Results for “traffic prediction” — Page 22

NASA NTRS2015

Identifying Key Issues and Potential Solutions for Integrated Arrival, Departure, Surface Operations by Surveying Stakeholder Preferences

NASA's Aeronautics Research Mission Directorate (ARMD) collaborates with the FAA and industry to provide concepts and technologies that enhance the transition to the next-generation air-traffic management system (NextGen). To facilitate this collaboration, ARMD has a series of Airspace Technology Demonstration (ATD) sub-projects that develop, demonstrate, and transitions NASA technologies and concepts for implementation in the National Airspace System (NAS). The second of these sub-projects, ATD-2, is focused on the potential benefits to NAS stakeholders of integrated arrival, departure, surface (IADS) operations. To determine the project objectives and assess the benefits of a potential solution, NASA surveyed NAS stakeholders to understand the existing issues in arrival, departure, and surface operations, and the perceived benefits of better integrating these operations. NASA surveyed a broad cross-section of stakeholders representing the airlines, airports, air-navigation service providers, and industry providers of NAS tools. The survey indicated that improving the predictability of flight times (schedules) could improve efficiency in arrival, departure, and surface operations. Stakeholders also mentioned the need for better strategic and tactical information on traffic constraints as well as better information sharing and a coupled collaborative planning process that allows stakeholders to coordinate IADS operations. To assess the impact of a potential solution, NASA sketched an initial departure scheduling concept and assessed its viability by surveying a select group of stakeholders for a second time. The objective of the departure scheduler was to enable flights to move continuously from gate to cruise with minimal interruption in a busy metroplex airspace environment using strategic and tactical scheduling enhanced by collaborative planning between airlines and service providers. The stakeholders agreed that this departure concept could improve schedule predictability and suggested several key attributes that were necessary to make the concept successful. The goals and objectives of the planned ATD-2 sub-project will incorporate the results of this stakeholder feedback.

integrated arrival/departure/surface operations↗

NASA NTRS2021

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗

NASA NTRS2021

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗

NASA NTRS2019

Real-Time UAV Trajectory Prediction for Safety Monitoring in Low-Altitude Airspace

The rising number of small unmanned aerial vehicles (UAVs) expected in the next decade will enable a new series of commercial, service, and military operations in low altitude airspace as well as above densely populated areas. These operations may include on-demand delivery, medical transportation services, law enforcement operations, traffic surveillance and many more. Such unprecedented scenarios create the need for robust, efficient ways to monitor the UAV state in time to guarantee safety and mitigate contingencies throughout the operations. This work proposes a generalized monitoring and prediction methodology that utilizes realtime measurements of an autonomous UAV following a series of way-points. Two different methods, based on sinusoidal acceleration profiles and high-order splines, are utilized to generate the predicted path. The monitoring approach includes dynamic trajectory re-planning in the event of unexpected detour or hovering of the UAV during flight. It can be further extended to different vehicle types, to quantify uncertainty affecting the state variables, e.g., aerodynamic and other environmental effects, and can also be implemented to prognosticate safety-critical metrics which depend on the estimated flight path and required thrust. The proposed framework is implemented on a simplified, scalable UAV modeling and control system traversing 3D trajectories. Results presented include examples of real-time predictions of the UAV trajectories during flight and a critical analysis of the proposed scenarios under uncertainty constraints.

UAV trajectory prognosis↗

NASA NTRS2008

Impact of Pilot Delay and Non-Responsiveness on the Safety Performance of Airborne Separation

Assessing the safety effects of prediction errors and uncertainty on automationsupported functions in the Next Generation Air Transportation System concept of operations is of foremost importance, particularly safety critical functions such as separation that involve human decision-making. Both ground-based and airborne, the automation of separation functions must be designed to account for, and mitigate the impact of, information uncertainty and varying human response. This paper describes an experiment that addresses the potential impact of operator delay when interacting with separation support systems. In this study, we evaluated an airborne separation capability operated by a simulated pilot. The experimental runs are part of the Safety Performance of Airborne Separation (SPAS) experiment suite that examines the safety implications of prediction errors and system uncertainties on airborne separation assistance systems. Pilot actions required by the airborne separation automation to resolve traffic conflicts were delayed within a wide range, varying from five to 240 seconds while a percentage of randomly selected pilots were programmed to completely miss the conflict alerts and therefore take no action. Results indicate that the strategicAirborne Separation Assistance System (ASAS) functions exercised in the experiment can sustain pilot response delays of up to 90 seconds and more, depending on the traffic density. However, when pilots or operators fail to respond to conflict alerts the safety effects are substantial, particularly at higher traffic densities.

