Dynamic Airspace Configuration

In air traffic management systems, airspace is partitioned into regions in part to distribute the tasks associated with managing air traffic among different systems and people. These regions, as well as the systems and people allocated to each, are changed dynamically so that air traffic can be safely and efficiently managed. It is expected that new air traffic control systems will enable greater flexibility in how airspace is partitioned and how resources are allocated to airspace regions. In this talk, I will begin by providing an overview of some previous work and open questions in Dynamic Airspace Configuration research, which is concerned with how to partition airspace and assign resources to regions of airspace. For example, I will introduce airspace partitioning algorithms based on clustering, integer programming optimization, and computational geometry. I will conclude by discussing the development of a tablet-based tool that is intended to help air traffic controller supervisors configure airspace and controllers in current operations

Comparing Methods for Dynamic Airspace Configuration

This paper compares airspace design solutions for dynamically reconfiguring airspace in response to nominal daily traffic volume fluctuation. Airspace designs from seven algorithmic methods and a representation of current day operations in Kansas City Center were simulated with two times today's demand traffic. A three-configuration scenario was used to represent current day operations. Algorithms used projected unimpeded flight tracks to design initial 24-hour plans to switch between three configurations at predetermined reconfiguration times. At each reconfiguration time, algorithms used updated projected flight tracks to update the subsequent planned configurations. Compared to the baseline, most airspace design methods reduced delay and increased reconfiguration complexity, with similar traffic pattern complexity results. Design updates enabled several methods to as much as half the delay from their original designs. Freeform design methods reduced delay and increased reconfiguration complexity the most

Overview of Dynamic Airspace Configuration

The Dynamic Airspace Configuration (DAC) research focus area has three topics: Restructured Airspace, Adaptable Airspace, and Generic Airspace. The first part of the presentation explains these topics and provides an overview of research activities under each of these topics. The second part of this presentation explains how the following presentations are related to each other and how they contribute to the overall DAC research focus area. The presentation flow proceeds from high-level fast-time simulation analysis to more detailed human-in-the-loop simulation analysis and integration considerations

Improvements in sectorization optimizer within the dynamic airspace configuration concept

The Dynamic Airspace Configuration (DAC) concept goal is to create more flexible airspace designs and configurations as a way to improve airspace use and absorb the increasing traffic demand. This modifies the conception of an airspace comprised of fixed volumes to one using a bigger quantity of building blocks and/or flexible boundaries. This translates into an increase in the number of possible configurations, which results in the creation of a large-scale optimization problem. To tackle this problem, CRIDA developed a sectorization optimizer ¿SECTORIA¿ which ranks all the possible configurations for a defined time and airspace in terms of occupancy and selects the optimal one. The aim of this thesis is to progress the tool in order to bring it closer to an operational environment. In particular, an improvement in its capacity constraint is added in order to obtain optimal solutions in all cases. A post-processing tool is also developed to refine the optimizer results and obtain more accurate configuration plans. The validation of these improvements is then carried out in two use cases: current sectorization in Madrid ACC Route 1 and a DAC sectorization of the same airspace, considering the addition of vertical cuts. The samples of traffic used are corresponding to the dates June 22nd, July 3rd, August 3rd and February 13th, 2019. The first feature is validated comparing the results obtained with and without the improvement, whereas the post-processing tool results are compared with the configuration plan opened with the current airspace design on the days of study and with a blind test performed on a Subject Matter Expert (SME). Results from the first validation show a better handling of inevitable overload, leading SECTORIA to provide more feasible solutions. On the other hand, the post-processing tool shows promising results, showing improvements in ATC hours which can reach up to 30\%. Conclusions and future work are then described following the analysis of the results

A Human-in-the Loop Exploration of the Dynamic Airspace Configuration Concept

An exploratory human-in-the-loop study was conducted to better understand the impact of Dynamic Airspace Configuration (DAC) on air traffic controllers. To do so, a range of three progressively more aggressive algorithmic approaches to sectorizations were chosen. Sectorizations from these algorithms were used to test and quantify the range of impact on the controller and traffic. Results show that traffic count was more equitably distributed between the four test sectors and duration of counts over MAP were progressively lower as the magnitude of boundary change increased. However, taskload and workload were also shown to increase with the increase in aggressiveness and acceptability of the boundary changes decreased. Overall, simulated operations of the DAC concept did not appear to compromise safety. Feedback from the participants highlighted the importance of limiting some aspects of boundary changes such as amount of volume gained or lost and the extent of change relative to the initial airspace design

