Results for “traffic prediction” — Page 8

NASA NTRS1997

Computational Models of Human Performance: Validation of Memory and Procedural Representation in Advanced Air/Ground Simulation

The Man-Machine Interaction Design and Analysis System (MIDAS) under joint U.S. Army and NASA cooperative is intended to assist designers of complex human/automation systems in successfully incorporating human performance capabilities and limitations into decision and action support systems. MIDAS is a computational representation of multiple human operators, selected perceptual, cognitive, and physical functions of those operators, and the physical/functional representation of the equipment with which they operate. MIDAS has been used as an integrated predictive framework for the investigation of human/machine systems, particularly in situations with high demands on the operators. We have extended the human performance models to include representation of both human operators and intelligent aiding systems in flight management, and air traffic service. The focus of this development is to predict human performance in response to aiding system developed to identify aircraft conflict and to assist in the shared authority for resolution. The demands of this application requires representation of many intelligent agents sharing world-models, coordinating action/intention, and cooperative scheduling of goals and action in an somewhat unpredictable world of operations. In recent applications to airborne systems development, MIDAS has demonstrated an ability to predict flight crew decision-making and procedural behavior when interacting with automated flight management systems and Air Traffic Control. In this paper, we describe two enhancements to MIDAS. The first involves the addition of working memory in the form of an articulatory buffer for verbal communication protocols and a visuo-spatial buffer for communications via digital datalink. The second enhancement is a representation of multiple operators working as a team. This enhanced model was used to predict the performance of human flight crews and their level of compliance with commercial aviation communication procedures. We show how the data produced by MIDAS compares with flight crew performance data from full mission simulations. Finally, we discuss the use of these features to study communication issues connected with aircraft-based separation assurance.

Corker, Kevin M.↗

NASA NTRS2005

Assessment of Mars Exploration Rover landing site predictions.

Comprehensive analyses of remote sensing data during the three-year effort to select the Mars Exploration Rover landing sites at Gusev crater and at Meridiani Planum correctly predicted the atmospheric density profile during entry and descent and the safe and trafficable surfaces explored by the two rovers. The Gusev crater site was correctly predicted to be a low-relief surface that was less rocky than the Viking landing sites but comparably dusty. A dark, lowalbedo, flat plain composed of basaltic sand and haematite with very few rocks was expected and found at Meridiani Planum. These results argue that future efforts to select safe landing sites based on existing and acquired remote sensing data will be successful. In contrast, geological interpretations of the sites based on remote sensing data were less certain and less successful, which emphasizes the inherent ambiguities in understanding surface geology from remotely sensed data and the uncertainty in predicting exactly what materials will be available for study at a landing site.

Zurek, R. W.↗

NASA NTRS2020

Integrated Demand Management: Concepts and Procedures

This report provides a comprehensive description of the Integrated Demand Management concept. Motivation: NASA’s Integrated Demand Management (IDM) research explores the idea that, under certain conditions, time-based flow management (TBFM) arrival operations can benefit from the coordinated use of a strategic traffic management initiative (TMI) to “precondition” the inbound demand. The research was motivated by the observation that TBFM was usually turned off during convective weather, even in facilities where it was routinely used. Our hypothesis was that strategic adjustments to the inbound traffic so that it provided a better match to the off-nominal changes in capacity observed during these conditions could enable TBFM scheduling to continue to provide effective support for arrival traffic management. Concept: IDM proposes that a TMI (e.g., a Collaborative Trajectory Options Program, or CTOP) be used to adjust the rate and/or geographic distribution across flows of the traffic inbound to a high-demand, TBFM-managed airport before that traffic reaches the TBFM planning horizon. After this strategic preconditioning, TBFM can then tactically fine-tune the demand to deliver a well-managed, orderly feed to the destination airport. Coordinated use of these two flow management capabilities is intended to improve system performance in terms of: • Equity of ground delay assignment, avoiding excessive ground delay for TBFM-scheduled departures, without penalizing longer flights; • Throughput, by distributing traffic to maximize use of available capacity; • Predictability for operators, providing advance notice about the impact on individual flights; • Increased flexibility, supporting operator mitigation strategies such as slot swapping or trajectory options; • Efficiency of flight operations, using ground delay more effectively and reducing airborne delay. The operational description in this document highlights how the IDM concept builds upon already existing tools and procedures, and also indicates where tool enhancements could facilitate conduct of IDM operations. However, enhanced tools are not a requirement for concept introduction. In fact, initial deployment that focused on training procedures and rationale for coordinated use of TFMS and TBFM, without changes to existing tools, might be a simpler way to introduce and to familiarize traffic managers with the idea of preconditioning. The concept and procedures described in this document can hopefully provide useful guidance for introduction of IDM into field operations..

