d5.3 final project results reportcotton-er.eu/results/cotton d5.3 final project results...d5.3 final...

D5.3 Final Project Results Report

Deliverable ID: D5.3

Dissemination Level: PU

Project Acronym: COTTON

Grant: 783222 Call: H2020-SESAR-2016-2

Topic: ER3-03-2016 "Optimised ATM Network Services TBO"

Consortium Coordinator: CRIDA Edition date: 5 December 2019 Edition: 00.01.00 Template Edition: 02.00.01

EXPLORATORY RESEARCH

D5.3 FINAL PROJECT RESULTS REPORT

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Authoring & Approval

Authors of the document

Name/Beneficiary Position/Title Date

Eva María Puntero Parla /CRIDA Project Coordinator 05/12/2019

Natividad Valle Fernández/CRIDA Project Contributor 05/12/2019

Marta Sánchez Cidoncha/CRIDA Project Contributor 26/11/2019

Fernando Gómez Comendador / UPM Project Member 19/10/2019

Tobias Finck/ DLR Project Contributor 28/10/2019

Andrija Vidosavljevic / ENAC Project Member 20/11/2019

Reviewers internal to the project


Bernard Van den Berg Project Contributor 03/12/2019

Rejected By - Representatives of beneficiaries involved in the project


Document History

Edition Date Status Author Justification

00.00.01 25/10/2019 Draft E. Puntero First draft

00.00.02 29/11/2019 Final Draft E. Puntero/N. Valle Include Partners contribution

Approved for submission to the SJU By - Representatives of beneficiaries involved in the project


Eva María Puntero Parla / CRIDA Project Coordinator

10/12/2019

Fernando Gómez Comendador / UPM UPM Representative 10/12/2019

Andrija Vidosavljevic / ENAC ENAC Representative 10/12/2019

Clark Borst / TU Delft TUD Representative 10/12/2019

Leila Zerrouki / Eurocontrol EUROCONTROL Representative 10/12/2019

Tobias Finck/ DLR DLR Representative 10/12/2019


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00.01.00 05/12/2019 Final E.Puntero Include review comments. Final version for approval.

© – 2019 – COTTON Consortium. All rights reserved. Licensed to the SESAR Joint

Undertaking under conditions.


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COTTON CAPACITY OPTIMISATION FOR TRAJECTORY BASED OPERATIONS

This document is part of a project that has received funding from the SESAR Joint Undertaking under grant agreement No 783222 under European Union’s Horizon 2020 research and innovation programme.

Abstract

This document summarises COTTON developed activities and results. It presents the evidence of COTTON Solutions benefits and operational feasibility. Complementarily, it presents their technological risks and a preliminary Plan for next R&D phases.


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Table of Contents

1 Executive Summary .................................................................................................... 8

2 Project Overview ...................................................................................................... 10

2.1 Operational/Technical Context .................................................................................... 10 2.1.1 Trajectory-based operations, an opportunity to improve uncertainty management..................... 10 2.1.2 Complexity, an enabler for the Capacity Management efficiency .................................................. 10 2.1.3 New Capacity Management requiring adapted complexity approaches ........................................ 11

2.2 Project Scope and Objectives ....................................................................................... 12 2.2.1 Objectives ........................................................................................................................................ 12 2.2.2 Scope and Methodology ................................................................................................................. 13

2.3 Work Performed .......................................................................................................... 15 2.3.1 Complexity Metrics Development ................................................................................................... 15 2.3.2 FCA Capacity Management Process Enhancement ......................................................................... 17 2.3.3 DAC Capacity Management Process Enhancement ........................................................................ 18 2.3.4 Integrated DAC/FCA Capacity Management Process Development ............................................... 19

2.4 Key Project Results ...................................................................................................... 20 2.4.1 Complexity Metrics ......................................................................................................................... 20 2.4.2 FCA Capacity Management Process ................................................................................................ 31 2.4.3 DAC Capacity Management Process ............................................................................................... 33 2.4.4 Integrated DAC/FCA Capacity Management Process ...................................................................... 38

