Engineering topics

Smith, Nancy M.

Publications and source records attributed to Smith, Nancy M..

13 records

NASA NTRS2018

Required Time of Arrival as a Control Mechanism to Mitigate Uncertainty in Arrival Traffic Demand Management

The objective of this study is to explore the use of Required Time of Arrival (RTA) capability on the flight deck as a control mechanism on arrival traffic management to improve traffic delivery accuracy by mitigating the effect of traffic demand uncertainty. The uncertainties are caused by various factors, such as departure error due to the difference between scheduled departure and the actual take-off time. A simulation study was conducted using the Multi Aircraft Control System (MACS) software, a comprehensive research platform developed in the Airspace Operations Laboratory (AOL) at NASA Ames Research Center. The Crossing Time (CT) performance (i.e. the difference between target crossing time and actual crossing time) of the RTA for uncertainty mitigation during cruise phase was evaluated under the influence of varying two main factors: wind severity (heavy wind vs. mild wind), and wind error (1 hour, 2 hours, and 5 hours wind forecast errors). To examine the CT performance improvement made by the RTA, the comparison to the CT of the aircraft that were not assigned with RTA (Non-RTA) under the influence of the selected factors was also made. The Newark Liberty International Airport (EWR) was chosen for this study. A total 66 inbound traffic to the EWR (34 of them were airborne when the simulation was initiated, 32 were pre-departures at that time) was simulated, where the pre-scripted departure error was assigned to each pre-departure (61 conform to their Expected Departure Clearance Time, which is +-300 seconds of their scheduled departure time). The results of the study show that the delivery accuracy improvement can be achieved by assigning RTA, regardless of the influence of the selected two factors (the wind severity and the wind information inaccuracy). Across all wind variances, 66.9 (265 out of 396) of the CT performance of the RTA assigned aircraft was within +- 60 seconds (i.e. target tolerance range) and 88.9 (352 out of 396) aircraft met +-300 seconds marginal tolerance range, while only 33.6 (133 out of 396) of the Non-RTA assigned aircrafts CT performance achieved the target tolerance range and 75.5 (299 out of 396) stayed within the marginal. Examination of the impact of different error sources i.e. departure error, wind severity, and wind error suggest that although large departure errors can significantly impact the CT performance, the impacts of wind severity and errors were modest relative the targeted +- 60 second conformance range.

required time of arrival (RTA)↗

NASA NTRS2018

Evaluation of Multiple Flow Constrained Area Capacity Setting Methods for Collaborative Trajectory Options Program

The purpose of this study was to compare flow constrained area (FCA) capacity setting methods for Collaborative Trajectory Options Program (CTOP) as they pertain to the Integrated Demand Management (IDM) concept. IDM uses flow balancing to manage air traffic across multiple FCAs with a common downstream constraint, as well as constraints at the respective FCA locations. FCA capacity rates can be set manually, but generating capacities for multiple, interdependent FCAs could potentially over-burden a user. A new enhancement to CTOP called the FCA Balance Algorithm (FBA) was developed at NASA Ames Research Center to improve the process of allocating capacity across multiple flow constrained segments in the airspace. The FBA evaluates the predicted demand and capacity across multiple FCAs and dynamically generates capacity settings for the FCAs that best meet capacity limits for all identified constraints. In a human-in-the-loop simulation study, both manual and automated capacity setting methods were evaluated in terms of their overall feasibility using measures of system performance, human performance, and qualitative feedback. Subject matter experts were asked to use three different methods to allocate capacity to three FCAs, either (1) by manually setting capacity for every 60-minute time window, (2) by manually setting capacity for every 15-minute time window, or (3) by using the FBA capability to automatically generate capacity settings. Results showed no significant differences in terms of overall system performance, indicated by similar ground delay and airport throughput numbers between methods. However, differences in individual strategies afforded by the manual methods allowed some participants to achieve system-wide delay that was much lower than the average. The FBA was the fastest method of capacity setting, and it received the lowest subjective rating scores on physical task load, mental task load, task difficulty and task complexity out of the three methods. Finally, participants explained through qualitative feedback that there were many benefits to using the FBA, such as ease of use, accuracy, and low risk of human input error. Participants did not experience the same limitations with the FBA that they did with the manual methods, such as reduced accuracy in the 60-minute manual condition, or high complexity in the 15-minute/manual condition. These results suggest that the FBA automation enhancement to CTOP maintains system performance while improving human performance. Therefore, the FBA could be introduced as a way to mitigate operator workload while planning a CTOP.