IDM↗

NASA NTRS2018

Using an Automated Air Traffic Simulation Capability for a Parametric Study in Traffic Flow Management

Flight delays occur when demand for capacity-constrained airspace or airports exceeds predicted capacity. Demand for capacity-constrained airspace or airports can be controlled by a series of Traffic Management Initiatives (TMIs), which use departure and airborne delays, as well as pre-departure and airborne reroutes, to manage access to the constrained resources. Two systems exist in current and planned future operations to address imbalances between demand and capacity. The Collaborative Trajectory Options Program (CTOP) reduces demand to constrained resources by assigning strategic departure delay and pre-departure reroutes. Reroutes are selected from Trajectory Options Sets (TOSs) submitted by airlines. As flights approach the constrained resource, the Time-Based Flow Management System (TBFM) is used to assign tactical delay to satisfy constraints. This paper describes experiments performed to study the impact of varying levels of airline participation in CTOP via submission of TOSs on ground delay and flight time, and the impact of departure uncertainty on TBFM delays. Results suggest that as CTOP participation increases, average ground delays decrease for all airlines, but to the greatest extent for airlines participating in CTOP. A threshold in CTOP participation, which varies with the constraint capacity, is identified beyond which there is relatively little further reduction in average ground delays. Similarly, given the likely level of CTOP participation, the capacity reduction for which CTOP would be an appropriate TMI is also identified. Results also suggest that high average departure errors and high variability in departure error can make the prioritization of TBFM internal departures in TBFM metering and scheduling infeasible. Departure errors at current levels are, however, acceptable.

CTOP↗

NASA NTRS2018

Using an Automated Air Traffic Simulation Capability for a Parametric Study in Traffic Flow Management

Flight delays occur when demand for capacity-constrained airspace or airports exceeds predicted capacity. Demand for capacity-constrained airspace or airports can be controlled by a series of Traffic Management Initiatives (TMIs), which use departure and airborne delays, as well as pre-departure and airborne reroutes, to manage access to the constrained resources. Two systems exist in current and planned future operations to address imbalances between demand and capacity. The Collaborative Trajectory Options Program (CTOP) reduces demand to constrained resources by assigning strategic departure delay and pre-departure reroutes. Reroutes are selected from Trajectory Options Sets (TOSs) submitted by airlines. As flights approach the constrained resource, the Time-Based Flow Management System (TBFM) is used to assign tactical delay to satisfy constraints. This paper describes experiments performed to study the impact of varying levels of airline participation in CTOP via submission of TOSs on ground delay and flight time, and the impact of departure uncertainty on TBFM delays. Results suggest that as CTOP participation increases, average ground delays decrease for all airlines, but to the greatest extent for airlines participating in CTOP. A threshold in CTOP participation, which varies with the constraint capacity, is identified beyond which there is relatively little further reduction in average ground delays. Similarly, given the likely level of CTOP participation, the capacity reduction for which CTOP would be an appropriate TMI is also identified. Results also suggest that high average departure errors and high variability in departure error can make the prioritization of TBFM internal departures in TBFM metering and scheduling infeasible. Departure errors at current levels are, however, acceptable.

