Results for “Traffic Control” — Page 19

NASA NTRS1999

Alternative Architectures for Distributed Cooperative Problem-Solving in the National Airspace System

The air traffic management system in the United States is an example of a distributed problem solving system. It has elements of both cooperative and competitive problem-solving. This system includes complex organizations such as Airline Operations Centers (AOCs), the FAA Air Traffic Control Systems Command Center (ATCSCC), and traffic management units (TMUs) at enroute centers and TRACONs, all of which have a major focus on strategic decision-making. It also includes individuals concerned more with tactical decisions (such as air traffic controllers and pilots). The architecture for this system has evolved over time to rely heavily on the distribution of tasks and control authority in order to keep cognitive complexity manageable for any one individual operator, and to provide redundancy (both human and technological) to serve as a safety net to catch the slips or mistakes that any one person or entity might make. Currently, major changes are being considered for this architecture, especially with respect to the locus of control, in an effort to improve efficiency and safety. This paper uses a series of case studies to help evaluate some of these changes from the perspective of system complexity, and to point out possible alternative approaches that might be taken to improve system performance. The paper illustrates the need to maintain a clear understanding of what is required to assure a high level of performance when alternative system architectures and decompositions are developed.

Smith, Phillip J.↗

NASA NTRS1996

A Task-Analytic Approach to the Determination of Training Requirements for the Precision Descent

A task-analytic approach was used to evaluate the results from an experiment comparing two training methods for the "Precision Descent," a cockpit procedure designed to complement a new, computer-based air traffic control advisory system by allowing air traffic controllers to assign precise descent trajectories to aircraft. A task model was developed for the procedure using a methodology that represents four different categories of task-related knowledge: (1) ability to determine current flight goals; (2) ability to assess the current flight situation relative to those goals; (3) operational knowledge about flight-related tasks; and (4) knowledge about task selection. This model showed what knowledge experienced pilots already possessed, and how that knowledge was supplemented by training material provided in the two training conditions. All flight crews were given a "Precision Descent Chart" that explained the procedure's clearances and compliance requirements. This information enabled pilots to establish appropriate flight goals for the descent, and to monitor their compliance with those goals. In addition to this chart, half of the crews received a "Precision Descent Bulletin" containing technique recommendations for performing procedure-related tasks. The Bulletin's recommendations supported pilots in task selection and helped clarify the procedure's compliance requirements. Eight type-rated flight crews flew eight Precision Descents in a Boeing 747-400 simulator, with four crews in each of the two training conditions. Both conditions (Chart and Chart-with-Bulletin) relied exclusively on the use of those documents to introduce the procedure. No performance feedback was provided during the experiment. Preliminary result show better procedure compliance and higher acceptability ratings from flight crews in the Chart-with-Bulletin condition. These crews performed flight-related tasks less efficiently, however, using the simpler but less efficient methods suggested in the Bulletin. When a more efficient method was recognized, these crews tended to use the more efficient method in addition to the Bulletin's recommendation, instead of replacing it.

Smith, Nancy↗

NASA NTRS2012

Traffic Aware Strategic Aircrew Requests (TASAR)

Under Instrument Flight Rules, pilots are not permitted to make changes to their approved trajectory without first receiving permission from Air Traffic Control (ATC). Referred to as "user requests," trajectory change requests from aircrews are often denied or deferred by controllers because they have awareness of traffic and airspace constraints not currently available to flight crews. With the introduction of Automatic Dependent Surveillance-Broadcast (ADS-B) and other information services, a rich traffic, weather, and airspace information environment is becoming available on the flight deck. Automation developed by NASA uses this information to aid flight crews in the identification and formulation of optimal conflict-free trajectory requests. The concept of Traffic Aware Strategic Aircrew Requests (TASAR) combines ADS-B and airborne automation to enable user-optimal in-flight trajectory replanning and to increase the likelihood of ATC approval for the resulting trajectory change request. TASAR may improve flight efficiency or other user-desired attributes of the flight while not impacting and potentially benefiting the air traffic controller. This paper describes the TASAR concept of operations, its enabling automation technology which is currently under development, and NASA s plans for concept assessment and maturation.

