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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records

All sources NASA NTRS DOE OSTI

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 NTRS2010

A Concept and Implementation of Optimized Operations of Airport Surface Traffic

This paper presents a new concept of optimized surface operations at busy airports to improve the efficiency of taxi operations, as well as reduce environmental impacts. The suggested system architecture consists of the integration of two decoupled optimization algorithms. The Spot Release Planner provides sequence and timing advisories to tower controllers for releasing departure aircraft into the movement area to reduce taxi delay while achieving maximum throughput. The Runway Scheduler provides take-off sequence and arrival runway crossing sequence to the controllers to maximize the runway usage. The description of a prototype implementation of this integrated decision support tool for the airport control tower controllers is also provided. The prototype decision support tool was evaluated through a human-in-the-loop experiment, where both the Spot Release Planner and Runway Scheduler provided advisories to the Ground and Local Controllers. Initial results indicate the average number of stops made by each departure aircraft in the departure runway queue was reduced by more than half when the controllers were using the advisories, which resulted in reduced taxi times in the departure queue.

Jung, Yoon C.↗

NASA NTRS2016

Scheduler Design Criteria: Requirements and Considerations

This presentation covers fundamental requirements and considerations for developing schedulers in airport operations. We first introduce performance and functional requirements for airport surface schedulers. Among various optimization problems in airport operations, we focus on airport surface scheduling problem, including runway and taxiway operations. We then describe a basic methodology for airport surface scheduling such as node-link network model and scheduling algorithms previously developed. Next, we explain how to design a mathematical formulation in more details, which consists of objectives, decision variables, and constraints. Lastly, we review other considerations, including optimization tools, computational performance, and performance metrics for evaluation.

NASA-KAIA/KARI research collaboration↗

NASA NTRS2018

Comparison of First-Come First-Served and Optimization Based Scheduling Algorithms for Integrated Departure and Arrival Management

Korea Aerospace Research Institute (KARI) and National Aeronautics and Space Administration (NASA) are investigating scheduling algorithms that will be a part of an integrated arrival and departure management system. Inha University, one of the Korean collaborators of KARI, developed an Extended First-Come First-Served (EFCFS) algorithm that is robust and efficient. However, since the EFCFS algorithm sequentially computes the schedule based on priority, the end results may not be optimal for system efficiency. The approach based on Mixed Integer Linear Programming (MILP) originally developed by NASA and modified by KARI is known to produce better schedules at the expense of computational cost. In this paper, the two different scheduling approaches are compared using common traffic scenarios and constraints at Incheon International Airport. Capabilities to apply weight class based wake turbulence runway separation minima and Miles-in-Trail (MIT) restrictions at selected meter fixes are added to the previously developed EFCFS scheduler. Based on historic data, 40 departures and 20 arrivals are chosen in a one-hour period and 100 scenarios were created by randomly assigning gate numbers, gate departure times, and runway landing times. With the current runway separation requirements, MILP resulted in about ten to twenty percent smaller average delays depending on the constraints. With artificially increased separation minima, the difference between MILP and EFCFS became more noticeable. However, the EFCFS was about ten times faster with smaller variations among different scenarios and constraints. The comparison suggests that the MILP-based algorithm has a small advantage at the current traffic level; however, has potential to be more effective in higher demand or severe weather situations. The EFCFS algorithm may be better suited for real-time applications or investigating larger scale scheduling problems.

air traffic optimization↗

NASA NTRS2016

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

airport surface traffic↗

DOE OSTI2022

Route Optimization for Energy Efficient Airport Shuttle Operations - A Case Study from Dallas Fort Worth International Airport

