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NASA NTRS1995

Evaluation of Winter Operational Runway Friction Measurement Equipment, Procedures, and Research

This document produced by the FAA/Industry Winter Runway Friction Measurement and Reporting Working Group, is designed to provide an overview of current information on the present guidance, practices, and procedures for reporting runway pavement surface conditions during winter operations at airports. It contains recommendations on the desirability of providing the best procedural consistency and standardization and discusses the available means to implement the guidance that will result in improved aviation safety at airports during hazardous winter conditions.

Source record↗

NASA NTRS2003

Runway Operations Planning: A Two-Stage Solution Methodology

The airport runway is a scarce resource that must be shared by different runway operations (arrivals, departures and runway crossings). Given the possible sequences of runway events, careful Runway Operations Planning (ROP) is required if runway utilization is to be maximized. Thus, Runway Operations Planning (ROP) is a critical component of airport operations planning in general and surface operations planning in particular. From the perspective of departures, ROP solutions are aircraft departure schedules developed by optimally allocating runway time for departures given the time required for arrivals and crossings. In addition to the obvious objective of maximizing throughput, other objectives, such as guaranteeing fairness and minimizing environmental impact, may be incorporated into the ROP solution subject to constraints introduced by Air Traffic Control (ATC) procedures. Generating optimal runway operations plans was approached in with a 'one-stage' optimization routine that considered all the desired objectives and constraints, and the characteristics of each aircraft (weight class, destination, Air Traffic Control (ATC) constraints) at the same time. Since, however, at any given point in time, there is less uncertainty in the predicted demand for departure resources in terms of weight class than in terms of specific aircraft, the ROP problem can be parsed into two stages. In the context of the Departure Planner (OP) research project, this paper introduces Runway Operations Planning (ROP) as part of the wider Surface Operations Optimization (SOO) and describes a proposed 'two stage' heuristic algorithm for solving the Runway Operations Planning (ROP) problem. Focus is specifically given on including runway crossings in the planning process of runway operations. In the first stage, sequences of departure class slots and runwy crossings slots are generated and ranked based on departure runway throughput under stochastic conditions. In the second stage, the departure class slots are populated with specific flights from the pool of available aircraft, by solving an integer program. Preliminary results from the algorithm implementation on real-world traffic data are included.

Anagnostakis, Ioannis↗

NASA NTRS2016

ICN Deicing Operations Analysis

Aircraft deicing and anti-icing on airport ground in winter weather conditions is important to ensure safe departure takeoff and enroute flying. At the same time, it imposes additional workload to controllers and airport operators, and causes delays to airlines and passengers.This presentation shows the initial results of analyzing the deicing related operations at Incheon International Airport (ICN). It describes the deicing operations concept, procedures, and deicing making processes at ICN. It also looks into the three-month ICN operation data from Dec 2015 to Feb 2016 to identify the characteristics of the current day deicing operation.This work is under the joint collaboration between Korea Aerospace Research Institute and NASA. The objective is to develop a deicing operation simulation capability in NASA's fast time simulation environment to study and improve deicing operations efficiency.

Airport Surface ATM↗

NASA NTRS2019

Field Evaluation of the Baseline Integrated Arrival, Departure, and Surface Capabilities at Charlotte Douglas International Airport

NASA is currently developing a suite of decision support capabilities for integrated arrival, departure, and surface (IADS) operations in a metroplex environment. The effort is being made in three phases, under NASA's Airspace Technology Demonstration 2 (ATD-2) sub-project, through a strong partnership with the Federal Aviation Administration (FAA), air carriers, airport, and general aviation community. The Phase 1 Baseline IADS capabilities provide enhanced operational efficiency and predictability of flight operations through data exchange and integration, tactical surface metering, and automated coordination of release time of controlled flights for overhead stream insertion. The users of the IADS system include the personnel at the Charlotte Douglas International Airport (CLT) air traffic control tower, American Airlines ramp tower, CLT terminal radar approach control (TRACON), and Washington Center. This paper describes the Phase 1 Baseline IADS capabilities and field evaluation conducted at CLT from September 2017 for a year. From the analysis of operations data, it is estimated that 538,915 kilograms of fuel savings, and 1,659 metric tons of CO2 emission reduction were achieved during the period with a total of 944 hours of engine run time reduction. The amount of CO2 savings is estimated as equivalent to planting 42,560 urban trees. The results have also shown that the surface metering had no negative impact on on-time arrival performance of both outbound and inbound flights. The technology transfer of Phase 1 Baseline IADS capabilities has been made to the FAA and aviation industry, and the development of additional capabilities for the subsequent phases is underway.