integrated demand management↗

NASA NTRS2013

A Turn-Projected State-Based Conflict Resolution Algorithm

State-based conflict detection and resolution (CD&R) algorithms detect conflicts and resolve them on the basis on current state information without the use of additional intent information from aircraft flight plans. Therefore, the prediction of the trajectory of aircraft is based solely upon the position and velocity vectors of the traffic aircraft. Most CD&R algorithms project the traffic state using only the current state vectors. However, the past state vectors can be used to make a better prediction of the future trajectory of the traffic aircraft. This paper explores the idea of using past state vectors to detect traffic turns and resolve conflicts caused by these turns using a non-linear projection of the traffic state. A new algorithm based on this idea is presented and validated using a fast-time simulator developed for this study.

Butler, Ricky W.↗

NASA NTRS1989

Controller evaluations of the descent advisor automation aid

An automation aid to assist air traffic controllers in efficiently spacing traffic and meeting arrival times at a fix has been developed at NASA Ames Research Center. The automation aid, referred to as the descent advisor (DA), is based on accurate models of aircraft performance and weather conditions. The DA generates suggested clearances, including both top-of-descent point and speed profile data, for one or more aircraft in order to achieve specific time or distance separation objectives. The DA algorithm is interfaced with a mouse-based, menu-driven controller display that allows the air traffic controller to interactively use its accurate predictive capability to resolve conflicts and issue advisories to arrival aircraft. This paper focuses on operational issues concerning the utilization of the DA, specifically, how the DA can be used for prediction, intrail spacing, and metering. In order to evaluate the DA, a real time simulation was conducted using both current and retired controller subjects. Controllers operated in teams of two, as they do in the present environment; issues of training and team interaction will be discussed. Evaluations by controllers indicated considerable enthusiasm for the DA aid, and provided specific recommendations for using the tool effectively.

Tobias, Leonard↗

NASA NTRS1989

Controller evaluations of the descent advisor automation aid

An automation aid to assist air traffic controllers in efficiently spacing traffic and meeting arrival times at a fix has been developed at NASA Ames Research Center. The automation aid, referred to as the descent advisor (DA), is based on accurate models of aircraft performance and weather conditions. The DA generates suggested clearances, including both top-of-descent point and speed profile data, for one or more aircraft in order to achieve specific time or distance separation objectives. The DA algorithm is interfaced with a mouse-based, menu-driven controller display that allows the air traffic controller to interactively use its accurate predictive capability to resolve conflicts and issue advisories to arrival aircraft. This paper focuses on operational issues concerning the utilization of the DA, specifically, how the DA can be used for prediction, intrail spacing, and metering. In order to evaluate the DA, a real time simulation was conducted using both current and retired controller subjects. Controllers operated in teams of two, as they do in the present environment; issues of training and team interaction will be discussed. Evaluations by controllers indicated considerable enthusiasm for the DA aid, and provided specific recommendations for using the tool effectively.

Tobias, Leonard↗

NASA NTRS1995

Descent advisor preliminary field test

A field test of the Descent Advisor (DA) automation tool was conducted at the Denver Air Route Traffic Control Center in September 1994. DA is being developed to assist Center controllers in the efficient management and control of arrival traffic. DA generates advisories, based on trajectory predictions, to achieve accurate meter-fix arrival times in a fuel efficient manner while assisting the controller with the prediction and resolution of potential conflicts. The test objectives were to evaluate the accuracy of DA trajectory predictions for conventional- and flight-management-system-equipped jet transports, to identify significant sources of trajectory prediction error, and to investigate procedural and training issues (both air and ground) associated with DA operations. Various commercial aircraft (97 flights total) and a Boeing 737-100 research aircraft participated in the test. Preliminary results from the primary test set of 24 commercial flights indicate a mean DA arrival time prediction error of 2.4 sec late with a standard deviation of 13.1 sec. This paper describes the field test and presents preliminary results for the commercial flights.