Air travel and requisite surface traffic supporting passenger arrival/departure constitutes a significant portion of travel and emissions in cities with large airports. An airport trip can segment into three parts namely: i) travel from a location in the city to the airport; ii) travel from a parking lot or rental car center to the terminal (i.e., within the airport premises), and iii) travel inside the terminal. Depending on the airport access mode all or a part of these legs comprise a traveler’s journey to the airport. The priority of airport ground transport management teams is to provide passengers with a seamless travel experience within the airport, so it is understandable that within airport shuttle routes might not be optimized for minimizing energy consumption. Solutions that meet the dual objective of reducing energy consumption from airport shuttle operations without compromising on passenger travel experience are key to improving system efficiency. There is currently a dearth of research and tools that can inform airports in making such decisions. Addressing this need, this research effort puts forth an optimization model that generates optimal shuttle routes for a given set of constraints, and a discrete-event simulator that evaluates the optimal solutions in a stochastic environment to understand the tradeoffs between passenger wait times, and within airport shuttle energy consumption. The proposed set of tools are tested in the context of optimizing airport shuttles routes within the Dallas Fort Worth International Airport (DFW). In addition to shuttle spatial positioning, and passenger demand information, high-fidelity vehicle data was collected using data loggers installed on DFW shuttles. Results show that 20% energy reduction in shuttle operations is possible with a modest two-minute increase in average passenger wait times. The tools developed in this research effort are designed to be generalizable and can help optimize shuttle operations planning at any major airport.

air travel↗

NASA NTRS1998

Overview of NASA Advanced Transportation Technologies Program

A General Overview of NASA Advanced Transportation Technologies Program is presented. The contents include: 1) Center-TRACON Automation System (CTAS) which provides automation tools to assist air traffic controllers in planning and controlling air traffic arriving into major airports; 2) Surface Movement Advisor (SMA) for expediting and optimizing aircraft operations on the airport surface; and 3) Terminal Area Productivity Program (TAP), which is aimed at improving airport throughput in instrument meteorological conditions to match that attainable in clear weather.

Ashford, Rose↗

NASA NTRS2013

Spot and Runway Departure Advisor

The Spot and Runway Departure Advisor (SARDA) is a research prototype of a decision support tool for ATC tower controllers to assist in manging and controlling traffic on the surface of an airport. SARDA employs a scheduler to generate an optimal runway schedule and gate push-back - spot release sequence and schedule that improves efficiency of surface operations. The advisories for ATC tower controllers are displayed on an Electronic Flight Strip (EFS) system. The human-in-the-loop simulation of the SARDA tool was conducted for east operations of Dallas-Ft. Worth International Airport (DFW) to evaluate performance of the SARDA tool and human factors, such as situational awareness and workload. The results indicates noticeable taxi delay reduction and fuel savings by using the SARDA tool. Reduction in controller workload were also observed throughout the scenario runs. The future plan includes modeling and simulation of the ramp operations of the Charlotte International Airport, and develop a decision support tool for the ramp controllers.

decision support tool↗

NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. It affects the efficiency of the National Airspace System (NAS) and both surface and airspace operations can benefit from better understanding future runway configurations. In this paper, we present a comprehensive implementation of predictive models for runway configuration estimation from large volumes of historical data. Specifically, operational data from two full years (2018 and 2019) is collected, analyzed, and fused together to build the data product used in this work. The data set differs from prior work in the field in terms of its scope, resolution, and variety of factors collected and considered. Meteorological data is collected from two different sources – current weather conditions from METAR (Meteorological Terminal Aviation Routine Weather Report) and forecast weather conditions from Localized Aviation MOS Program (LAMP). Operational data from the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) related to scheduled and actual number of arrivals and departures, average taxi times, etc. are collected. NASA’s Sherlock Data Warehouse is used to identify critical information such as go-arounds, and other events that might impact RCM decision-making. All data is collected and aggregated over 15-minute intervals throughout the two years. This provides a resolution like the timescales that might be necessary for runway configuration management decision-making. A variety of supervised learning algorithms are tested including Support Vector Machine, Random Forest, Gradient Boosting, etc. including tuning of the model hyperparameters. The modeling process is applied and presented on two representative U.S. airports – Charlotte Douglas International Airport (KCLT) and Denver International Airport (KDEN). The two airports present different levels of complexity in terms of the total number of configurations used and provide a balanced perspective on the generalizability of the developed approach to other airports in the NAS. Initial results are promising (F1 score of 0.91 at KCLT and 0.83 at KDEN) for data in the test set. The final paper will contain a comprehensive comparison between different models and model building strategies as well as further refined results. Most important predictors for each airport will be identified along with a discussion and recommendations on adapting the framework to other scenarios.

