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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Pilot and Controller Evaluations of Separation Function Allocation in Air Traffic Management

Two human-in-the-loop simulation experiments were conducted in coordinated fashion to investigate the allocation of separation assurance functions between ground and air and between humans and automation. The experiments modeled a mixed-operations concept in which aircraft receiving ground-based separation services shared the airspace with aircraft providing their own separation service (i.e., self-separation). Ground-based separation was provided by air traffic controllers without automation tools, with tools, or by ground-based automation with controllers in a managing role. Airborne self-separation was provided by airline pilots using self-separation automation enabled by airborne surveillance technology. The two experiments, one pilot-focused and the other controller-focused, addressed selected key issues of mixed operations, assuming the starting point of current-day operations and modeling an emergence of NextGen technologies and procedures. In the controller-focused experiment, the impact of mixed operations on controller performance was assessed at four stages of NextGen implementation. In the pilot-focused experiment, the limits to which pilots with automation tools could take full responsibility for separation from ground-controlled aircraft were tested. Results indicate that the presence of self-separating aircraft had little impact on the controllers' ability to provide separation services for ground-controlled aircraft. Overall performance was best in the most automated environment in which all aircraft were data communications equipped, ground-based separation was highly automated, and self-separating aircraft had access to trajectory intent information for all aircraft. In this environment, safe, efficient, and highly acceptable operations could be achieved for twice today's peak airspace throughput. In less automated environments, reduced trajectory intent exchange and manual air traffic control limited the safely achievable airspace throughput and negatively impacted the maneuver efficiency of self-separating aircraft through high-density airspace. In a test of scripted conflicts with ground-managed aircraft, flight crews of self-separating aircraft prevented separation loss in all conflicts with detection time greater than one minute. In debrief, pilots indicated a preference for at least five minute's alerting notice and trajectory intent information on all aircraft. When intent information on ground-managed aircraft was available, self-separating aircraft benefited from fewer conflict alerts and fewer required deviations from trajectory-based operations.

Wing, David↗
DOE OSTI2020

Multi-Commodity Traffic Signal Control and Routing With Connected Vehicles

We report a real-time traffic management policy that integrates traffic signal control and multi-commodity routing of connected vehicles in networks with multiple destinations is developed. The proposed policy is based on a multi-commodity formulation of the store-and-forward model and assumes all vehicles are able to exchange information with the infrastructure. Vehicles share information about their current location and final destination. Based on this information, the strategy determines both optimized signal timings at every intersection and vehicle-specific routing information at every link of the network. The control actions, i.e., signal times and routing information, are updated at every cycle and delivered by a finite horizon optimal control problem cast into a rolling horizon framework. The underlying optimization problem is convex, and thus the method is suitable for real-time operation in large networks. The method is validated via a micro-simulation study in networks with up to twenty intersections and, in all simulations, outperforms a real-time traffic-responsive signal control strategy that is based on a single-commodity store-and-forward model. The scalable computation effort for increasing network sizes and prediction horizon confirms the computational efficiency of the method.

42 ENGINEERING↗
NASA NTRS1992

Distributed traffic signal control using fuzzy logic

We present a distributed approach to traffic signal control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic condition and of the signal timing parameters at adjacent intersections. Thus, the signal timing parameters evolve dynamically using only local information to improve traffic flow. This distributed approach provides for a fault-tolerant, highly responsive traffic management system. The signal timing at an intersection is defined by three parameters: cycle time, phase split, and offset. We use fuzzy decision rules to adjust these three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. We show the effectiveness of this method through simulation of the traffic flow in a network of controlled intersections.

Chiu, Stephen↗
NASA NTRS1973

Automatic speed control of highway traffic

Vehicle control system monitors all vehicles in its range, and automatically slows down speeding vehicles by activating governor in vehicle. System determines only maximum speed; speeds below maximum are controlled by vehicle operator. Loss of transmitted signal or activation of emergency over-ride will open fuel line and return control to operator.

Klingman, E. E.↗
DOE OSTI2022

Traffic Signal Control With Adaptive Online-Learning Scheme Using Multiple-Model Neural Networks

This article proposes a new traffic signal control algorithm to deal with unknown-traffic-system uncertainties and reduce delays in vehicle travel time. Unknown-traffic-system dynamics are approximated using a recurrent neural network (NN). To accurately identify the traffic system model, an online-learning scheme is developed to switch among a set of candidate NNs (i.e., multiple-model NNs) based on their estimation errors. Then, a bank of optimal signal-timing controllers is designed based on the online identification of the traffic system. Simulation studies have been carried out for the obtained control strategies using multiple-model NNs, and the desired results have been obtained. Moreover, compared with the widely used actuated traffic signal control schemes, it is shown that the proposed method can reduce vehicle travel delays and improve traffic system robustness.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2010