Green, Steven M.↗

NASA NTRS1995

Descent Advisor Preliminary Field Test

A field test of the Descent Advisor (DA) automation tool was conducted at the Denver Air Route Traffic Control Center in September 1994. DA is being developed to assist Center controllers in the efficient management and control of arrival traffic. DA generates advisories, based on trajectory predictions, to achieve accurate meter-fix arrival times in a fuel efficient manner while assisting the controller with the prediction and resolution of potential conflicts. The test objectives were: (1) to evaluate the accuracy of DA trajectory predictions for conventional and flight-management system equipped jet transports, (2) to identify significant sources of trajectory prediction error, and (3) to investigate procedural and training issues (both air and ground) associated with DA operations. Various commercial aircraft (97 flights total) and a Boeing 737-100 research aircraft participated in the test. Preliminary results from the primary test set of 24 commercial flights indicate a mean DA arrival time prediction error of 2.4 seconds late with a standard deviation of 13.1 seconds. This paper describes the field test and presents preliminary results for the commercial flights.

Green, Steven M.↗

NASA NTRS2001

Real-Time Surface Traffic Adviser

A real-time data management system which uses data generated at different rates by multiple heterogeneous incompatible data sources are presented. In one embodiment, the invention is as an airport surface traffic data management system (traffic adviser) that electronically interconnects air traffic control, airline, and airport operations user communities to facilitate information sharing and improve taxi queuing. The system uses an expert system to fuse dam from a variety of airline, airport operations, ramp control, and air traffic control sources, in order to establish, predict, and update reference data values for every aircraft surface operation.

Brian J Glass↗

NASA NTRS2005

Apex Reference Manual 3.0 Beta

Apex is a toolkit for constructing software that behaves intelligently and responsively in demanding task environments. Reflecting its origin at NASA where Apex continues to be developed, current applications include: a) Providing autonomous mission management and tactical control capabilities for unmanned aerial vehicles including an autonomous surveillance helicopter and a simulation prototype of an unmanned fixed-wing aircraft to be used for wildfire mapping; b) Simulating human air traffic controllers, pilots and astronauts to help predict how people might respond to changes in equipment or procedures; and c) Predicting the precise duration and sequence of routine human behaviors based on a human-computer interaction engineering technique called CPM-GOMS. Among Apex s components are a set of implemented reasoning services, such as those for reactive planning and temporal pattern recognition; a software architecture that embeds and integrates these services and allows additional reasoning elements to be added as extensions; a formal language for specifying agent knowledge; a simulation environment to facilitate prototyping and analysis; and Sherpa, a set of tools for visualizing autonomy logic and runtime behavior. In combination, these are meant to provide a flexible and usable framework for creating, testing, and deploying intelligent agent software. Overall, our goal in developing Apex is to lower economic barriers to developing intelligent software agents. New ideas about how to extend or modify the system are evaluated in terms of their impact in reducing the time, expertise, and inventiveness required to build and maintain applications. For example, potential enhancements to the AI reasoning capabilities in the system are reviewed not only for usefulness and distinctiveness, but also for their impact on the readability and general usability of Apex s behavior representation language (PDL) and on the transparency of resulting behavior. A second central part of our approach is to iteratively refine Apex based on lessons learned from as diverse a set of applications as possible. Many applications have been developed by users outside the core development team including engineers, researchers, and students. Usability is thus a central concern for every aspect of Apex visible to a user, including PDL, Sherpa, the Apex installation process, APIs, and user documentation. Apex users vary in their areas of expertise and in their familiarity with autonomy technology. Focusing on usability, a development philosophy summarized by the project motto "Usable Autonomy," has been important part of enabling diverse users to employ Apex successfully and to provide feedback needed to guide iterative, user-centered refinement.