Ballin, Mark G.↗

DOE OSTI2019

Investigating the Impact of Connected Vehicle Market Share on the Performance of Reinforcement-Learning Based Traffic Signal Control

We aim to understand and explore the performance of reinforcement learning based signal control algorithms in a mixed environment with less than 100% market share of connected and automated vehicles (CAVs). Within a simulation environment, we have considered partial connectivity—less than 100% market share of CAVs—in the network and investigated the impact on the performance of the signal control algorithm. Two test networks including a four-intersection arterial in Lankershim Boulevard, CA, and a portion of downtown Springfield, IL, with 20 intersections. The first network is calibrated in the micro-simulator PTV Vissim with the US DOT provided NGSIM datasets. The results provide insights regarding the impact of the connectivity and sensing technologies on the practical implementation of traffic signal control algorithms that leverage the data sharing capability of a connected environment. For scenarios with 40% or more market share of CAVs, we observed improvement in the performance metrics—travel time, queue time, and energy consumption—with the increase in market share. Results from our experiments do not indicate any clear trend when the networks have low (less than 40%) market share of CAVs. The higher standard deviations as obtained from the statistical analyses of the performance metrics at low market share may indicate the instability of the RL controller arising from the partial (if not zero) observability of the traffic states. Further, we have conducted simplified scenario analyses to explore the impact of the market share of battery electric vehicles (BEVs) on energy consumption due to the regenerative braking feature. Our results and findings will be the foundation for the future reinforcement learning based control algorithm development that accounts for partial connectivity—less than 100% CAV market share, and the presence of BEVs in a network of connected and automated signalized intersections.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

HumanLight: Incentivizing ridesharing via human-centric deep reinforcement learning in traffic signal control

Not Available

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2013

Work Practice Simulation of Complex Human-Automation Systems in Safety Critical Situations: The Brahms Generalized berlingen Model

The transition from the current air traffic system to the next generation air traffic system will require the introduction of new automated systems, including transferring some functions from air traffic controllers to on-board automation. This report describes a new design verification and validation (V&V) methodology for assessing aviation safety. The approach involves a detailed computer simulation of work practices that includes people interacting with flight-critical systems. The research is part of an effort to develop new modeling and verification methodologies that can assess the safety of flight-critical systems, system configurations, and operational concepts. The 2002 Ueberlingen mid-air collision was chosen for analysis and modeling because one of the main causes of the accident was one crew's response to a conflict between the instructions of the air traffic controller and the instructions of TCAS, an automated Traffic Alert and Collision Avoidance System on-board warning system. It thus furnishes an example of the problem of authority versus autonomy. It provides a starting point for exploring authority/autonomy conflict in the larger system of organization, tools, and practices in which the participants' moment-by-moment actions take place. We have developed a general air traffic system model (not a specific simulation of Überlingen events), called the Brahms Generalized Ueberlingen Model (Brahms-GUeM). Brahms is a multi-agent simulation system that models people, tools, facilities/vehicles, and geography to simulate the current air transportation system as a collection of distributed, interactive subsystems (e.g., airports, air-traffic control towers and personnel, aircraft, automated flight systems and air-traffic tools, instruments, crew). Brahms-GUeM can be configured in different ways, called scenarios, such that anomalous events that contributed to the Überlingen accident can be modeled as functioning according to requirements or in an anomalous condition, as occurred during the accident. Brahms-GUeM thus implicitly defines a class of scenarios, which include as an instance what occurred at Überlingen. Brahms-GUeM is a modeling framework enabling "what if" analysis of alternative work system configurations and thus facilitating design of alternative operations concepts. It enables subsequent adaption (reusing simulation components) for modeling and simulating NextGen scenarios. This project demonstrates that BRAHMS provides the capacity to model the complexity of air transportation systems, going beyond idealized and simple flights to include for example the interaction of pilots and ATCOs. The research shows clearly that verification and validation must include the entire work system, on the one hand to check that mechanisms exist to handle failures of communication and alerting subsystems and/or failures of people to notice, comprehend, or communicate problematic (unsafe) situations; but also to understand how people must use their own judgment in relating fallible systems like TCAS to other sources of information and thus to evaluate how the unreliability of automation affects system safety. The simulation shows in particular that distributed agents (people and automated systems) acting without knowledge of each others' actions can create a complex, dynamic system whose interactive behavior is unexpected and is changing too quickly to comprehend and control.