Consiglio, Maria↗

NASA NTRS2023

An Interface Specification for Urban Air Mobility Performance Models to Support Air Traffic Management Research

Performance modeling of Urban Air Mobility vehicles in support of Air Traffic Management research poses new challenges. These aircraft often rely on a combination of fixed-wing and rotorcraft performance capabilities to enable a new and still-evolving concept of high-volume operations in densely populated areas. These characteristics lead to variability in performance model format and functionality, complicating model development and leading to difficulties for users integrating the models into their applications. This paper describes an interface specification for performance models that is intended to help address this issue. The interface aims to support the core functionality of performance models while also providing the necessary flexibility to both model developers and users. The specification describes the required model documentation, required inputs to the model, and required outputs from the model in general terms that are adaptable to most programming languages. The utilization of an appropriate interface specification will support the development of an Urban Air Mobility performance model database and help to improve interoperability of these models for a broad array of user applications.

Trajectory Prediction↗

NASA NTRS2023

An Interface Specification for Urban Air Mobility Performance Models to Support Air Traffic Management Research

Performance modeling of Urban Air Mobility vehicles in support of Air Traffic Management research poses new challenges. These aircraft often rely on a combination of fixed-wing and rotorcraft performance capabilities to enable a new and still-evolving concept of high-volume operations in densely populated areas. These characteristics lead to variability in performance model format and functionality, complicating model development and leading to difficulties for users integrating the models into their applications. This paper describes an interface specification for performance models that is intended to help address this issue. The interface aims to support the core functionality of performance models while also providing the necessary flexibility to both model developers and users. The specification describes the required model documentation, required inputs to the model, and required outputs from the model in general terms that are adaptable to most programming languages. The utilization of an appropriate interface specification will support the development of an Urban Air Mobility performance model database and help to improve interoperability of these models for a broad array of user applications.

Trajectory Prediction↗

NASA NTRS2013

Data Mining for Understanding and Improving Decision-making Affecting Ground Delay Programs

The continuous growth in the demand for air transportation results in an imbalance between airspace capacity and traffic demand. The airspace capacity of a region depends on the ability of the system to maintain safe separation between aircraft in the region. In addition to growing demand, the airspace capacity is severely limited by convective weather. During such conditions, traffic managers at the FAA's Air Traffic Control System Command Center (ATCSCC) and dispatchers at various Airlines' Operations Center (AOC) collaborate to mitigate the demand-capacity imbalance caused by weather. The end result is the implementation of a set of Traffic Flow Management (TFM) initiatives such as ground delay programs, reroute advisories, flow metering, and ground stops. Data Mining is the automated process of analyzing large sets of data and then extracting patterns in the data. Data mining tools are capable of predicting behaviors and future trends, allowing an organization to benefit from past experience in making knowledge-driven decisions.

Weather↗

NASA NTRS2020

Investigating Effects of Controlled Flights through Fast-Time Simulation

Departure flights at major U.S. airports are often subject to Traffic Management Initiatives to mitigate congestion and delay due to demand-capacity imbalances. These controlled flights can lead to inefficiency and delay on the airport surface. The integrated arrival, departure, and surface traffic management capabilities developed by NASA’s Airspace Technology Demonstration 2 (ATD-2) sub-project provide enhanced operational efficiency and predictability of flight operations through data exchange and integration, surface metering, and automated coordination of release time of controlled flights for overhead stream insertion. This paper evaluates the impacts of controlled flights on airport performance and assesses the ATD-2 benefits of pushback hold advisories for both controlled and non-controlled flights using fast-time simulation for Charlotte Douglas International Airport.

controlled flight↗

NASA NTRS2009

Survey and Method for Determination of Trajectory Predictor Requirements

A survey of air-traffic-management researchers, representing a broad range of automation applications, was conducted to document trajectory-predictor requirements for future decision-support systems. Results indicated that the researchers were unable to articulate a basic set of trajectory-prediction requirements for their automation concepts. Survey responses showed the need to establish a process to help developers determine the trajectory-predictor-performance requirements for their concepts. Two methods for determining trajectory-predictor requirements are introduced. A fast-time simulation method is discussed that captures the sensitivity of a concept to the performance of its trajectory-prediction capability. A characterization method is proposed to provide quicker, yet less precise results, based on analysis and simulation to characterize the trajectory-prediction errors associated with key modeling options for a specific concept. Concept developers can then identify the relative sizes of errors associated with key modeling options, and qualitatively determine which options lead to significant errors. The characterization method is demonstrated for a case study involving future airport surface traffic management automation. Of the top four sources of error, results indicated that the error associated with accelerations to and from turn speeds was unacceptable, the error associated with the turn path model was acceptable, and the error associated with taxi-speed estimation was of concern and needed a higher fidelity concept simulation to obtain a more precise result