Freed, Michael A.↗

NASA NTRS2010

Tropospheric Airborne Meteorological Data Reporting (TAMDAR) Sensor Validation and Verification on National Oceanographic and Atmospheric Administration (NOAA) Lockheed WP-3D Aircraft

As part of the National Aeronautics and Space Administration's Aviation Safety and Security Program, the Tropospheric Airborne Meteorological Data Reporting project (TAMDAR) developed a low-cost sensor for aircraft flying in the lower troposphere. This activity was a joint effort with support from Federal Aviation Administration, National Oceanic and Atmospheric Administration, and industry. This paper reports the TAMDAR sensor performance validation and verification, as flown on board NOAA Lockheed WP-3D aircraft. These flight tests were conducted to assess the performance of the TAMDAR sensor for measurements of temperature, relative humidity, and wind parameters. The ultimate goal was to develop a small low-cost sensor, collect useful meteorological data, downlink the data in near real time, and use the data to improve weather forecasts. The envisioned system will initially be used on regional and package carrier aircraft. The ultimate users of the data are National Centers for Environmental Prediction forecast modelers. Other users include air traffic controllers, flight service stations, and airline weather centers. NASA worked with an industry partner to develop the sensor. Prototype sensors were subjected to numerous tests in ground and flight facilities. As a result of these earlier tests, many design improvements were made to the sensor. The results of tests on a final version of the sensor are the subject of this report. The sensor is capable of measuring temperature, relative humidity, pressure, and icing. It can compute pressure altitude, indicated air speed, true air speed, ice presence, wind speed and direction, and eddy dissipation rate. Summary results from the flight test are presented along with corroborative data from aircraft instruments.

Tsoucalas, George↗

NASA NTRS2014

Analysis and Prediction of Weather Impacted Ground Stop Operations

When the air traffic demand is expected to exceed the available airport's capacity for a short period of time, Ground Stop (GS) operations are implemented by Federal Aviation Administration (FAA) Traffic Flow Management (TFM). The GS requires departing aircraft meeting specific criteria to remain on the ground to achieve reduced demands at the constrained destination airport until the end of the GS. This paper provides a high-level overview of the statistical distributions as well as causal factors for the GSs at the major airports in the United States. The GS's character, the weather impact on GSs, GS variations with delays, and the interaction between GSs and Ground Delay Programs (GDPs) at Newark Liberty International Airport (EWR) are investigated. The machine learning methods are used to generate classification models that map the historical airport weather forecast, schedule traffic, and other airport conditions to implemented GS/GDP operations and the models are evaluated using the cross-validations. This modeling approach produced promising results as it yielded an 85% overall classification accuracy to distinguish the implemented GS days from the normal days without GS and GDP operations and a 71% accuracy to differentiate the GS and GDP implemented days from the GDP only days.

Analysis↗

NASA NTRS2016

Probabilistic Modeling of Aircraft Trajectories for Dynamic Separation Volumes

With a proliferation of new and unconventional vehicles and operations expected in the future, the ab initio airspace design will require new approaches to trajectory prediction for separation assurance and other air traffic management functions. This paper presents an approach to probabilistic modeling of the trajectory of an aircraft when its intent is unknown. The approach uses a set of feature functions to constrain a maximum entropy probability distribution based on a set of observed aircraft trajectories. This model can be used to sample new aircraft trajectories to form an ensemble reflecting the variability in an aircraft's intent. The model learning process ensures that the variability in this ensemble reflects the behavior observed in the original data set. Computational examples are presented.