Jung, Yoon C.↗

NASA NTRS2019

Field Evaluation of the Baseline Integrated Arrival, Departure, and Surface (IADS) Capabilities at Charlotte Douglas International Airport (CLT)

and Surface (IADS)↗

NASA NTRS2006

Airport Surface Network Architecture Definition

Currently, airport surface communications are fragmented across multiple types of systems. These communication systems for airport operations at most airports today are based dedicated and separate architectures that cannot support system-wide interoperability and information sharing. The requirements placed upon the Communications, Navigation, and Surveillance (CNS) systems in airports are rapidly growing and integration is urgently needed if the future vision of the National Airspace System (NAS) and the Next Generation Air Transportation System (NGATS) 2025 concept are to be realized. To address this and other problems such as airport surface congestion, the Space Based Technologies Project s Surface ICNS Network Architecture team at NASA Glenn Research Center has assessed airport surface communications requirements, analyzed existing and future surface applications, and defined a set of architecture functions that will help design a scalable, reliable and flexible surface network architecture to meet the current and future needs of airport operations. This paper describes the systems approach or methodology to networking that was employed to assess airport surface communications requirements, analyze applications, and to define the surface network architecture functions as the building blocks or components of the network. The systems approach used for defining these functions is relatively new to networking. It is viewing the surface network, along with its environment (everything that the surface network interacts with or impacts), as a system. Associated with this system are sets of services that are offered by the network to the rest of the system. Therefore, the surface network is considered as part of the larger system (such as the NAS), with interactions and dependencies between the surface network and its users, applications, and devices. The surface network architecture includes components such as addressing/routing, network management, network performance and security.

Nguyen, Thanh C.↗

NASA NTRS2016

Building Airport Surface HITL Simulation Capability

FutureFlight Central is a high fidelity, real-time simulator designed to study surface operations and automation. As an air traffic control tower simulator, FFC allows stakeholders such as the FAA, controllers, pilots, airports, and airlines to develop and test advanced surface and terminal area concepts and automation including NextGen and beyond automation concepts and tools. These technologies will improve the safety, capacity and environmental issues facing the National Airspace system. FFC also has extensive video streaming capabilities, which combined with the 3-D database capability makes the facility ideal for any research needing an immersive virtual and or video environment. FutureFlight Central allows human in the loop testing which accommodates human interactions and errors giving a more complete picture than fast time simulations. This presentation describes FFCs capabilities and the components necessary to build an airport surface human in the loop simulation capability.

facility capabilities↗

NASA NTRS2018

Impact of Data Exchange Provided by ATD2 Tools at Charlotte-Douglas International Airport

Data Exchange and Integration is necessary for progress towards an Integrated Arrival, Departure, and Surface (IADS) traffic management capability. In collaboration with the FAA, NASA has introduced new data exchange elements to Charlotte-Douglas International Airport air traffic facilities, including the American Airlines ramp, as part of the Airspace Technology Demonstration 2 (ATD2). This paper describes the new tools that deliver these elements, and the human factors impact of the tools as measured by post-bank surveys. Workload was unaffected by ATD2 tool use, and situational awareness was improved in the Tower and with Ramp controllers in the second round of surveys. Respondents described their tools as more helpful if they included ATD2 tools 1) in the Tower for insuring compliance for aircraft under a Traffic Management Initiative, 2) in the TRACON when actively used for many TRACON tasks, and 3) in the Ramp in the second round of surveys.