Interaction Between Strategic and Local Traffic Flow Controls

The loosely coordinated sets of traffic flow management initiatives that are operationally implemented at the national- and local-levels have the potential to under, over, and inconsistently control flights. This study is designed to explore these interactions through fast-time simulations with an emphasis on identifying inequitable situations in which flights receive multiple uncoordinated delays. Two operationally derived scenarios were considered in which flights arriving into the Dallas/Fort Worth International Airport were first controlled at the national-level, either with a Ground Delay Program or a playbook reroute. These flights were subsequently controlled at the local level. The Traffic Management Advisor assigned them arrival scheduling delays. For the Ground Delay Program scenarios, between 51% and 53% of all arrivals experience both pre-departure delays from the Ground Delay Program and arrival scheduling delays from the Traffic Management Advisor. Of the subset of flights that received multiple delays, between 5.7% and 6.4% of the internal departures were first assigned a pre-departure delay by the Ground Delay Program, followed by a second pre-departure delay as a result of the arrival scheduling. For the playbook reroute scenario, Dallas/Fort Worth International Airport arrivals were first assigned pre-departure reroutes based on the MW_2_DALLAS playbook plan, and were subsequently assigned arrival scheduling delays by the Traffic Management Advisor. Since the airport was operating well below capacity when the playbook reroute was in effect, only 7% of the arrivals were observed to receive both rerouting and arrival scheduling delays. Findings from these initial experiments confirm field observations that Ground Delay Programs operated in conjunction with arrival scheduling can result in inequitable situations in which flights receive multiple uncoordinated delays.

Grabbe, Son↗
DOE OSTI2025

Integrated Routing and Traffic Signal Control for CAVs via Reinforcement Learning Approach

Incorporating Connected and Automated Vehicles (CAVs) into urban traffic networks presents opportunities and challenges for traffic management systems. This paper aims to develop an integrated routing and traffic signal control system designed explicitly for CAVs, utilizing a Reinforcement Learning (RL) approach. The objective is to enhance traffic flow and improve overall transportation efficiency in the controlled areas. We propose an innovative framework that employs the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Q-network (DQN), to dynamically adjust the number of vehicles in the routes and the duration of traffic signals. Our simulation results demonstrate that a DQN agent successfully optimizes the number of vehicles in the routes and traffic signal timings of traffic signal controllers, eventually reducing total travel time. The study illustrates the potential usage of RL-based systems in managing routing and traffic signals for CAVs, offering a promising opportunity for future urban traffic management strategies.

Park, Jiho [New York University]↗
DOE OSTI2017

Control of Networked Traffic Flow Distribution: A Stochastic Distribution System Perspective

At signalized intersections, the traffic queues are controlled by traffic signal control settings and effective traffic lights control would realize both smooth traffic flow and minimize fuel consumption. Funded by the Energy Efficient Mobility Systems (EEMS) program of the Vehicle Technologies Office of the US Department of Energy, we performed a preliminary investigation on the modelling and control framework in context of urban network of signalized intersections. In specific, we developed a recursive input-output traffic queueing models. The queue formation can be modeled as a stochastic process where the number of vehicles entering each intersection is a random number. Further, we proposed a preliminary B-Spline stochastic modelling and control for a one-way single-lane corridor traffic system based on theory of stochastic distribution control that shapes the PDF of the queue length.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2017

Weather Impact on Airport Arrival Meter Fix Throughput

Time-based flow management provides arrival aircraft schedules based on arrival airport conditions, airport capacity, required spacing, and weather conditions. In order to meet a scheduled time at which arrival aircraft can cross an airport arrival meter fix prior to entering the airport terminal airspace, air traffic controllers make regulations on air traffic. Severe weather may create an airport arrival bottleneck if one or more of airport arrival meter fixes are partially or completely blocked by the weather and the arrival demand has not been reduced accordingly. Under these conditions, aircraft are frequently being put in holding patterns until they can be rerouted. A model that predicts the weather impacted meter fix throughput may help air traffic controllers direct arrival flows into the airport more efficiently, minimizing arrival meter fix congestion. This paper presents an analysis of air traffic flows across arrival meter fixes at the Newark Liberty International Airport (EWR). Several scenarios of weather impacted EWR arrival fix flows are described. Furthermore, multiple linear regression and regression tree ensemble learning approaches for translating multiple sector Weather Impacted Traffic Indexes (WITI) to EWR arrival meter fix throughputs are examined. These weather translation models are developed and validated using the EWR arrival flight and weather data for the period of April-September in 2014. This study also compares the performance of the regression tree ensemble with traditional multiple linear regression models for estimating the weather impacted throughputs at each of the EWR arrival meter fixes. For all meter fixes investigated, the results from the regression tree ensemble weather translation models show a stronger correlation between model outputs and observed meter fix throughputs than that produced from multiple linear regression method.