complex systems↗

NASA NTRS1988

A simulation study of data link message exchange - Flight deck perspective

Data link message exchange for Air Traffic Control (ATC) offers the potential of increasing flight safety and efficiency by reducing communication errors and enabling more information to be exchanged between aircraft and ground facilities. Digital communication also presents an opportunity to relieve the overloading of air traffic control radio frequencies, which hampers efficient message exchange during peak traffic periods in many busy terminal areas. A study has been completed at NASA Langley Research Center to compare the use of data link, for tactical ATC messages and ATIS reports, to conventional voice communication. The results indicate that flight crews prefer the data link message exchange for routine communication with air traffic control facilities. The data link communication process, however, entailed delays related to the physical characteristics of the link and the nature of the interface explored in this study. Consequently, the crews preferred voice radio when exchanging time critical messages.

Waller, Marvin C.↗

NASA NTRS2005

Air Traffic Management Research at NASA Ames Research Center

Since the late 1980's, NASA Ames researchers have been investigating ways to improve the air transportation system through the development of decision support automation. These software advances, such as the Center-TRACON Automation System (eTAS) have been developed with teams of engineers, software developers, human factors experts, and air traffic controllers; some ASA Ames decision support tools are currently operational in Federal Aviation Administration (FAA) facilities and some are in use by the airlines. These tools have provided air traffic controllers and traffic managers the capabilities to help reduce overall delays and holding, and provide significant cost savings to the airlines as well as more manageable workload levels for air traffic service providers. NASA is continuing to collaborate with the FAA, as well as other government agencies, to plan and develop the next generation of decision support tools that will support anticipated changes in the air transportation system, including a projected increase to three times today's air-traffic levels by 2025. The presentation will review some of NASA Ames' recent achievements in air traffic management research, and discuss future tool developments and concepts currently under consideration.

Lee, Katharine↗

NASA NTRS2017

Towards Designing Graceful Degradation into Trajectory Based Operations: A Human-Systems Integration Approach

One of the most fundamental changes to the air traffic management system in NextGen is the concept of trajectory based operations (TBO). With the introduction of such change, system safety and resilience is a critical concern, in particular, the ability of systems to gracefully degrade. In order to design graceful degradation into a TBO envrionment, knowledge of the potential causes of degradation, and appropriate solutions, is required. In addition, previous research has predominantly explored the technological contribution to graceful degradation, frequently neglecting to consider the role of the human operator, specifically, air traffic controllers (ATCOs). This is out of step with real-world operations, and potentially limits an ecologically valid understanding of achieving graceful degradation in an air traffic control (ATC) environment. The following literature review aims to identify and summarize the literature to date on the potential causes of degradation in ATC and the solutions that may be applied within a TBO context, with a specific focus on the contribution of the air traffic controller. A framework of graceful degradation, developed from the literature, is presented. It is argued that in order to achieve graceful degradation within TBO, a human-system integration approach must be applied.

human-system integration↗

NASA NTRS2017

Towards Designing Graceful Degradation into Trajectory Based Operations: A Human-Machine System Integration Approach

One of the most fundamental changes to the air traffic management system in NextGen is the concept of trajectory based operations (TBO). With the introduction of such change, system safety and resilience is a critical concern, in particular, the ability of systems to gracefully degrade. In order to design graceful degradation into a TBO envrionment, knowledge of the potential causes of degradation, and appropriate solutions, is required. In addition, previous research has predominantly explored the technological contribution to graceful degradation, frequently neglecting to consider the role of the human operator, specifically, air traffic controllers (ATCOs). This is out of step with real-world operations, and potentially limits an ecologically valid understanding of achieving graceful degradation in an air traffic control (ATC) environment. The following literature review aims to identify and summarize the literature to date on the potential causes of degradation in ATC and the solutions that may be applied within a TBO context, with a specific focus on the contribution of the air traffic controller. A framework of graceful degradation, developed from the literature, is presented. It is argued that in order to achieve graceful degradation within TBO, a human-system integration approach must be applied.

