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At least 19 records

Initial Data Analysis Results for ATD-2 ISAS HITL Simulation

To evaluate the operational procedures and information requirements for the core functional capabilities of the ATD-2 project, such as tactical surface metering tool, APREQ-CFR procedure, and data element exchanges between ramp and tower, human-in-the-loop (HITL) simulations were performed in March, 2017. This presentation shows the initial data analysis results from the HITL simulations. With respect to the different runway configurations and metering values in tactical surface scheduler, various airport performance metrics were analyzed and compared. These metrics include gate holding time, taxi-out in time, runway throughput, queue size and wait time in queue, and TMI flight compliance. In addition to the metering value, other factors affecting the airport performance in the HITL simulation, including run duration, runway changes, and TMI constraints, are also discussed.

HITL simulation

Assessment of Ramp Times (ART 2) Human-In-The-Loop (HITL) Simulation - Final Results

Airspace Technology Demonstration 2 (ATD-2) sub-project conducted a human-in-the-loop (HITL) simulation to assess various strategies for Ramp controllers to deliver aircraft to the spot at a specified time. Results show that the rate of compliance with the spot time improved when Ramp controllers first complied with a gate hold advisory for pushing aircraft off the gates. Results also show that Ramp controller workload was lower when they only had to focus on complying with the gate hold advisories.

Stevens, Lindsay K.

Assessment of Ramp Times 4 (ART-4) Human-in-the-Loop (HITL) Simulation - Final Results

Airspace Technology Demonstration 2 (ATD-2) sub-project conducted a Human-in-the-Loop (HITL) simulation to assess Ramp Controllers ability to deliver aircraft to the spot within the compliance window (+/- 5 min) under various metering conditions. Compliance at the spot was similar between the different metering conditions ranging between 83% - 85% and increased to 92% - 99% when aircraft were initially compliant with gate advisories. Metering benefits that exist in the field did not appear in the simulation due to simulation artifacts such as gate holding departures in Baseline, which effectively metered the demand. The combined Target Off-Block Time (TOBT) +Target Movement Area entry Time (TMAT) condition resulted in higher workload on the Workload Assessment Keypad (WAK) than the Baseline and TOBT alone conditions, and lower situation awareness than the Baseline condition. Metering at Dallas/Fort Worth International Airport (DFW) with TOBT only or TMAT only could be equally effective, and either would be a better option than TOBT + TMAT due to increased workload and reduced situation awareness.

Jung, Yoon C.

SARDA HITL Simulations: System Performance Results

This presentation gives an overview of the 2012 SARDA human-in-the-loop simulation, and presents a summary of system performance results from the simulation, including delay, throughput and fuel consumption

Gupta, Gautam

Building Airport Surface HITL Simulation Capability

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

facility capabilities

NextGen Technologies on the FAA's Standard Terminal Automation Replacement System

This paper describes the integration, evaluation, and results from a high-fidelity human-in-the-loop (HITL) simulation of key NASA Air Traffic Management Technology Demonstration - 1 (ATD- 1) technologies implemented in an enhanced version of the FAA's Standard Terminal Automation Replacement System (STARS) platform. These ATD-1 technologies include: (1) a NASA enhanced version of the FAA's Time-Based Flow Management, (2) a NASA ground-based automation technology known as controller-managed spacing (CMS), and (3) a NASA advanced avionics airborne technology known as flight-deck interval management (FIM). These ATD-1 technologies have been extensively tested in large-scale HITL simulations using general-purpose workstations to study air transportation technologies. These general purpose workstations perform multiple functions and are collectively referred to as the Multi-Aircraft Control System (MACS). Researchers at NASA Ames Research Center and Raytheon collaborated to augment the STARS platform by including CMS and FIM advisory tools to validate the feasibility of integrating these automation enhancements into the current FAA automation infrastructure. NASA Ames acquired three STARS terminal controller workstations, and then integrated the ATD-1 technologies. HITL simulations were conducted to evaluate the ATD-1 technologies when using the STARS platform. These results were compared with the results obtained when the ATD-1 technologies were tested in the MACS environment. Results collected from the numerical data show acceptably minor differences, and, together with the subjective controller questionnaires showing a trend towards preferring STARS, validate the ATD-1/STARS integration.