NextGen↗

NASA NTRS2018

Evaluation of Multiple Flow Constrained Area Capacity Setting Methods for Collaborative Trajectory Options Program

NextGen↗

NASA NTRS2018

Impact of Different Trajectory Option Set Participation Levels within an Air Traffic Management Collaborative Trajectory Option Program

This paper presents the methodology and results of a Human-In-The-Loop (HITL) simulation study conducted in the Airspace Operations Laboratory at NASA Ames Research Center. This study is a part of NASA's ongoing research into developing an Integrated Demand Management (IDM) concept, whose aim is to improve traffic flow management (TFM) by coordinating the FAA's strategic Traffic Flow Management System (TFMS) with its more tactical Time-Based Flow Management (TBFM) system. The purpose of TFM is to regulate air traffic demand so that it is delivered efficiently through constrained airspace resources without exceeding their capacity limits. The IDM concept leverages a new TFMS capability called the Collaborative Trajectory Options Program (CTOP) to strategically pre-condition traffic demand flowing into a TBFM-managed arrival environment, where TBFM is responsible for managing traffic tactically by generating precise arrival schedules. Unlike other TFM tools, CTOP gives flight operators the option of submitting a set of user-preferred alternative trajectories for each flight. CTOP can then use these trajectory option sets (or TOSs) to find user-preferred alternative routes to reduce demand on an overloaded resource. CTOP's effectiveness in redistributing demand is limited, however, by the availability of flights with alternative routes. The research presented in this paper focuses on evaluating the impact on TFM operations by varying the percentage of flights that submit a multiple-option TOS ('TOS participation levels'). Results show the impact on overall system performance and on the rerouted flights themselves. The simulation used a Newark (EWR) airport arrival scenario, with en route weather affecting traffic inbound from the west. Participants were asked to control each of the three arrival flows (north, west, and south) to meet their individual capacity constraints while simultaneously ensuring efficient utilization of the capacity at the destination airport. A large, permeable convective weather cell located southeast of Chicago severely reduced the capacity of the west flow. The study evaluated the impact of five different TOS participation levels on CTOP's ability to re-allocate traffic from the west and improve TFM performance in terms of delay assignment and traffic delivery rate to the airport. Overall, the results showed that increasing TOS submissions allowed the overall system delays to be reduced and fairly distributed among the three arrival flows, at the same time achieving the airport throughput rate. Moreover, it was found that aircraft who submitted a TOS saw a greater reduction in delay, even when they were assigned longer routes. This was particularly true when fewer aircraft submitted a TOS. The results confirm that the CTOP operations with higher TOS participation levels helped utilize the overall National Airspace System (NAS) resources as well as benefited the users who participated.

Yoo, Hyo-Sang↗

NASA NTRS2017

Evaluation of Integrated Demand Management Looking into Strategic & Tactical Flow Management

