predictors of the occurrence of double delay and high TMA scheduling delay are built using logistic regression, providing prediction accuracies of 69 and 73, respectively.

traffic flow management↗
NASA NTRS2017

Take-off Engine Particle Emission Indices for In-Service Aircraft at Los Angeles International Airport

We present ground‐based, advected aircraft engine emissions from flights taking off at Los Angeles International Airport. 275 discrete engine take‐off plumes were observed on 18 and 25 May 2014 at a distance of 400 m downwind of the runway. CO2 measurements are used to convert the aerosol data into plume‐average emissions indices that are suitable for modelling aircraft emissions. Total and non‐volatile particle number EIs are of order 1016‐1017 kg‐1 and 1014‐1016 kg‐1, respectively. Black‐carbon‐equivalent particle mass EIs vary between 175‐941 mg kg‐1 (except for the GE GEnx engines at 46 mg kg‐1). Aircraft tail numbers recorded for each take‐off event are used to incorporate aircraft‐ and engine‐specific parameters into the data set. Data acquisition and processing follow standard methods for quality assurance. A unique aspect of the data set is the mapping of aerosol concentration time series to integrated plume EIs, aircraft and engine specifications, and manufacturer‐reported engine emissions certifications. The integrated data enable future studies seeking to understand and model aircraft emissions and their impact on air quality.

Richard H Moore↗
NASA NTRS2015

Integration of Uncertain Ramp Area Aircraft Trajectories and Generation of Optimal Taxiway Schedules at Charlotte Douglas (CLT) Airport

The integration of aircraft maneuver characteristics into an optimal taxiway scheduling solution is challenging due to the uncertainties that are intrinsic to ramp area aircraft trajectories. To address the challenge, we build a stochastic model of ramp area aircraft trajectories that is used to generate a probabilistic measure of conflict within the Charlotte Douglas International Airport (CLT) ramp area. Parameters of the conflict distributions are estimated and passed to a Mixed Integer Linear Program that solves for an optimal taxiway schedule constrained to be conflict free in the presence of trajectory uncertainties. Here we extend our previous research by accounting for departing and arriving aircraft whereas our prior formulation only accounted for departing aircraft.

taxiway schedule↗
NASA NTRS2014

Analysis of Airport Ground Delay Program Decisions Using Data Mining Techniques

Air traffic service providers have to make decisions regarding changes to air traffic flow in the event of major weather disturbances and traffic congestions to maintain safety of the system. The behavior of the air traffic management system will be more predictable if consistent decisions are made under similar traffic and weather conditions. Consistency of deciding on control action depends on the weather and traffic conditions as well as accuracy in predicting these conditions. Weather parameters (defined in terms of forecast and actual weather and traffic conditions) on different days can be used to categorize days into days with little decision consistency, days with moderate decision consistency and days with high decision consistency. Four years of traffic, weather and ground delay program decisions data at major airports in the United States are used in the analysis. This paper examines performance of different data mining methods in the three regions of decision consistency. Not surprisingly, data mining methods have the best performance in the region of most decision consistency and have the poorest performance in the region of little decision consistency. In applications where data mining methods have differing performance in differing regions, it would be more useful to characterize the region specific performance instead of characterizing performance by a single parameter. Finally, the results show no significant variation in the performance of different data mining methods for this particular problem. The fact that different mining methods show no significant variation also provides further confidence in the results of data mining methods. Work in this abstract discusses initial results. This paper describes the results in terms of both forecast and actual environmental conditions and discusses how prediction errors impact decision consistency.

Kulkarni, Deepak↗
NASA NTRSDate not supplied

Impact of ATD-2 Tools on Human Factors Metrics at Charlotte Douglas International Airport

New decision support tools were introduced at Charlotte Douglas International Airport (CLT) as part of NASA’s Air Traffic Demonstration 2 (ATD-2) project. ATD-2 is based on concepts which enable integrated arrival, departures, and surface (IADS) operations in a metroplex environment. Metrics on environmental benefits from ATD-2’s surface metering at CLT have shown impressive reductions in fuel consumption and CO2 emissions. Questions have remained on 1) the human factors impact of these new ATD-2 tools and surface metering and 2) the users’ perceptions of how these tools affect operations. To address these questions, post-bank surveys were designed for the four types of users of the new tools at CLT: Ramp Controllers, Ramp Managers, Tower Traffic Management Coordinators (TMCs) and Terminal Radar Approach Control (TRACON) TMCs. These surveys were administered during five-day blocks at three different times. Three types of banks were compared: No ATD-2 tool use, ATD-2 tool use, and ATD-2 use plus surface metering. Results indicate that higher workload or decreased situation awareness was not significantly associated with ATD-2 tool use more than no ATD-2 tool use--in fact for many users the opposite was true. Also, acceptability and operational efficiency measures indicate that the users did not perceive that banks with ATD-2 tools were significantly less acceptable or less efficient than banks without ATD-2 tools.

Airspace Technology Demonstration 2↗
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↗
NASA NTRS2020

Impact of ATD-2 Tools on Human Factor Metrics at Charlotte Douglas International Airport

New decision support tools were introduced at Charlotte Douglas International Airport (CLT) as part of NASA’s Air Traffic Demonstration 2 (ATD-2) project. ATD-2 is based on concepts which enable integrated arrival, departures, and surface (IADS) operations in a metroplex environment. Metrics on environmental benefits from ATD-2’s surface metering at CLT have shown impressive reductions in fuel consumption and CO2 emissions. Questions have remained on 1) the human factors impact of these new ATD-2 tools and surface metering and 2) the users’ perceptions of how these tools affect operations. To address these questions, post-bank surveys were designed for the four types of users of the new tools at CLT: Ramp Controllers, Ramp Managers, Tower Traffic Management Coordinators (TMCs) and Terminal Radar Approach Control (TRACON) TMCs. These surveys were administered during five-day blocks at three different times. Three types of banks were compared: No ATD-2 tool use, ATD-2 tool use, and ATD-2 use plus surface metering. Results indicate that higher workload or decreased situation awareness was not significantly associated with ATD-2 tool use more than no ATD-2 tool use--in fact for many users the opposite was true. Also, acceptability and operational efficiency measures indicate that the users did not perceive that banks with ATD-2 tools were significantly less acceptable or less efficient than banks without ATD-2 tools.

Bonny K Parke↗
NASA NTRSDate not supplied

Fast-Time Simulation of Airport Surface Movement

Fast-time simulation of airport surface operations allows forthe thorough testing and analysis of modeling concepts andalgorithms for managing aircraft movement. However, air-port surface movement operations present a difficult, large-scale logistics problem. This paper summarizes the resultsof a multi-year effort to build a fast-time simulator of largeairport surface movement, using San Francisco IntrnationalAirport as a case study.

Simulation↗
NASA NTRSDate not supplied

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗
NASA NTRSDate not supplied

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