human-system integration↗

NASA NTRS2017

Towards Designing Graceful Degradation into Trajectory Based Operations: A Human-Machine System Integration Approach

One of the most fundamental changes to the air traffic management system in NextGen is the concept of trajectory based operations (TBO). With the introduction of such change, system safety and resilience is a critical concern, in particular, the ability of systems to gracefully degrade. In order to design graceful degradation into a TBO envrionment, knowledge of the potential causes of degradation, and appropriate solutions, is required. In addition, previous research has predominantly explored the technological contribution to graceful degradation, frequently neglecting to consider the role of the human operator, specifically, air traffic controllers (ATCOs). This is out of step with real-world operations, and potentially limits an ecologically valid understanding of achieving graceful degradation in an air traffic control (ATC) environment. The following literature review aims to identify and summarize the literature to date on the potential causes of degradation in ATC and the solutions that may be applied within a TBO context, with a specific focus on the contribution of the air traffic controller. A framework of graceful degradation, developed from the literature, is presented. It is argued that in order to achieve graceful degradation within TBO, a human-system integration approach must be applied.

trajectory based operations↗

NASA NTRS2014

UAS Measured Response: The Effect of GCS Control Mode Interfaces on Pilot Ability to Comply with ATC Clearances

The present study examines the effects of three different control mode interfaces on unmanned aerial system (UAS) pilots ability to comply with air traffic controller traffic clearances. Pilots controlled a simulated UAS with a waypoint-only interface, an auto-pilot interface and a manual, stick and throttle interface. Results indicate that pilots are best able to get in-the-loop when provided with auto-pilot and manual control inputs. Limitations to the present study and future analyses are discussed.

Measured Response↗

NASA NTRS1994

A knowledge-based system for controlling automobile traffic

Transportation network capacity variations arising from accidents, roadway maintenance activity, and special events as well as fluctuations in commuters' travel demands complicate traffic management. Artificial intelligence concepts and expert systems can be useful in framing policies for incident detection, congestion anticipation, and optimal traffic management. This paper examines the applicability of intelligent route guidance and control as decision aids for traffic management. Basic requirements for managing traffic are reviewed, concepts for studying traffic flow are introduced, and mathematical models for modeling traffic flow are examined. Measures for quantifying transportation network performance levels are chosen, and surveillance and control strategies are evaluated. It can be concluded that automated decision support holds great promise for aiding the efficient flow of automobile traffic over limited-access roadways, bridges, and tunnels.

Maravas, Alexander↗

NASA NTRSDate not supplied

Applying Machine Learning Tools for Runway Configuration Decision Support

Determining optimal runway configurations at airports, a responsibility assigned to air traffic controllers, is a challenging task. The decision-making process is intricate and involves consideration of many factors such as prevailing wind condition, convective weather, visibility, cloud ceilings, departure and arrival demand, traffic flow, equipment status, and other airport constraints. In a previous work, we developed a Runway Configuration Assistance tool using an offline reinforcement learning method called conservative Q-learning. In this paper, we evaluate and validate our Runway Configuration Assistance tool as a decision support for air traffic controllers. We validated our tool using three airports with differing levels of complexity: Charlotte Douglas International Airport, Denver International Airport, and Dallas Fort Worth International Airport. We quantified the performance of the Runway Configuration Assistance tool based on (1) agreement with historical air traffic controller decisions and (2) violation of decisions that would be obvious to subject-matter experts. Our tool showed promising results in both performance metrics for the three airports, despite the complexities in the runway configuration decision-making process. We also discuss challenges in using machine learning in general to aid air traffic management and identify deployment considerations for the Runway Configuration Assistance tool.