The motivation behind Integrated Demand Management (IDM) research is to explore possible improvements to United States National Airspace System (NAS) performance that could be realized through procedural integration of strategic traffic flow management capabilities, such as the Collaborative Trajectory Options Program (CTOP), and tactical capabilities, such as Time Based Flow Management (TBFM). An initial IDM concept for clear weather operations was developed and evaluated for potential benefits, including efficiency, delay reduction, predictability and throughput, and to identify any major issues that might represent a showstopper for a fielded application. Newark Liberty International Airport (EWR) arrival operations provided a use case for concept development. EWR uses miles-in-trail (MIT) metering to regulate demand into TBFM during high volume operations, and short-haul flights are often penalized with excessive, last-minute ground delays when the overhead stream is saturated. IDM addresses this problem by replacing MIT conditioning with CTOP to better manage the demand delivery to the TBFM entry points. A quasi-real time high-fidelity simulation that would normally involve participants was conducted using heuristic-based procedures that mimicked operators behaviors instead. Five total conditions were compared: two baseline conditions with MIT delivery to TBFM entry points using two different TBFM settings; and three IDM conditions: one with airborne speed control using an Required Time of Arrival (RTA) capability, a second without RTA, and a third with no wind forecast errors. Results suggest that the IDM concept can deliver traffic more efficiently by shifting the delays from airborne to ground for both RTA and non-RTA conditions, while maintaining a target throughput rate. The results also suggest that with good predictability of airport capacity, excessive TBFM ground delay can be minimized by applying more strategic CTOP delay, increasing predictability for the airline operators. Overall, the results indicate that the implementation of an IDM concept under clear weather conditions can improve NAS system performance. Future IDM research aims to expand the concept to address demandcapacity imbalance d severe weather.

Time-Based Flow Management (TBFM)↗

NASA NTRS2017

Integrated Demand Management: Minimizing Unanticipated Excessive Departure Delay while Ensuring Fairness from a Traffic Management Initiative

This paper introduces NASA's Integrated Demand Management (IDM) concept and presents the results from an early proof-of-concept evaluation and an exploratory experiment. An initial development of the concept was focused on integrating two systems - i.e. the FAA's newly deployed Traffic Flow Management System (TFMS) tool called the Collaborative Trajectory Options Program (CTOP) and the Time-Based Flow Management (TBFM) system with Extended Metering (XM) capabilities to manage projected heavy traffic demand into a capacity-constrained airport. A human-in-the-loop (HITL) simulation experiment was conducted to demonstrate the feasibility of the initial development of the concept by adapting it to an arrival traffic problem at Newark Liberty International Airport (EWR) during clear weather conditions. In this study, the CTOP was utilized to strategically plan the arrival traffic demand by controlling take-off times of both short- and long-haul flights (long-hauls specify aircraft outside TBFM regions and short-hauls specify aircraft within TBFM regions) in a way that results in equitable delays among the groups. Such strategic planning allows less airborne delay to occur within TBFM by feeding manageable long-haul traffic demand while reserving sufficient slots in the overhead streams for the short-haul departures. The manageable traffic demand indicates the TBFM scheduler assigns no more airborne delay than its assigned airspace is capable of absorbing. TBFM then uses its time-based metering capabilities to deliver the desirable throughput by tactically rescheduling the TBFM entered long-haul flights and short-haul departures. Additional research was also performed to explore use of Required Time of Arrival (RTA) capabilities as a potential control mechanism for the airborne flights to improve arrival traffic delivery accuracy of scheduled long-haul traffic demand. The study results show that both short- and long-haul flights received similar ground delays. In addition, there was a noticeable reduction in the total amount of excessive unanticipated last-minute ground delays, i.e. delays that are frequently imposed on the short-haul flight in current day operations due to saturation in the overhead stream, commonly referred to as 'double penalty'. Furthermore, the concept achieved the target throughput while minimizing the expected cost associated with overall delays in arrival traffic. Assessment of the RTA capabilities showed that there was indeed improvement of the scheduled entry times into TBFM regions by using RTA capabilities. However, with respect to reduction in delays incurred within TBFM, there was no observable benefit of improving the precision of long-haul flights entry times.

Collaborative Trajectory Options Program (CTOP)↗

NASA NTRS2016

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↗

NASA NTRS2015

Tool-Enabled Changes in Terminal Air Traffic Controller Task and Workload Distribution