and surface traffic management; human factors asse↗

NASA NTRS2015

System-Oriented Runway Management Concept of Operations

This document describes a concept for runway management that maximizes the overall efficiency of arrival and departure operations at an airport or group of airports. Specifically, by planning airport runway configurations/usage, it focuses on the efficiency with which arrival flights reach their parking gates from their arrival fixes and departure flights exit the terminal airspace from their parking gates. In the future, the concept could be expanded to include the management of other limited airport resources. While most easily described in the context of a single airport, the concept applies equally well to a group of airports that comprise a metroplex (i.e., airports in close proximity that share resources such that operations at the airports are at least partially dependent) by including the coordination of runway usage decisions between the airports. In fact, the potential benefit of the concept is expected to be larger in future metroplex environments due to the increasing need to coordinate the operations at proximate airports to more efficiently share limited airspace resources. This concept, called System-Oriented Runway Management (SORM), is further broken down into a set of airport traffic management functions that share the principle that operational performance must be measured over the complete surface and airborne trajectories of the airport's arrivals and departures. The "system-oriented" term derives from the belief that the traffic management objective must consider the efficiency of operations over a wide range of aircraft movements and National Airspace System (NAS) dynamics. The SORM concept is comprised of three primary elements: strategic airport capacity planning, airport configuration management, and combined arrival/departure runway planning. Some aspects of the SORM concept, such as using airport configuration management1 as a mechanism for improving aircraft efficiency, are novel. Other elements (e.g., runway scheduling, which is a part of combined arrival/departure runway scheduling) have been well studied, but are included in the concept for completeness and to allow the concept to define the necessary relationship among the elements. The goal of this document is to describe the overall SORM concept and how it would apply both within the NAS and potential future Next Generation Air Traffic System (NextGen) environments, including research conducted to date. Note that the concept is based on the belief that runways are the primary constraint and the decision point for controlling efficiency, but the efficiency of runway management must be measured over a wide range of space and time. Implementation of the SORM concept is envisioned through a collection of complementary, necessary capabilities collectively focused on ensuring efficient arrival and departure traffic management, where that efficiency is measured not only in terms of runway efficiency but in terms of the overall trajectories between parking gates and transition fixes. For the more original elements of the concept-airport configuration management-this document proposes specific air traffic management (ATM) decision-support automation for realizing the concept.

Lohr, Gary W.↗

NASA NTRS2016

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 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. 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 and 2014 for Charlotte airport ramp tower.

surface management↗

NASA NTRS2017

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

This presentation describes the overview of the ATD-2 project and the integrated simulation of surface and airspace to evaluate the procedures of IADS system and evaluate surface metering capabilities via a high-fidelity human-in-the-loop simulation. Two HITL facilities, Future Flight Central (FFC) and Airspace Operations Laboratory (AOL), are integrated for simulating surface operations of the Charlotte-Douglas International Airport (CLT) and airspace in CLT TRACON and Washington Center.

ATD-2↗

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 NTRS2016

AAtS over AeroMACS Technology Trials on the Airport Surface

Air-Ground component of SWIM; Enables enhanced two-way information exchanges between flight operators, aircrew, and ATSP (TFM); Used in all flight domains including pre-departure and post-arrival; Aircrew active in CDM; For strategic planning, advisory information; Not for command control (data voice) Wireless communications system for airport surface; Family member of Mobile WiMAX: (IEEE802.16e), Band 5091-5150 MHz, Bandwidth 5 MHz - TDDOFDMA - Adaptive Modulation and Coding - Quality of Service (QoS)

Wireless Communications↗

NASA NTRS2019

Alternatives for Scheduling Departures for Efficient Surface Metering in ATD-2: Exploration in a Human-in-the-Loop Simulation