User Involvement in the Design of ML-Infused Systems

Advances in machine learning (ML) open up possibilities for better supporting the decision making that occurs in high-stakes domains such as air traffic management (ATM). The success of such decision-making systems highly depends upon end users’ involvement in their development process. However, most designers face challenges with finding appropriate ways of doing this. This paper presents our ongoing work to investigate design practices by reporting lessons learned from user involvement in the development of an ML-infused ATM decision support system. To explore if and how UX design methods need to be refined when working with ML as a design material, we conducted an online study with domain experts consisting of three iterations. The paper reports the main challenges we faced and our actions to overcome them. Our results can be useful to other designers working with ML-infused systems.acceptedVersio

Operational Dynamic Configuration Analysis

Sectors may combine or split within areas of specialization in response to changing traffic patterns. This method of managing capacity and controller workload could be made more flexible by dynamically modifying sector boundaries. Much work has been done on methods for dynamically creating new sector boundaries [1-5]. Many assessments of dynamic configuration methods assume the current day baseline configuration remains fixed [6-7]. A challenging question is how to select a dynamic configuration baseline to assess potential benefits of proposed dynamic configuration concepts. Bloem used operational sector reconfigurations as a baseline [8]. The main difficulty is that operational reconfiguration data is noisy. Reconfigurations often occur frequently to accommodate staff training or breaks, or to complete a more complicated reconfiguration through a rapid sequence of simpler reconfigurations. Gupta quantified a few aspects of airspace boundary changes from this data [9]. Most of these metrics are unique to sector combining operations and not applicable to more flexible dynamic configuration concepts. To better understand what sort of reconfigurations are acceptable or beneficial, more configuration change metrics should be developed and their distribution in current practice should be computed. This paper proposes a method to select a simple sequence of configurations among operational configurations to serve as a dynamic configuration baseline for future dynamic configuration concept assessments. New configuration change metrics are applied to the operational data to establish current day thresholds for these metrics. These thresholds are then corroborated, refined, or dismissed based on airspace practitioner feedback. The dynamic configuration baseline selection method uses a k-means clustering algorithm to select the sequence of configurations and trigger times from a given day of operational sector combination data. The clustering algorithm selects a simplified schedule containing k configurations based on stability score of the sector combinations among the raw operational configurations. In addition, the number of the selected configurations is determined based on balance between accuracy and assessment complexity

Effect of Dynamic Sector Boundary Changes on Air Traffic Controllers

The effect of dynamic sector boundary changes on air traffic controller workload was investigated with data from a human-in-the-loop simulation. Multiple boundary changes were made during simulated operations, and controller rating of workload was recorded. Analysis of these data showed an increase of 16.9% in controller workload due to boundary changes. This increased workload was correlated with the number of aircraft handoffs and change in sector volume. There was also a 12.7% increase in average workload due to the changed sector design after boundary changes. This increase was correlated to traffic flow crossing points getting closer to sector boundaries and an increase in the number of flights with short dwell time in a sector. This study has identified some of the factors that affect controller workload when sector boundaries are changed, but more research is needed to better understand their relationships

User Selection Criteria of Airspace Designs in Flexible Airspace Management

A method for identifying global aerodynamic models from flight data in an efficient manner is explained and demonstrated. A novel experiment design technique was used to obtain dynamic flight data over a range of flight conditions with a single flight maneuver. Multivariate polynomials and polynomial splines were used with orthogonalization techniques and statistical modeling metrics to synthesize global nonlinear aerodynamic models directly and completely from flight data alone. Simulation data and flight data from a subscale twin-engine jet transport aircraft were used to demonstrate the techniques. Results showed that global multivariate nonlinear aerodynamic dependencies could be accurately identified using flight data from a single maneuver. Flight-derived global aerodynamic model structures, model parameter estimates, and associated uncertainties were provided for all six nondimensional force and moment coefficients for the test aircraft. These models were combined with a propulsion model identified from engine ground test data to produce a high-fidelity nonlinear flight simulation very efficiently. Prediction testing using a multi-axis maneuver showed that the identified global model accurately predicted aircraft responses

Airspace Systems Program - NextGen Concepts and Technology Development (CTD) Project NextGen Systems Analysis, Integration, and Evaluation (SAIE) Project

Overview presentation of the Airspace Systems Program two NextGen projects