Rentas, Tamika L.↗

NASA NTRS2014

Flight Deck Robustness/Conformance Testing with a Surface Management System: An Integrated Pilot-Controller Human-in-the-Loop Surface Operations Simulation

An integrated flight deck and controller human-in-the-loopsimulation was conducted with a total of 120 Dallas-Ft.Worth (DFW) taxi-out operations. In this first integratedPilot-Controller Spot and Runway Departure Advisor(SARDA) simulation, ATC Ground and Local Controllersused the SARDA decision support tool to plan and issuespot release clearances and departure clearances. TheAirport and Terminal Area Simulator (ATAS), a simulatedB737NG piloted, in turn, by 10 commercial transportparticipant pilots, was integrated into the realisticsimulation traffic environment. In the simulation,controllers used SARDA advisories to issue spot release,taxi route, and runway/departure radio voice clearances toall aircraft on the airport surface. Simulation resultsindicated that under a variety of observed pilot/aircraftperformance variations, SARDA yielded controlleradvisories that were: Supportive of current-day time-basedoperations; Compatible with controllers’ expectations;Predictive of actual take-off times; and, Adaptable to offnominalevents. An Information Sharing Display, thatpresented SARDA sequence and timing information on theflight deck, was considered useful for both NextGenoperations and current-day time-based Traffic ManagementInitiative (TMI) operations.

Foyle, David C.↗

DOE OSTI2022

Real-Time On-Ramp Merging Control of Connected and Automated Vehicles using Pseudospectral Convex Optimization

Highway on-ramp merging can be a challenging task for human drivers due to the complex vehicle negotiations and interactions in limited time and space. Connected and automated vehicles (CAVs) have great potential to address the problem and offer many benefits in terms of safety, traffic efficiency, and fuel economy. However, real-time optimal control of CAVs still faces many challenges, including nonlinear dynamics, complex inter-vehicle interactions, and a highly dynamic and uncertain traffic environment. To address these challenges, we develop a novel control approach that balances the solution optimality and computational efficiency to determine optimal merging speed profiles in real time. Specifically, by employing a pseudospectral method and a sequential convex programming approach, two algorithms are proposed and implemented within the model predictive control (MPC) framework to enable real-time generation of optimal solutions for potential on-vehicle applications. The convergence and optimality of the proposed algorithms are validated by comparing with a general-purpose solver under different traffic scenarios.

Shi, Yang↗

DOE OSTI2022

Advancing Platooning with ADAS (Advanced Driver-Assistance Systems) Control Integration and Assessment

Application of Cooperative Adaptive Cruise Control (CACC) to heavy duty trucks known as truck platooning has shown fuel economy improvements on the test track under ideal driving conditions. However, limited test data is available to assess the truck platooning under real-world driving conditions. Under this Cummins-led project that was funded by the U.S. Department of Energy, truck platooning with CACC has been tested on a real-world interstate highway and the results of the project are reviewed in this report. At first, the real-world driving conditions were characterized using National Renewable Energy Laboratory (NREL) Fleet DNA database to define test factors, including route, terrain, and highway traffic. Afterward, both test track and on-highway testing guided by SAE J1321 procedures were conducted to assess truck platooning under controlled and real-world driving conditions. On-highway testing was done on a highway route in Indiana, consisting of low, medium, and high road grade segments. The highway test results of 2-truck and 3-truck platooning showed considerably reduced fuel savings compared to the controlled test track data, which mainly stems from the traffic or high-grade portions of the route. However, integration of Cummins powertrain and vehicle eco-driving features such as predictive cruise control and neutral coasting called ADEPT™ on the lead truck showed an improvement of fuel saving for the trucks in CACC operation. Furthermore, the importance of tire connectivity in efficient and safe operation of the trucks in platooning is characterized.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1981

Pilot scanning patterns while viewing cockpit displays of traffic information

Scanning eye movements of airline pilots were recorded while they judged air traffic situations displayed on cockpit displays of traffic information (CDTI). The observed 1st order transition patterns between points of interest on the display showed reliable deviation from those patterns predicted by the assumption of statistical independence. However, both patterns of transitions correlated quite well with each other. Accordingly, the assumption of independence provided a surprisingly good model of the results. Nevertheless, the deviation between the observed patterns of transition and that based on the assumption of independence was for all subjects in the direction of increased determinism. Thus, the results provide objective evidence consistent with the existence of "scanpaths" in the data.