Machine Learning Model.↗
DOE OSTI2018

Delay-Based Traffic Signal Control for Throughput Optimality and Fairness at an Isolated Intersection

With the attractive feature of guaranteeing maximum network throughput, backpressure routing has been widely used in wireless communication networks. Motivated by the backpressure routing, in this paper, we propose a delay-based traffic signal control algorithm in transportation networks. We prove that this delay-based control achieves optimal throughput performance, same as the queue-based traffic signal control in the literature. However, a vehicle at a lane whose queue length remains very small may be excessively delayed under the queue-based signal control. Our delay-based backpressure control can deal with the excessive delays and achieve better fairness with respect to delay while still guaranteeing throughput optimality. Moreover, a general weighted control scheme combining the queue-based and delay-based schemes is also investigated, to provide a more flexible control according to the quality of service requirements. Numerical results explore their performance under both homogeneous and heterogeneous traffic scenarios.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRSDate not supplied

Introduction to Air Traffic Management

The presentation introduces students and faculty to air traffic management with focus on air traffic data for data-science. Starting with the common attributes of transportation systems — highway transportation, air transportation and data transportation, the initial set of slides discuss the purpose of data-science in air traffic management, reasons why air traffic management is challenging, and the multidisciplinary nature of air traffic management research. The history of flight from 1903 — Wright Flyer — to 1987 — formation of the National Air Traffic Controllers Association — is briefly discussed. The national airspace system is described in terms of airports in the U. S., air traffic control facilities (flight service stations, terminal, enroute and system command center), airspace geometry (sectors, airways and navaids), governing regulations and directives, airspace classification (Class A through G), special use airspace, visual flight rules and instrument flight rules. The contents of a flight-plan are described. Weather briefing is discussed. The surveillance equipment used for surface, terminal area and enroute are described, and the aircraft states obtained using the surveillance data are listed. Airline operations control functions — schedule development, flight planning, resource scheduling and flight following — are noted. Next, the roles and responsibilities of air traffic controllers and traffic flow managers are discussed. Separation standards and conflict resolution techniques are outlined. Finally, traffic flow management techniques are reviewed with an illustrative example.

Air Traffic Management↗
NASA NTRSDate not supplied

Introduction to Air Traffic Management

The presentation introduces students and faculty to air traffic management with focus on air traffic data for data-science. Starting with the common attributes of transportation systems — highway transportation, air transportation and data transportation, the initial set of slides discuss the purpose of data-science in air traffic management, reasons why air traffic management is challenging, and the multidisciplinary nature of air traffic management research. The history of flight from 1903 — Wright Flyer — to 1987 — formation of the National Air Traffic Controllers Association — is briefly discussed. The national airspace system is described in terms of airports in the U. S., air traffic control facilities (flight service stations, terminal, enroute and system command center), airspace geometry (sectors, airways and navaids), governing regulations and directives, airspace classification (Class A through G), special use airspace, visual flight rules and instrument flight rules. The contents of a flight-plan are described. Weather briefing is discussed. The surveillance equipment used for surface, terminal area and enroute are described, and the aircraft states obtained using the surveillance data are listed. Airline operations control functions — schedule development, flight planning, resource scheduling and flight following — are noted. Next, the roles and responsibilities of air traffic controllers and traffic flow managers are discussed. Separation standards and conflict resolution techniques are outlined. Finally, traffic flow management techniques are reviewed with an illustrative example.

Air Traffic Management↗
NASA NTRS2009

Formal Methods Applications in Air Transportation

The U.S. air transportation system is the most productive in the world, moving far more people and goods than any other. It is also the safest system in the world, thanks in part to its venerable air traffic control system. But as demand for air travel continues to grow, the air traffic control system s aging infrastructure and labor-intensive procedures are impinging on its ability to keep pace with demand. And that impinges on the growth of our economy. Air traffic control modernization has long held the promise of a more efficient air transportation system. Part of NASA s current mission is to develop advanced automation and operational concepts that will expand the capacity of our national airspace system while still maintaining its excellent record for safety. It is a challenging mission, as efforts to modernize have, for decades, been hamstrung by the inability to assure safety to the satisfaction of system operators, system regulators, and/or the traveling public. In this talk, we ll provide a brief history of air traffic control, focusing on the tension between efficiency and safety assurance, and the promise of formal methods going forward.