Runway Configuration Management↗

NASA NTRSDate not supplied

Machine Learning Tools for Runway Configuration Decision Support

Determining optimal runway configurations at airports, a responsibility assigned to air traffic controllers, is a challenging task. The decision-making process is intricate and involves consideration of many factors such as prevailing wind condition, convective weather, visibility, cloud ceilings, departure and arrival demand, traffic flow, equipment status, and other airport constraints. In a previous work, we developed a Runway Configuration Assistance tool using an offline reinforcement learning method called conservative Q-learning. In this paper, we evaluate and validate our Runway Configuration Assistance tool as a decision support for air traffic controllers. We validated our tool using three airports with differing levels of complexity: Charlotte Douglas International Airport, Denver International Airport, and Dallas Fort Worth International Airport. We quantified the performance of the Runway Configuration Assistance tool based on (1) agreement with historical air traffic controller decisions and (2) violation of decisions that would be obvious to subject-matter experts. Our tool showed promising results in both performance metrics for the three airports, despite the complexities in the runway configuration decision-making process. We also discuss challenges in using machine learning in general to aid air traffic management and identify deployment considerations for the Runway Configuration Assistance tool.

Runway Configuration Management↗

NASA NTRS1984

Application of fuel/time minimization techniques to route planning and trajectory optimization

Rising fuel costs combined with other economic pressures have resulted in industry requirements for more efficient air traffic control and airborne operations. NASA has responded with an on-going research program to investigate the requirements and benefits of using new airborne guidance and pilot procedures that are compatible with advanced air traffic control systems and that will result in more fuel efficient flight. The results of flight testing an airborne computer algorithm designed to provide either open-loop or closed-loop guidance for fuel efficient descents while satisfying time constraints imposed by the air traffic control system is summarized. Some of the potential cost and fuel savings that are obtained with sophisticated vertical path optimization capabilities are described.

Knox, C. E.↗

NASA NTRS2001

User Acceptability of Physiological and Other Measures of Hazardous States of Awareness

Two studies explored user acceptance of devices that measure hazardous states of awareness. In the first study, critical incident data were collected in two workshops from 11 operators working as air traffic controllers or commercial pilots. These critical incident data were used to develop a survey of the acceptability of awareness measures. In the second study, the survey was administered to 100 people also working as air traffic controllers or commercial pilots. Results show that operators are open to the inclusion of technology to measure HSAs even if that technology is somewhat invasive as long as feedback about the HSAs is considered to be useful and helpful. Nonetheless, a major concern is the legal complications associated with being recorded, particularly for older and more experienced operators. Air traffic controllers emphasized the importance of sharing technology information with supervisors in order to receive backup or assistance under conditions of task overload, whereas pilots emphasized the influence of work schedules on problems with awareness. Recommendations are offered concerning the implementation of devices to measure hazardous states of awareness.

Dickinson, Terry L.↗

DOE OSTI2023

A Novel Deep Reinforcement Learning Approach to Traffic Signal Control with Connected Vehicles

The advent of connected vehicle (CV) technology offers new possibilities for a revolution in future transportation systems. With the availability of real-time traffic data from CVs, it is possible to more effectively optimize traffic signals to reduce congestion, increase fuel efficiency, and enhance road safety. The success of CV-based signal control depends on an accurate and computationally efficient model that accounts for the stochastic and nonlinear nature of the traffic flow. Without the necessity of prior knowledge of the traffic system’s model architecture, reinforcement learning (RL) is a promising tool to acquire the control policy through observing the transition of the traffic states. In this paper, we propose a novel data-driven traffic signal control method that leverages the latest in deep learning and reinforcement learning techniques. By incorporating a compressed representation of the traffic states, the proposed method overcomes the limitations of the existing methods in defining the action space to include more practical and flexible signal phases. The simulation results demonstrate the convergence and robust performance of the proposed method against several existing benchmark methods in terms of average vehicle speeds, queue length, wait time, and traffic density.

42 ENGINEERING↗