2.5 Technical Deliverables ................................................................................................. 43

3 Links to SESAR Programme ....................................................................................... 45

3.1 Contribution to the ATM Master Plan ........................................................................... 45

3.2 Maturity Assessment ................................................................................................... 46

4 Conclusion and Lessons Learned ............................................................................... 50

4.1 Conclusions ................................................................................................................. 50 4.1.1 Complexity Metrics ......................................................................................................................... 50 4.1.2 Enhanced Capacity Management Process ...................................................................................... 50 4.1.3 Concept Benefits Assessment ......................................................................................................... 52

4.2 Technical Lessons Learned ........................................................................................... 53 4.2.1 Complexity Metrics ......................................................................................................................... 53 4.2.2 Capacity Management Process ....................................................................................................... 56 4.2.3 Concept Benefits Assessment ......................................................................................................... 57

4.3 Plan for next R&D phase (Next steps) ........................................................................... 58

5 References ............................................................................................................... 60

5.1 Project Deliverables ..................................................................................................... 60

5.2 Project Publications ..................................................................................................... 60

5.3 Other .......................................................................................................................... 61

A.1 Acronyms and Terminology ......................................................................................... 62


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List of Tables Table 1. List of categories and its complexity generators ..................................................................... 20

Table 2. Complexity generator cross-analysis ....................................................................................... 23

Table 3. Selected complexity metric application .................................................................................. 24

Table 4. Convolution of an ATCO task Matrix. ...................................................................................... 30

Table 5. Convolution metric matrix results. .......................................................................................... 30

Table 6: FCA Validation Exercise Summary of Results per KPA ............................................................. 32

Table 7: DAC Validation Exercise Summary of Results per KPA ............................................................ 35

Table 8: Integrated DAC/FCA Validation Exercise Summary of Results per KPA .................................. 41

Table 9: Project Deliverables ................................................................................................................. 43

Table 10: Project Maturity .................................................................................................................... 46

Table 11: ER/ IR Maturity Assessment .................................................................................................. 47

Table 12: Acronyms and technology ..................................................................................................... 64

List of Figures Figure 1: COTTON Technical Work Packages and Tasks ........................................................................ 14

Figure 2: COTTON Methodology ........................................................................................................... 15

Figure 3. Human Performance Test overview ....................................................................................... 17

Figure 4: Bayesian network for assessing Complexity Uncertainty under DAC operational framework in the short-term horizon .......................................................................................................................... 22

Figure 5. Aircraft solution space area.................................................................................................... 24

Figure 6. Correlation results of the scenarios with structured routes and without no-fly zone. ......... 25

Figure 7. Probability Distribution of Cognitive Complexity at time t. ................................................... 26

Figure 8. Complexity values with and without demand uncertainty .................................................... 27

Figure 9. CC and GC predictability comparison for sector TERI T-12 hours forecast horizon and without demand uncertainty .............................................................................................................................. 27

Figure 10. CC and GC predictability comparison for sector TERI T-12 hours forecast horizon and with demand uncertainty .............................................................................................................................. 28


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Figure 11. Comparison between actual CC and GC for sector TERI ...................................................... 28

Figure 12. Sensors of human response, parameters and their theoretical evolution with increasing activity ................................................................................................................................................... 29

Figure 13. Scenarios together with Complexity Generators Complexity Metric value. ........................ 30

Figure 14: Enhanced FCA Capacity Management proposed by COTTON.............................................. 31

Figure 15: Enhanced DAC Capacity Management proposed by COTTON ............................................. 33

Figure 16: Enhanced DAC/FCA Capacity Management proposed by COTTON ..................................... 39

Figure 17. COTTON possible Plan for next R&D phases. ....................................................................... 59


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1 Executive Summary

The main purpose of COTTON project is to provide enhanced capacity management solutions based on complexity and including uncertainty in its calculation. This innovative approach contrasts with the actual way of calculating a sector’s capacity, where the controllers’ workload and uncertainty are assessed relying on the number of entries and occupancies of the sector and human experience. In addition, COTTON operational context considers a Trajectory-Based Operations environment, simultaneously with Dynamic Airspace Configuration (DAC), Flight Centric ATC (FCA) and DAC/FCA integrated environments.