air traffic management

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model

Initial Evaluation of a Conflict Detection Tool in the Terminal Area

Despite the recent economic recession and its adverse impact on air travel, the Federal Aviation Administration (FAA) continues to forecast an increase in air traffic demand that may see traffic double or triple by the year 2025. Increases in air traffic will burden the air traffic management system, and higher levels of safety and efficiency will be required. The air traffic controllers primary task is to ensure separation between aircraft in their airspace and keep the skies safe. As air traffic is forecasted to increase in volume and complexity [1], there is an increased likelihood of conflicts between aircraft, which adds risk and inefficiency to air traffic management and increases controller workload. To attenuate these factors, recent ATM research has shown that air and ground-based automation tools could reduce controller workload, especially if the automation is focused on conflict detection and resolution. Conflict Alert is a short time horizon conflict detection tool deployed in the Terminal Radar Approach Control (TRACON), which has limited utility due to the high number of false alerts generated and its use of dead reckoning to predict loss of separation between aircraft. Terminal Tactical Separation Assurance Flight Environment (T-TSAFE) is a short time horizon conflict detection tool that uses both flight intent and dead reckoning to detect conflicts. Results of a fast time simulation experiment indicated that TTSAFE provided a more effective alert lead-time and generated less false alerts than Conflict Alert [2]. TSAFE was previously tested in a Human-In-The-Loop (HITL) simulation study that focused on the en route phase of flight [3]. The current study tested the T-TSAFE tool in an HITL simulation study, focusing on the terminal environment with current day operations. The study identified procedures, roles, responsibilities, information requirements and usability, with the help of TRACON controllers who participated in the experiment. Metrics such as lead alert time, alert response time, workload, situation awareness and other measures were statistically analyzed. These metrics were examined from an overall perspective and comparisons between conditions (altitude resolutions via keyboard entry vs. ADS-B entry) and controller positions (two final approach sectors and two feeder sectors) were also examined. Results of these analyses and controller feedback provided evidence of T-TSAFE s potential promise as a useful air traffic controller tool. Heuristic analysis also provided information on ways in which the T-TSAFE tool can be improved. Details of analyses results will be presented in the full paper.

Verma Savita Arora

Evaluation of a Tactical Surface Metering Tool for Charlotte Douglas International Airport via Human-in-the-Loop Simulation

NASA has been working with the FAA and aviation industry partners to develop and demonstrate new concepts and technologies that integrate arrival, departure, and surface traffic management capabilities. In March 2017, NASA conducted a human-in-the-loop (HITL) simulation for integrated surface and airspace operations, modeling Charlotte Douglas International Airport, to evaluate the operational procedures and information requirements for the tactical surface metering tool, and data exchange elements between the airline controlled ramp and ATC Tower. In this paper, we focus on the calibration of the tactical surface metering tool using various metrics measured from the HITL simulation results. Key performance metrics include gate hold times from pushback advisories, taxi-in-out times, runway throughput, and departure queue size. Subjective metrics presented in this paper include workload, situational awareness, and acceptability of the metering tool and its calibration.

Verma, Savita

Evaluation of a Tactical Surface Metering Tool for Charlotte Douglas International Airport Via Human-in-the-Loop Simulation

NASA has been working with the FAA and aviation industry partners to develop and demonstrate new concepts and technologies that integrate arrival, departure, and surface traffic management capabilities. In March 2017, NASA conducted a human-in-the-loop (HITL) simulation for integrated surface and airspace operations, modeling Charlotte Douglas International Airport, to evaluate the operational procedures and information requirements for the tactical surface metering tool, and data exchange elements between the airline controlled ramp and ATC Tower. In this paper, we focus on the calibration of the tactical surface metering tool using various metrics measured from the HITL simulation results. Key performance metrics include gate hold times from pushback advisories, taxi-in/out times, runway throughput, and departure queue size. Subjective metrics presented in this paper include workload, situational awareness, and acceptability of the metering tool and its calibration

air traffic management

A Human-in-the Loop Evaluation of a Coordinated Arrival Departure Scheduling Operations for Managing Departure Delays at LaGuardia Airport