Ellis, S. R.↗

NASA NTRS1982

Contingency in visual scanning of cockpit traffic displays

A statistical method to identify reliable, repetitive scanning patterns in the positions of visual fixations has been developed and applied to data from 8 airline pilots who monitored cockpit displays of traffic information (CDTI). Their fixational transition patterns between points of interest on the display showed deterministic, statistical dependencies generally associated with symbols predicting future aircraft position. These dependencies constitute deviations from stratified random sampling of the visual information on the display, confirm the importance of future aircraft position information for the use of CDTI, and provide objective support for the existence of 'scanpaths' in visual fixation patterns.

Ellis, S. R.↗

NASA NTRS1996

Evaluation of Perspective and Coplanar Cockpit Displays of Traffic Information to Support Hazard Awareness in Free Flight

We examined the cockpit display representation of traffic, to support the pilot in tactical planning and conflict avoidance. Such displays may support the "free flight" concept, but can also support greater situation awareness in a non-free flight environment. Two perspective views and a coplanar display were contrasted in scenarios in which pilots needed to navigate around conflicting traffic, either in the absence (low workload) or presence (high workload) of a second intruder aircraft. All three formats were configured with predictive aiding vectors that explicitly represented the predicted point of closest pass, and predicted penetration of an alert zone around ownship. Ten pilots were assigned to each of the display conditions, and each flew a series of 60 conflict maneuvers that varied in their workload and the complexity of the conflict geometry. Results indicated a tendency to choose vertical over lateral maneuvers, a tendency which was amplified with the coplanar display. Vertical maneuvers by the intruder produced an added source of workload. Importantly, the coplanar display supported performance in all measures that was equal to or greater than either of the perspective displays (i.e., fewer predicted and actual conflicts, less extreme maneuvers). Previous studies that have indicated perspective superiority have only contrasted these with UNIplanar displays rather than the coplanar display used here.

Merwin, David H.↗

NASA NTRSDate not supplied

Managing Traffic on the Three-Way Street: Steps Toward Closing the Aerosol Forcing Uncertainty Gap

Understanding changes in the radiative forcing of climate is critical for any effort to attribute, mitigate, or predict climate change. Although greenhouse gases (GHGs) contribute most of the radiative forcing, uncertainty in the climate forcing by airborne particles (aerosols) dominates the uncertainty in forcing changes overall. Yet, aerosol forcing has remained virtually undiminished for more than 20 years despite considerable advances in most of the key contributing elements. Satellite and suborbital measurements, as well as modeling, each have essential roles to play in reducing the uncertainty in the aerosol forcing of climate. This presentation will begin by briefly covering the reasons why aerosols are important for climate study, and will then summarize what we are learning about aerosols from current satellite remote-sensing work, and allude to the roles of suborbital measurements and models. Emphasis will be placed on nascent efforts to link satellite data with models, the need to continue as planned current programs supporting advanced, global-scale satellite and surface-based aerosol and precursor gas observations, climate modeling, as well as intensive field campaigns aimed at characterizing the underlying physical and chemical processes involved. The talk will conclude by highlighting new efforts needed to obtain systematic in situ measurements of aerosol microphysical and chemical properties, along with a greater research focus on integrating the unique contributions of measurements and modeling toward reducing the aerosol climate forcing uncertainty gap.

aerosol↗

DOE OSTI2020

Post-Disturbance Dynamic Distribution System Restoration with DGs and Mobile Resources

Distributed generations (DGs) can act as emergency power supplies when distribution systems suffer from outages. However, the generation capabilities of DGs are generally limited by a number of factors including weather conditions, fuel limitations, etc. In this context, mobile resources that are able to reallocate resources to desired locations are regarded as important complements to conventional fixed DGs in assisting distribution system restoration. In this paper, a distribution system restoration model with DGs and mobile resources is proposed. Firstly, the dispatch and allocation of mobile resources are modeled with respect to the characteristics of the traffic network. Then the developed mobile resource models are integrated into the distribution system restoration model to co-optimize the scheduling of DGs and mobile resources. Uncertainty factors are managed by a model predictive control approach so that system operators can dynamically adjust the restoration strategy with the up-to-date information. The effectiveness of the proposed method is validated through an IEEE 13-bus test system.

distributed generations (DGs)↗