A human-in-the-loop simulation was conducted that examined an approach to adjusting airport arrivals in such a way as to enable higher departure throughput while maintaining arrival throughput. This approach, referred to as Departure-Sensitive Arrival Spacing (DSAS), leverages the capabilities of the Terminal Sequencing and Spacing (TSS) system with an additional Decision Support Tool (DST) to assign more precise arrival spacing interval. This presentation will focus on the changes observed in the task distribution and control strategies among three Terminal Radar Approach Control (TRACON) controller participants and the workload associated with those changes across three conditions: Baseline, TSS, and DSAS. Results showed that the application of the DSTs in the TSS condition and those in the DSAS condition enabled a progressive reduction in the number of clearances issued and for the clearances to be issued earlier, further away from the airport. In doing so, there was also a greater use of speed control versus vectoring and altitude for schedule conformance in the TSS and DSAS conditions respectively and relative to Baseline. Workload also shifted in conjunction with the clearance distribution with an overall reduction across conditions. The changes in task and workload distribution enabled a shift from tactical to strategic control, which allowed for a more predictable and efficient delivery of arrivals.

task↗

NASA NTRS2014

A Novel Application of TMA to Converging Runway Operations in a Simulated NextGen Environment

In 2013, the Airspace Operations Laboratory at NASA Ames Research Center conducted a human-in-the-loop simulation that examined the feasibility of applying a number of Next Generation Air Transportation System (NextGen) solutions to complex arrival operations in and around the New York metroplex. The delivery of arrivals to Newark Liberty International Airport (EWR) was the focus of this simulation, which involved extending the Terminal Sequencing and Spacing (TSS) scheduling capability to precisely schedule arrivals to intersecting runways 22 Left and 11. An important enabler for the concept was the availability of a dependent runway scheduler that was able to coordinate arrival times between aircraft landing on intersecting runways. At the time of the study, there was no functionality within the TSS scheduler to automatically create the dependent runway schedules. Instead, a Traffic Management Coordinator (TMC) manually created a de-conflicted schedule, which allowed for the concept to be tested as well as provided valuable insight into the tool requirements for a dependent runway scheduler. Throughout the course of preparations for the simulation, the individual serving as the TMC developed a number of strategies and procedures for manually adjusting the Scheduled Time of Arrival (STA) of the EWR arrivals in order to ensure that adequate spacing was provided between runway 22L and 11 arrival pairs. This paper describes the strategies and procedures that were developed and details how they were successfully applied during the simulation. Results will also be presented that shed additional light on exactly how the schedules were manipulated and their impact on delivery performance and safety. Ideas for additional TSS enhancements and next steps, based on participant feedback, will also be presented.

Homola, Jeffrey↗

NASA NTRS2014

An Overview of Current Capabilities and Research Activities in the Airspace Operations Laboratory at NASA Ames Research Center

The Airspace Operations Laboratory at NASA Ames conducts research to provide a better understanding of roles, responsibilities, and requirements for human operators and automation in future air traffic management (ATM) systems. The research encompasses developing, evaluating, and integrating operational concepts and technologies for near-, mid-, and far-term air traffic operations. Current research threads include efficient arrival operations, function allocation in separation assurance and efficient airspace and trajectory management. The AOL has developed powerful air traffic simulation capabilities, most notably the Multi Aircraft Control System (MACS) that is used for many air traffic control simulations at NASA and its partners in government, academia and industry. Several additional NASA technologies have been integrated with the AOL's primary simulation capabilities where appropriate. Using this environment, large and small-scale system-level evaluations can be conducted to help make near-term improvements and transition NASA technologies to the FAA, such as the technologies developed under NASA's Air Traffic Management Demonstration-1 (ATD-1). The AOL's rapid prototyping and flexible simulation capabilities have proven a highly effective environment to progress the initiation of trajectory-based operations and support the mid-term implementation of NextGen. Fundamental questions about accuracy requirements have been investigated as well as realworld problems on how to improve operations in some of the most complex airspaces in the US. This includes using advanced trajectory-based operations and prototype tools for coordinating arrivals to converging runways at Newark airport and coordinating departures and arrivals in the San Francisco and the New York metro areas. Looking beyond NextGen, the AOL has started exploring hybrid human/automation control strategies as well as highly autonomous operations in the air traffic control domain. Initial results indicate improved capacity, low operator workload, good situation awareness and acceptability for controllers teaming with autonomous air traffic systems. While much research and development needs to be conducted to make such concepts a reality, these approaches have the potential to truly transform the airspace system towards increased mobility, safe and efficient growth in global operations and enabling many of the new vehicles and operations that are expected over the next decades. This paper describes how the AOL currently contributes to the ongoing air transportation transformation.