To achieve its goal, COTTON project have implemented different activities, from the analysis of the existing complexity metrics, identifying the requirements to be adapted to DAC and FCA concepts, to the definition of the use cases and execution of various fast time simulation exercises with the developed models. As a result, the algorithm computations of three selected metrics have been developed and validated to improve Capacity Management effectiveness.

This deliverable presents the results accomplished during the two years of COTTON project. It gathers the conclusions of COTTON deliverables, putting them altogether in order to have a common perspective.

In general, COTTON metric development activities are considered successful. Of the three proposed complexity metrics, they are improved to be taken into account the requirements from the different capacity management solutions (DAC and FCA). In fact, the Solution Space metric considers the most influencing Complexity Generator, which is the predicted conflicts. The Geometrical and Cognitive metrics also consider the most important generators but in addition, they confirmed their capability to support a Capacity Management efficient automation with increased temporal stability thanks to the introduction of uncertainty in their computation. As for the Human Performance tests executed, they validated the hypothesis about the influence of Complexity Generators on the ATCOs effort in the different environments and planning phases.

COTTON results also present a high operational acceptance of COTTON Enhanced Complexity Metrics in DAC and FCA environments. Geometrical and Cognitive metric allowed configurations more flexibly adapted to the demand variations, where overloads are better anticipated, trajectories better allocated. Moreover, the introduction of uncertainty has shown no negative impact in capacity, together with a consistent delineation of DAC/FCA boundaries. In particular, the validation exercises showed the following:

A more even WL distribution with Geometric Complexity in the FCA short-term horizon, and that a RTS with human is needed for an optimal traffic allocation.

In DAC short-term horizon, the executed exercises revealed a reduction in personal costs with reduced number of sectors or at least a more balanced WL among original sectors. Sectors; overloads are more effectively detected and mitigated, along with a reduced number of sectors. However, the computational effort is high requiring a high optimisation of the algorithms together with a proper selection of the systems.

DAC medium term processes benefit from COTTON solution as the sector configuration plan is demonstrated to be more adapted to demand with less risk of imbalances.


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DAC/FCA boundary delineation processes with the support of COTTON Enhanced Complexity is defined with sufficient level of detail to demonstrate its technical and operational feasibility and demonstrated a reduction of overloads.

Finally, COTTON is planned to continue developing in SESAR IR Wave 2, where a follow-up of COTTON would be needed to elaborate technical enablers, the supporting tools, for enhanced DAC and FCA processes. These enablers are expected to achieve their full development and integration in SESAR Wave 3. To do so, COTTON proposes the following operational improvements to be integrated to SESAR ATM Master Plan:

Code Name Maturity at project end

COTTON-CM-01 DAC Capacity Management process explicitly taking into account the uncertainty inherent to 4D trajectory planning

V1/TRL2

COTTON-CM-02 FCA Capacity Management process explicitly taking into account the uncertainty inherent to 4D trajectory planning

V1/TRL2

COTTON-CM-03 Traffic complexity and workload assessment adapted to Trajectory Based Operations including uncertainty

V1/TRL2

COTTON-CM-04 An overall European ATM framework for Capacity Management in TBO integrating Dynamic Airspace Configuration (DAC) and Flight Centric ATC (FCA) solutions

FO/AO


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2 Project Overview

2.1 Operational/Technical Context

2.1.1 Trajectory-based operations, an opportunity to improve uncertainty management

Trajectory-Based Operations brings together many different improvements that allow the uncertainty of trajectory prediction to be better managed and reduced (e.g. downlinking of the Extended Projected Profile, sharing of detailed information on the ground through SWIM, the use of 4D contracts during the flight or increasing adoption of airport CDM).

Current systems and operational processes mostly rely on human experience to deal with uncertainty. The result is often a very imprecise balance between demand and capacity, leading to capacity going unused, thus rendering the airspace design and configuration process inefficient and difficult to automate.