Lewis, Timothy A.↗

NASA NTRS2017

Point-Mass Aircraft Trajectory Prediction Using a Hierarchical, Highly-Adaptable Software Design

A highly adaptable and extensible method for predicting four-dimensional trajectories of civil aircraft has been developed. This method, Behavior-Based Trajectory Prediction, is based on taxonomic concepts developed for the description and comparison of trajectory prediction software. A hierarchical approach to the "behavioral" layer of a point-mass model of aircraft flight, a clear separation between the "behavioral" and "mathematical" layers of the model, and an abstraction of the methods of integrating differential equations in the "mathematical" layer have been demonstrated to support aircraft models of different types (in particular, turbojet vs. turboprop aircraft) using performance models at different levels of detail and in different formats, and promise to be easily extensible to other aircraft types and sources of data. The resulting trajectories predict location, altitude, lateral and vertical speeds, and fuel consumption along the flight path of the subject aircraft accurately and quickly, accounting for local conditions of wind and outside air temperature. The Behavior-Based Trajectory Prediction concept was implemented in NASA's Traffic Aware Planner (TAP) flight-optimizing cockpit software application.

Karr, David A.↗

NASA NTRS1993

A satellite-borne radar wind sensor (RAWS)

Modeling global atmospheric circulations and forecasting the weather would improve if worldwide information on winds aloft were available. Accurate prediction of weather is important to agriculture, shipping, air traffic, and many other fields. Global system models of climate are of great importance. Current global atmospheric models use pressure measurements and thermodynamic properties to calculate the effects of wind for use in Numerical Weather Prediction (NWP) models. Inputs to the NWP models are temperature, pressure and wind velocities at different heights. Clearly direct wind measurements could significantly improve the NWP model performance. The RAdar Wind Sounder (RAWS) program at the University of Kansas is a study of the feasibility and the trade-offs in the design of a space-based radar system to measure wind vectors. This can be done by measuring the Doppler shift of cloud and rain returns from three or more points and calculating the components of the wind vector. The RAWS study to date uses the candidate system selected after preliminary study of frequencies and sensitivities. Two frequencies chosen, 10 and 35 GHz, allow higher sensitivity for clouds and more penetration for rain. The past year was devoted to modeling the signal-to-noise ratio (SNR) achievable for the two frequencies. The determination of SNR versus cloud penetration depth used a cloud backscattering and attenuation model in the appropriate radar equation. Calculations assumed reasonable losses in reception and transmission, in addition to the atmospheric attenuation. We discovered that ice clouds provide a higher SNR than previously calculated, but some water clouds give lower SNRs than we calculated before. One of the primary issues in the SNR calculation was the choice of the drop size distribution. Although Xin used several distributions (e.g., log normal, Khrigian and Mazin), this year we used the Deirmendjian cloud model. SNR versus cloud penetration plots were generated to validate the candidate system. Rain, which appears in the cloud models at the lower altitudes, provides ample SNR, as do the higher clouds composed of ice particles. However, in some cloud situations we found the sensitivity for the clouds was marginal or inadequate. At 35 GHz, two of the cloud models characterized by 1 to 2 g/cu m of water content at altitudes extending from 150 to 1500 meters, produced a sufficient SNR. Other models, however, with water contents ranging from 0.5 to 4 g/cu m and altitudes up to 4000 meters, exhibit SNR of -3 to -23 dB, largely because of attenuation in the upper cloud layers. These results coupled with the lower SNR at 10 GHz, led to an investigation of alternate frequencies. The rain present beneath these clouds provides adequate SNR at 10 GHz, and in most cases, at GHz.

Moore, Richard K.↗

NASA NTRS2009

Expanding Regional Airport Usage to Accommodate Increased Air Traffic Demand

Small regional airports present an underutilized source of capacity in the national air transportation system. This study sought to determine whether a 50 percent increase in national operations could be achieved by limiting demand growth at large hub airports and instead growing traffic levels at the surrounding regional airports. This demand scenario for future air traffic in the United States was generated and used as input to a 24-hour simulation of the national airspace system. Results of the demand generation process and metrics predicting the simulation results are presented, in addition to the actual simulation results. The demand generation process showed that sufficient runway capacity exists at regional airports to offload a significant portion of traffic from hub airports. Predictive metrics forecast a large reduction of delays at most major airports when demand is shifted. The simulation results then show that offloading hub traffic can significantly reduce nationwide delays.

Russell, Carl R.↗