Farley, Todd↗
NASA NTRS1995

The development of the Final Approach Spacing Tool (FAST): A cooperative controller-engineer design approach

Historically, the development of advanced automation for air traffic control in the United States has excluded the input of the air traffic controller until the need of the development process. In contrast, the development of the Final Approach Spacing Tool (FAST), for the terminal area controller, has incorporated the end-user in early, iterative testing. This paper describes a cooperative between the controller and the developer to create a tool which incorporates the complexity of the air traffic controller's job. This approach to software development has enhanced the usability of FAST and has helped smooth the introduction of FAST into the operational environment.

Lee, Katharine K.↗
NASA NTRS2021

Metroplex Planner User Manual

This document serves as a user manual for the ATD-2 Metroplex Planner (version 6.1.2) for use by the Air Route Traffic Control Center (ARTCC), Terminal Radar Approach Control (TRACON), airport Air Traffic Control Tower, and airline Flight Operators. It describes the elements of the Metroplex Planner interface and provides step-by-step instructions for using the tool. In addition to live flight information, traffic management information, and demand/delay predictions, the Metroplex Planner supports Trajectory Option Set (TOS) rerouting. The Metroplex Planner is a component of the NASA Airspace Technology Demonstration 2 (ATD-2) sub-project.

Deborah L Bakowski↗
NASA NTRS1998

Dynamic Density: An Air Traffic Management Metric

The definition of a metric of air traffic controller workload based on air traffic characteristics is essential to the development of both air traffic management automation and air traffic procedures. Dynamic density is a proposed concept for a metric that includes both traffic density (a count of aircraft in a volume of airspace) and traffic complexity (a measure of the complexity of the air traffic in a volume of airspace). It was hypothesized that a metric that includes terms that capture air traffic complexity will be a better measure of air traffic controller workload than current measures based only on traffic density. A weighted linear dynamic density function was developed and validated operationally. The proposed dynamic density function includes a traffic density term and eight traffic complexity terms. A unit-weighted dynamic density function was able to account for an average of 22% of the variance in observed controller activity not accounted for by traffic density alone. A comparative analysis of unit weights, subjective weights, and regression weights for the terms in the dynamic density equation was conducted. The best predictor of controller activity was the dynamic density equation with regression-weighted complexity terms.

Laudeman, I. V.↗
NASA NTRS1993

Parallel runway requirement analysis study. Volume 1: The analysis

The correlation of increased flight delays with the level of aviation activity is well recognized. A main contributor to these flight delays has been the capacity of airports. Though new airport and runway construction would significantly increase airport capacity, few programs of this type are currently underway, let alone planned, because of the high cost associated with such endeavors. Therefore, it is necessary to achieve the most efficient and cost effective use of existing fixed airport resources through better planning and control of traffic flows. In fact, during the past few years the FAA has initiated such an airport capacity program designed to provide additional capacity at existing airports. Some of the improvements that that program has generated thus far have been based on new Air Traffic Control procedures, terminal automation, additional Instrument Landing Systems, improved controller display aids, and improved utilization of multiple runways/Instrument Meteorological Conditions (IMC) approach procedures. A useful element to understanding potential operational capacity enhancements at high demand airports has been the development and use of an analysis tool called The PLAND_BLUNDER (PLB) Simulation Model. The objective for building this simulation was to develop a parametric model that could be used for analysis in determining the minimum safety level of parallel runway operations for various parameters representing the airplane, navigation, surveillance, and ATC system performance. This simulation is useful as: a quick and economical evaluation of existing environments that are experiencing IMC delays, an efficient way to study and validate proposed procedure modifications, an aid in evaluating requirements for new airports or new runways in old airports, a simple, parametric investigation of a wide range of issues and approaches, an ability to tradeoff air and ground technology and procedures contributions, and a way of considering probable blunder mechanisms and range of blunder scenarios. This study describes the steps of building the simulation and considers the input parameters, assumptions and limitations, and available outputs. Validation results and sensitivity analysis are addressed as well as outlining some IMC and Visual Meteorological Conditions (VMC) approaches to parallel runways. Also, present and future applicable technologies (e.g., Digital Autoland Systems, Traffic Collision and Avoidance System II, Enhanced Situational Awareness System, Global Positioning Systems for Landing, etc.) are assessed and recommendations made.

Ebrahimi, Yaghoob S.↗
NASA NTRS1992

The Center/TRACON Automation System (CTAS): A video presentation

NASA Ames, working with the FAA, has developed a highly effective set of automation tools for aiding the air traffic controller in traffic management within the terminal area. To effectively demonstrate these tools, the video AAV-1372, entitled 'Center/TRACON Automation System,' was produced. The script to the video is provided along with instructions for its acquisition.

Green, Steven M.↗