LaGuardia (LGA) departure delay was identified by the stakeholders and subject matter experts as a significant bottleneck in the New York metropolitan area. Departure delay at LGA is primarily due to dependency between LGA's arrival and departure runways: LGA departures cannot begin takeoff until arrivals have cleared the runway intersection. If one-in one-out operations are not maintained and a significant arrival-to-departure imbalance occurs, the departure backup can persist through the rest of the day. At NASA Ames Research Center, a solution called "Departure-sensitive Arrival Spacing" (DSAS) was developed to maximize the departure throughput without creating significant delays in the arrival traffic. The concept leverages a Terminal Sequencing and Spacing (TSS) operations that create and manage the arrival schedule to the runway threshold and added an interface enhancement to the traffic manager's timeline to provide the ability to manually adjust inter-arrival spacing to build precise gaps for multiple departures between arrivals. A more complete solution would include a TSS algorithm enhancement that could automatically build these multi-departure gaps. With this set of capabilities, inter-arrival spacing could be controlled for optimal departure throughput. The concept was prototyped in a human-in-the- loop (HITL) simulation environment so that operational requirements such as coordination procedures, timing and magnitude of TSS schedule adjustments, and display features for Tower, TRACON and Traffic Management Unit could be determined. A HITL simulation was conducted in August 2014 to evaluate the concept in terms of feasibility, controller workload impact, and potential benefits. Three conditions were tested, namely a Baseline condition without scheduling, TSS condition that schedules the arrivals to the runway threshold, and TSS+DSAS condition that adjusts the arrival schedule to maximize the departure throughput. The results showed that during high arrival demand period, departure throughput could be incrementally increased under TSS and TSS+DSAS conditions without compromising the arrival throughput. The concept, operational procedures, and summary results were originally published in ATM20151 but detailed results were omitted. This paper expands on the earlier paper to provide the detailed results on throughput, conformance, safety, flight time/distance, etc. that provide extra insights into the feasibility and the potential benefits on the concept.

trajectory-based operations

Accounting for the Speed-Accuracy Trade-Off in Quantifying Human-In-The-Loop Error Probabilities

Human-in-the-loop (HITL) simulations cannot collect enough data from human operators to validate estimates of error probabilities for task components. Error rates for tasks have been estimated by using laboratory data for error rates depending on variables such as the cognitive complexity of the task. The limited channel capacity of human operators compels error rates to be strongly related to the time available for the task, the speed-accuracy trade-off. HITL simulations can provide valuable data on the time available for the operator's tasks. We propose that the response times be used in conjunction with measured speed-accuracy curves to estimate the operator error rates contributing to mission failure. Such analyses should be especially important in the estimation of error rates in off-nominal situations.

probability

Hazard Perception & Avoidance (HPA): Part Task 1 - Results Outbrief

n April 2022, the Human Autonomy Teaming Lab (NASA Ames Research Center) conducted a manned, human-in-the-loop (HITL) simulation. This part task HITL began the lab’s Hazard Perception and Avoidance (HPA) technical work under NASA’s Advanced Air Mobility (AAM), Automated Flight and Contingency Management (AFCM) Sub-Project. The goals were to assess levels of automation for manned, electric vertical takeoff and landing (eVTOL) aircraft. This simulation tested manual and automated Resolution Advisory (RA) responses and return-to-course (RTC) maneuvers for the first version of the Airborne Collision Avoidance System’s (ACAS) rotary-wing (Xr) variant. This was conducted on a fixed-based simulator designed to fly eVTOL aircraft while maneuvering for intruding traffic. Variables for this study included levels of autonomy (i.e., manual and automated) as well as the types of alerts at the onset of conflicts (i.e., Corrective and RA). The data collected included response times, losses of well clear, and maneuver sizes and durations as well as subjective ratings like acceptability, and perceived workload. Additional details and future anticipations are also discussed.

air taxis

ACAS Xr Part Task Sim, Preliminary Experiment Design

In early 2022, the Human Autonomy Teaming Lab (NASA Ames Research Center) will conduct a manned, human-in-the-loop (HITL) simulation. This part task HITL will begin the lab’s Hazard Perception and Avoidance (HPA) technical work under NASA’s Advanced Air Mobility (AAM), Automated Flight and Contingency Management (AFCM) Sub-Project. The goals will be to assess levels of automation for manned, electric vertical takeoff and landing (eVTOL) aircraft. This simulation will test manual and automated Resolution Advisory (RA) responses and return-to-course (RTC) maneuvers for the first version of the Airborne Collision Avoidance System’s (ACAS) rotary-wing (Xr) variant. This will be conducted on a fixed-based simulator designed to fly eVTOL aircraft while maneuvering for intruding traffic. Variables for this study include levels of autonomy (i.e., manual and automated) as well as the types of alerts at the onset of conflicts (i.e., Corrective and RA). The data collected will include response times, losses of well clear, and maneuver sizes and durations as well as subjective ratings like acceptability, perceived workload, and meaningful human control. Additional details and future anticipations are also discussed.

air taxis

SARDA HITL #6 Simulation: System Performance Analysis (Initial Results)

This presentation discusses the results of the 6th human-in-the-loop simulation of the Spot and Runway Departure Advisor (SARDA). SARDA is a research prototype decision support tool for both Air Traffic Control Tower controllers and airlines ramp controllers to assit in controlling surface traffic of a busy airport. Data was collected during the human-in-the-loop simulations of SARDA for ramp operatoins at Charlotte International Airport (Oct-Nov 2014). The performance results include taxi time, taxi delay, total delay, and runway usage.

surface management

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

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

Airport surface scheduling