Human-in-the-Loop (HITL) simulation was conducted to explore the impacts of various surface metering goals on operations and Ramp Controllers at Charlotte Douglas International Airport (CLT). Three conditions were compared: Baseline, with no surface metering, instructions to meet advisory times at the gate only, and instructions to meet advisory times at the gate as well as the times at the scheduled taxiway spot, where aircraft are delivered to Air Traffic Control (ATC). Results showed increased compliance for taxiway spot times when compliance was first met for gate advisories. Instructing Ramp Controllers to meet advisory times at the gate improves spot time compliance and therefore surface scheduling predictability at CLT. Results also demonstrated there was increased compliance overall with gate and spot times in the second condition. This was likely due to higher Ramp Controller workload in the third condition.

Airport surface scheduling↗

NASA NTRS2016

Airport Traffic Conflict Detection and Resolution Algorithm Evaluation

Two conflict detection and resolution (CD&R) algorithms for the terminal maneuvering area (TMA) were evaluated in a fast-time batch simulation study at the National Aeronautics and Space Administration (NASA) Langley Research Center. One CD&R algorithm, developed at NASA, was designed to enhance surface situation awareness and provide cockpit alerts of potential conflicts during runway, taxi, and low altitude air-to-air operations. The second algorithm, Enhanced Traffic Situation Awareness on the Airport Surface with Indications and Alerts (SURF IA), was designed to increase flight crew awareness of the runway environment and facilitate an appropriate and timely response to potential conflict situations. The purpose of the study was to evaluate the performance of the aircraft-based CD&R algorithms during various runway, taxiway, and low altitude scenarios, multiple levels of CD&R system equipage, and various levels of horizontal position accuracy. Algorithm performance was assessed through various metrics including the collision rate, nuisance and missed alert rate, and alert toggling rate. The data suggests that, in general, alert toggling, nuisance and missed alerts, and unnecessary maneuvering occurred more frequently as the position accuracy was reduced. Collision avoidance was more effective when all of the aircraft were equipped with CD&R and maneuvered to avoid a collision after an alert was issued. In order to reduce the number of unwanted (nuisance) alerts when taxiing across a runway, a buffer is needed between the hold line and the alerting zone so alerts are not generated when an aircraft is behind the hold line. All of the results support RTCA horizontal position accuracy requirements for performing a CD&R function to reduce the likelihood and severity of runway incursions and collisions.

Jones, Denise R.↗

NASA NTRS2001

Modifying and Testing ATC Controller Interface (CI) for Data Link Clearances

The Controller-Pilot Data Link Communications (CPDLC) and Air Traffic Control workstation research was conducted as part of the 1997 NASA Low Visibility Landing and Surface Operations (LVLASO) demonstration program at Atlanta Hartsfield airport. Research activity under this grant increased the sophistication of the Controllers' Communication and Situational Awareness Terminal (C-CAST) and developed a VHF Data Link -Mode 2 communications platform. The research culminated with participation in the 2000 NASA Aviation Safety Program's Synthetic Vision System (SVS) / Runway Incursion Prevention System (RIPS) flight demonstration at Dallas-Fort Worth Airport.

Source record↗

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 NTRS1999

Development of Airport Surface Required Navigation Performance (RNP)

The U.S. and international aviation communities have adopted the Required Navigation Performance (RNP) process for defining aircraft performance when operating the en-route, approach and landing phases of flight. RNP consists primarily of the following key parameters - accuracy, integrity, continuity, and availability. The processes and analytical techniques employed to define en-route, approach and landing RNP have been applied in the development of RNP for the airport surface. To validate the proposed RNP requirements several methods were used. Operational and flight demonstration data were analyzed for conformance with proposed requirements, as were several aircraft flight simulation studies. The pilot failure risk component was analyzed through several hypothetical scenarios. Additional simulator studies are recommended to better quantify crew reactions to failures as well as additional simulator and field testing to validate achieved accuracy performance, This research was performed in support of the NASA Low Visibility Landing and Surface Operations Programs.

Cassell, Rick↗