Tejas G Puranik↗

NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

air traffic management↗

NASA NTRSDate not supplied

Predicting Airport Runway Configuration for Decision-Support Using Supervised Learning

air traffic management↗

NASA NTRS2014

Overview: Spot and Runway Departure Advisor (SARDA)

Spot and Runway Departure Advisor (SARDA) is a decision support tool to assist airline ramp controllers and ATC tower controllers to manage traffic on the airport surface to significantly improve efficiency and predictability in surface operations. The core function of the tool is the runway scheduler which generates an optimal solution for runway sequence and schedule of departure aircraft, which would minimize system delay and maximize runway throughput. The presentation describes the concept of the SARDA tool and results from human-in-the-loop simulations conducted in 2012 for DallasFt. Worth International Airport. The presentation also explains the latest status of NASA's current surface research through a collaboration with an airline partner. The presentation concludes with a discussion on other on-going as well as future surface research.

decision support tool↗

NASA NTRS2009

Initial Concept for Terminal Area Conflict Detection, Alerting, and Resolution Capability on or Near the Airport Surface

The Next Generation Air Transportation System (NextGen) concept for 2025 envisions the movement of large numbers of people and goods in a safe, efficient, and reliable manner. The NextGen will remove many of the constraints in the current air transportation system, support a wider range of operations, and deliver an overall system capacity up to 3 times that of current operating levels. In order to achieve the NextGen vision, research is necessary in the areas of surface traffic optimization, maximum runway capacity, reduced runway occupancy time, simultaneous single runway operations, and terminal area conflict prevention, among others. The National Aeronautics and Space Administration (NASA) is conducting Collision Avoidance for Airport Traffic (CAAT) research to develop technologies, data, and guidelines to enable Conflict Detection and Resolution (CD&R) in the Airport Terminal Maneuvering Area (ATMA) under current and emerging NextGen operating concepts. In this report, an initial concept for an aircraft-based method for CD&R in the ATMA is presented. This method is based upon previous NASA work in CD&R for runway incursion prevention, the Runway Incursion Prevention System (RIPS). CAAT research is conducted jointly under NASA's Airspace Systems Program, Airportal Project and the Aviation Safety Program, Integrated Intelligent Flight Deck Project.

Green, David F.↗

NASA NTRS2015

Spot and Departure Runway Advisor (SARDA)

Spot and Runway Departure Advisor (SARDA) is a decision support tool to assist airline ramp controllers and ATC tower controllers to manage traffic on the airport surface to significantly improve efficiency and predictability in surface operations. The core function of the tool is the runway scheduler which generates an optimal solution for runway sequence and schedule of departure aircraft, which would minimize system delay and maximize runway throughput. The presentation describes the concept of the SARDA tool and results from human-in-the-loop simulations conducted in 2012 for Dallas-Ft. Worth International Airport. The presentation also discusses the latest status of NASA's current surface research through a collaboration with an airline partner, where a tool is developed for airline ramp operators to assist departure pushback operations.

Departure Schedule↗

NASA NTRS2013

Initial Concept for Terminal Area Conflict Detection, Alerting, and Resolution Capability On or Near the Airport Surface, Version 2.0

The Next Generation Air Transportation System (NextGen) concept for 2025 envisions the movement of large numbers of people and goods in a safe, efficient, and reliable manner. The NextGen will remove many of the constraints in the current air transportation system, support a wider range of operations, and deliver an overall system capacity up to 3 times that of current operating levels. In order to achieve the NextGen vision, research is necessary in the areas of surface traffic optimization, maximum runway capacity, reduced runway occupancy time, simultaneous single runway operations, and terminal area conflict prevention, among others. The National Aeronautics and Space Administration (NASA) is conducting Collision Avoidance for Airport Traffic (CAAT) research to develop technologies, data, and guidelines to enable Conflict Detection and Resolution (CD&R) in the Airport Terminal Maneuvering Area (ATMA) under current and emerging NextGen operating concepts. The term ATMA was created to reflect the fact that the CD&R concept area of operation is focused near the airport within the terminal maneuvering area. In the following, an initial concept for an aircraft-based method for CD&R in the ATMA is presented. This method is based upon previous NASA work in CD&R for runway incursion prevention, the Runway Incursion Prevention System (RIPS).

Otero, Sharon D.↗

NASA NTRS2017

TRACC_PB SOSS Integrated Traffic Simulation for CLT Ramp Operation

This presentation provides the current task under the NASA-DLR research collaboration for airport surface. It presents the effort done to adapt TRACC and SOSS software components to simulate airport (CLT) ramp area traffic management using TRACC's conflict free taxi trajectory optimization and SOSS's fast time simulation platform.

surface operations↗

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↗