simulation↗

NASA NTRS2013

NextGen Operations in a Simulated NY Area Airspace

A human-in-the-loop simulation conducted in the Airspace Operations Laboratory (AOL) at NASA Ames Research Center explored the feasibility of a Next Generation Air Transportation System (NextGen) solution to address airspace and airport capacity limitations in and around the New York metropolitan area. A week-long study explored the feasibility of a new Optimal Profile Descent (OPD) arrival into the airspace as well as a novel application of a Terminal Area Precision Scheduling and Spacing (TAPSS) enhancement to the Traffic Management Advisor (TMA) arrival scheduling tool to coordinate high volume arrival traffic to intersecting runways. In the simulation, four en route sector controllers and four terminal radar approach control (TRACON) controllers managed traffic inbound to Newark International Airport's primary runway, 22L, and its intersecting overflow runway, 11. TAPSS was used to generate independent arrival schedules for each runway and a traffic management coordinator participant adjusted the arrival schedule for each runway 11 aircraft to follow one of the 22L aircraft. TAPSS also provided controller-managed spacing tools (slot markers with speed advisories and timelines) to assist the TRACON controllers in managing the arrivals that were descending on OPDs. Results showed that the tools significantly decreased the occurrence of runway violations (potential go-arounds) when compared with a Baseline condition with no tools. Further, the combined use of the tools with the new OPD produced a peak arrival rate of over 65 aircraft per hour using instrument flight rules (IFR), exceeding the current maximum arrival rate at Newark Liberty International Airport (EWR) of 52 per hour under visual flight rules (VFR). Although the participants rated the workload as relatively low and acceptable both with and without the tools, they rated the tools as reducing their workload further. Safety and coordination were rated by most participants as acceptable in both conditions, although the TRACON Runway Coordinator (TRC) rated neither as acceptable in the Baseline condition. Regarding the role of the TRC, the two TRACON controllers handling the 11 arrivals indicated that the TRC was very much needed in the Baseline condition without tools, but not needed in the condition with tools. This indicates that the tools were providing much of the sequencing and spacing information that the TRC had supplied in the Baseline condition.

NextGen↗

NASA NTRS2010

Ground-Ground Data Communication-Assisted Planning and Coordination: Shorter Verbal Communications

A human-in-the-loop simulation was conducted to investigate the operational feasibility, technical requirements, and potential improvement in airspace efficiency of adding a Multi-Sector Planner position. A subset of the data from that simulation is analyzed here to determine the impact, if any, of ground-ground data communication (Data Comm) on verbal communication and coordination for multi-sector air traffic management. The results suggest that the use of Data Comm significantly decreases the duration of individual verbal communications. The results also suggest that the use of Data Comm, as instantiated in the current simulation, does not obviate the need for accompanying voice calls.

Kessell, Angela Mary↗

NASA NTRS2010

Managing Demand and Capacity Using Multi-Sector Planning and Flexible Airspace: Human-in-the-Loop Evaluation of NextGen

When demand for an airspace sector exceeds capacity, the balance can be re-established by reducing the demand, increasing the capacity, or both. The Multi-Sector Planner (MSP) concept has been proposed to better manage traffic demand by modifying trajectories across multiple sectors. A complementary approach to MSP, called Flexible Airspace Management (FAM), reconfigures the airspace such that capacity can be reallocated dynamically to balance the traffic demand across multiple sectors, resulting in fewer traffic management initiatives. The two concepts have been evaluated with a series of human-in-the-loop simulations at the Airspace Operations Laboratory to examine and refine the roles of the human operators in these concepts, as well as their tools and procedural requirements. So far MSP and FAM functions have been evaluated individually but the integration of the two functions is desirable since there are significant overlaps in their goals, geographic/temporal scope of the problem space, and the implementation timeframe. Ongoing research is planned to refine the humans roles in the integrated concept.

Lee, Paul U.↗