Although several state-of-the-art techniques have been developed to determine the uncertainty associated to an aircraft trajectory prediction (based on machine learning [9], statistical analysis [11][12], or model driven parametric analysis [13]), they have not been integrated in the process of managing demand and capacity. For this reason, COTTON identified a need to work on the integration of the available uncertainty models into the Capacity Management processes.

2.1.2 Complexity, an enabler for the Capacity Management efficiency

Ground-based ATM systems traditionally consider system capacity as a function of the number of aircraft in an airspace volume. However, it is well acknowledged that control workload increases with traffic in a non-linear manner depending on the ATC routes, sector geometry, distribution of traffic flows, etc. Therefore, complexity metrics have been introduced with the goal of evaluating the difficulty experienced by air traffic controllers. Over the years a broad number of approaches for complexity assessment has been proposed attending to a wide variety of complexity factors.

Complexity has evolved with the era of distributed trajectory management and the evolution towards an autonomous aircraft framework, new generators of complexity or the way of influence of the existing one will change in future TBO framework or operations. With this idea in mind, COTTON identified the need to review the existing complexity metrics in order to afford their limitations in the support of future Capacity Management processes. Amongst these limitations, COTTON targets the following ones:

Lack of generalisation capability of the metrics to support the new, highly airspace structures, which are needed to achieve an airspace design flexible enough to efficiently serve free route operations in TBO;

High sensitivity of the metrics to the controllers used to infer the complexity model;

High sensitivity of the metrics to minor fluctuation in the traffic demand;


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Capacity Management require complexity assessment accounting for ATCo workload and to support trajectory-based operations decision making;

Some of them are adapted for the post-operational performance evaluation or macroscopic (strategic) system evaluation, whereas, to support capacity management, metrics need to be used operationally for the real-time decision support;

In order to re-inforce metrics usability in the resolution of high-workload situations. Metrics should be more comprehensible to human operator about the factors causing the problem.

2.1.3 New Capacity Management requiring adapted complexity approaches

DAC solution provides a performance-oriented approach that will result in overall European ATM improvement in capacity, fuel and cost efficiency, and environment sustainability. It offers increasing levels of adaptability of the Sector Configurations to the traffic demand based on the Airspace Building blocks and the Controlling Building Block principle. This principle will provide a possibility to “assemble” the airspace in seamless process and according to various performance targets.

Although some initiatives are in development to enable an efficient capacity management based on complexity assessment support1, the evaluation of the capacity thresholds associated to the new airspace structures is yet diffuse.

On the other hand, Flight Centric ATC (or Sectorless) provides a solution to demand and capacity balancing by considering the airspace as one piece without subdivisions. This solution supports the idea of trajectory-based (or user-preferred) operations in an ideal way since the ATCO is responsible for the aircraft for the entire trajectory within the FCA area. The aim of FCA is addressing capacity problems where the airspace cannot be further split in smaller sectors while minimising deviations from the desired trajectories.

Flight Centric ATC requires adequate strategies on how incoming aircraft are assigned to controllers to maximize capacity for a given airspace. Common sense is that workload should be one of the driving factors for this optimization.

In the support of DAC and FCA Capacity Management approaches, COTTON identified the need of refining complexity assessment methodologies, so that they can be automated optimising time and resources consumed. Another challenge addressed in COTTON is the interface and transfer between the Dynamic Airspace Configurations and Flight Centric ATC Approach. That is, to find best time and conditions for application of each of them, and define interfaces between them.

1 For example, ENAC and EUROCONTROL developed a method for Dynamic Airspace Management based on the Genetic Algorithm [14][15] and CRIDA applied Cognitive complexity to support dynamic airspace configuration in SESAR PJ08 [27]


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2.2 Project Scope and Objectives

2.2.1 Objectives

COTTON (Capacity Optimisation in TrajecTory-based OperatioNs) project is addressing the research topic ER3-03-2016 “Optimised ATM Network Services: TBO”, and in particular the challenge of exploring how the uncertainties associated with the agreed trajectory will impact the quality of the predictions – both volume and complexity of traffic demand – and the effectiveness of DCB processes regarding airspace management.

The main objective of COTTON project is to deliver innovative solutions to maximise the effectiveness of the Capacity Management processes in Trajectory Based Operations (TBO) taking full advantage of the available trajectory information. To achieve this goal, COTTON identifies three main objectives. This section introduces COTTON three main objectives as they are contained in COTTON DoA Part B (section 1.1) [19], followed by their associated sub-objectives.

OBJECTIVE 1: Improve the use of trajectory-based complexity and workload assessment to support Capacity Management enabled by Trajectory-Based Operations (TBO) including uncertainty. To achieve this goal, COTTON will:

[OBJ1.1] identify complexity and workload assessment needs to support Capacity Management in a TBO environment. On one side, the effect of uncertainty in the current complexity and workload metrics will be analysed and quantified. On the other side, SESAR Capacity Management processes in TBO will be evaluated to identify the limitations of the complexity assessment supporting them.

[OBJ1.2] research on the adaptations and refinements of the complexity and workload metrics and methodologies to address the identified needs. COTTON will research on mechanisms to provide complexity per trajectory instead of airspace volume as well as the use of trajectory-based information for the assessment of human factors aspects impacting workload.

[OBJ1.3] evaluate the use of the proposed complexity and workload assessment to support Capacity Management processes considering DAC and FCA solutions to demonstrate that Capacity Management effectiveness in TBO environment is improved with COTTON’s complexity approach.

OBJECTIVE 2: Identify and promote the benefits of Trajectory-Based Operations (TBO) to develop innovative demand/capacity models based on Dynamic Airspace Configuration (DAC) and Flight Centric ATC (FCA) solutions. TBO may have a positive impact on the cost-efficiency and safety provided. To achieve this goal, COTTON aims to:

[OBJ2.1] develop the benefit mechanisms that identify how TBO processes may maximise or reduce the expected benefits of DAC and FCA solutions.

[OBJ2.2] issue recommendations to improve the effectiveness of the Capacity Management processes and related airspace DCB measures. COTTON will identify the most efficient airspace DCB measures and the criteria to apply them in a TBO environment.


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[OBJ2.3] propose and improve the demand/capacity model associated to the Capacity Management processes integrating the use of trajectory information.

OBJECTIVE 3: Explore Dynamic Airspace Configuration (DAC) and Flight Centric ATC (FCA) solutions integration. This objective will be achieved through:

[OBJ3.1] the building of an integrated view on future Capacity Management processes in TBO by exploring how complexity and workload assessment could help integrating DAC and FCA solutions taking advantage of trajectory information that is available in a TBO environment.

[OBJ3.2] exploration of potential alternatives for their safe integration maximising the effectiveness of the Capacity Management processes.

2.2.2 Scope and Methodology

In this context, COTTON has addressed the optimisation of Capacity Management processes incorporating trajectory uncertainty into an advanced model for demand and capacity balancing considering all the Capacity Management planning phases, and integrating complexity and workload algorithms more suitable to the most innovative aspects of the SESAR 2020 solutions– Dynamic Airspace Configuration (DAC) and Flight Centric ATC (FCA) –.

To achieve its objectives, COTTON has assessed the suitability of the available complexity metrics to support DAC, FCA, and integrated DAC/FCA CM process. From the result of this assessment [2], COTTON has selected three candidate complexity metrics, namely Solution Space, Cognitive Complexity and Geometrical Complexity. It has evolved their mathematical formulation; and developed complexity-based methods to assess capacity [3] in DAC and FCA.

COTTON proposes a complementary use of these three enhanced complexity metrics to build COTTON Complexity Assessment, which is flexible enough to support each CM sub-process; with the due granularity to address the specificities of DAC and FCA airspaces; and effective at each planning phase.

The development and integration of COTTON Complexity Assessment within the CM processes constitutes COTTON Enhanced Capacity Management [4], whose potential benefits are assessed in COTTON validation[5][6].

The impact of COTTON proposed solutions on the most relevant Key Performance Areas (KPAs) has been evaluated by means of Fast Time Simulations to test not only DAC and FCA solutions in isolation but also potential alternatives for their safe integration.

The validation envisages three fast time simulations focused on the evaluation of the COTTON Enhanced CM Feasibility, Capacity, Cost-efficiency, Safety and Human Performance. Specifically, it focuses on the enhancements of COTTON Complexity Assessment brought to FCA short term planning phase (one hour till the execution time); DAC short-term planning phase (day of operations up to 20 minutes before execution time) and Integrated DAC/FCA medium-term planning phase (six to one days before operations).

To achieve its objectives, COTTON project was organised around three technical Work Packages (WP) and seven technical sub-tasks as shown in Figure 1:


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Figure 1: COTTON Technical Work Packages and Tasks

COTTON methodology is depicted in Figure 2; it shows main project tasks and milestones.

Firstly, COTTON identified limitations of complexity and workload metrics in DAC and FCA taking into account uncertainty prediction models (WP2, Task 2.1 Limitations of complexity and workload metrics in TBO).

Afterwards, WP3 Tasks 3.1 and 3.2 (Enhanced Capacity Management in DAC and FCA respectively) were devoted to assess the effects of the identified limitations to derive the requirements that complexity predictions should comply with in order to be useful in a TBO environment where Capacity Management processes are in place (Project Milestone M1).

WP2 Task 2.2 (Improvements of complexity and workload metrics to support DAC and FCA Solutions in TBO) developed the improvement of the complexity assessment methodologies and metrics in line with the requirements previously outlined.

Once the improved complexity methodologies and metrics were available (Project Milestone M2), they were integrated in DAC and FCA demand and capacity models through WP3 Tasks 3.1 and 3.2 (Resulting in the achievement of Project Milestone M3).

DAC and FCA improved demand and capacity models were validated in WP4 tasks through Fast Time Simulation exercises. They evaluated the appropriateness of new algorithms for DAC and FCA incorporating uncertainty in the processes of airspace organisation and segment allocation. Project Milestone M4 was achieved at the end of the simulation exercises.


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In addition to the validation results obtained in WP4, Real time experiments for integration of Human Factors performed within the framework of WP2 Task 2.2 guided the improvement of the complexity methodologies.

Finally, the integration of the DAC and FCA improved DCB models was assessed and the integrated Capacity Management function (whose achievement was articulated through Milestone M5) was validated.

Figure 2: COTTON Methodology

2.3 Work Performed

2.3.1 Complexity Metrics Development

In order to achieve first sub-objective of COTTON project (see section 2.2.1), OBJECTIVE 1: Improve the use of trajectory-based complexity and workload assessment to support Capacity Management enabled by Trajectory-Based Operations (TBO) including uncertainty; COTTON WP2 was in charge to identify limitations of existing complexity assessment approaches and research on improvements to support Capacity Management processes in a TBO environment.

The first task performed was to study how trajectory uncertainty affects the existing complexity assessment methodologies. For this purpose, key elements generating complexity, called Complexity Generators, in the Capacity Management applications were identified. The initial list of Complexity Generators found in the literature was analysed during the First COTTON workshop [9].

Then, Causal Models were developed to evaluate the effect of trajectory uncertainty on complexity assessment for each time horizon and operational context. Based on them, one Bayesian Network per environment has been proposed.

The evaluation of the compliance of a given complexity method to support DAC and FCA environments was based on the identified limitations in the top-down review, and on the Bayesian Network bottom-up. This resulted in conclusions and recommendations about the applicability of different existing


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complexity techniques. D2.1 Impact of trajectories' uncertainty in existing complexity methodologies [2] describes the work performed under this task.

Subsequently, second task of WP2 continued the cross analysis between Complexity Generators directly or indirectly considered by the complexity metrics and most influencing ones for DAC/FCA environment at different forecast horizons. Three complexity metrics were selected as the most adapted for further development, in particular: Solution Space, Cognitive Complexity and Geometrical Complexity metrics.

D2.2 Innovative complexity and workload assessment to support future Capacity Management processes in TBO [3] describes the developments performed on the three metrics, which have been performed in response to issues of the existing metrics limiting the effectiveness of the capacity management [2], WP3 requirements on complexity assessment [4], and validation results [6].

The development activities included:

1. Metric model modification to better take into account Complexity Generators;

2. Consideration of the demand uncertainty in the complexity assessment;

3. Adaptation of the metric result granularity to provide instantaneous and aggregated values per given period, as well as graphical (geographical) representation of the results;

4. Verification tests checking implemented improvements against the designed expectations;

5. Identification of the reference value of capacity (controller’s workload) to enable the use of the metrics in the capacity management process.

To get a better understanding on the metrics performance and the possible benefits of the demand uncertainty consideration in the complexity assessment, several small-scale complexity metric assessment tests were performed:

The Solution Space metric test was conducted by a Subject Matter Expert (SME) trial, where one air traffic controller was asked to rate the expected workload levels at regular time intervals during the playback of several traffic scenarios. After the data had been gathered, a correlation analysis was done between the metric responses and the ISA ratings.

A test on Geometrical and Cognitive metrics was performed to assess their applicability within DAC concept of operations. The principal objective of this analysis was to better understand both complexity assessment approaches and to learn how they could complement each other. It confirmed that metric temporal stability increases with introduction of the demand uncertainty information into the complexity assessment.

Finally, Human Performance tests have been carried out to validate hypothesis on the identified influence of the Complexity Generators on the ATCOs effort in different operational environments (DAC, FCA). To achieve this, Human response measurement devices were used (Electroencephalography (EEG), Video-based-eye-tracker, monitoring of pulse plethysmography). It included the following tasks:

1. Selection of the Complexity Generators to be tested;


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2. Transfer the selected Complexity Generators into a simulation environment (EuroScope tool [26]);

3. Simulation of different scenarios (Reference, DAC and FCA scenarios) to test ATCO’s human response to different level of presence of the Complexity Generators;

4. Analysis of the results.

Figure 3. Human Performance Test overview

The results of the Human Performance test reinforced the outcome of First COTTON Workshop with the respect to the most influencing Complexity Generators, and showed to be aligned with the hypotheses and conclusions of D2.1[2], that represented the starting point for the complexity metric improvements in the COTTON project

2.3.2 FCA Capacity Management Process Enhancement

In order to achieve second sub-objective of COTTON project (see section 2.2.1), OBJECTIVE 2: Identify and promote the benefits of Trajectory-Based Operations (TBO) to develop innovative demand/capacity models based on Dynamic Airspace Configuration (DAC) and Flight Centric ATC (FCA) solutions; COTTON WP3 T3.1 COTTON Enhanced Capacity Management in FCA has accomplished the following tasks:

Specification of complexity and uncertainty requirements for FCA as an input to WP2.

Identification of FCA use cases affected by the use of advanced complexity metrics.

Definition of the enhanced capacity management processes for FCA.

Specification of the DCB tools enhancements needed to use advanced complexity.

The result of these tasks is reported in D3.1 Enhanced Capacity Management considering DAC and FCA in Trajectory Based Operations [4].


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Two FCA use cases for short-term planning phase identified in COTTON D3.1 [4] (FCA Use Case 4 “Opening and closure of CWP” and FCA Use Case 5 “Allocation of Traffic entering the FCA”) were assessed through FCA validation reported in D4.2 COTTON Validation Report [6] with the following objectives:

Assess technical feasibility of COTTON improved Capacity Management processes in FCA short-term planning phase.

Assess potential benefits in Capacity of COTTON improved Capacity Management processes in FCA short-term planning phase

FCA validation comprised the following activities:

The execution of a DLR-internal Expert Workshop to determine influences of trajectory uncertainty on the investigated use cases (Allocation and Opening and Closure of CWP) and in preparation of a Fast Time Simulation.

The execution of a Fast Time Simulation using existing complexity for quantitative assessment of the impact of trajectory uncertainty on the investigated use vases. TrafficSim platform of DLR was used for this Fast Time Simulation

The execution of Fast Time Simulations to compare existing complexity tools with tools enhanced by innovative workload prediction algorithms.

2.3.3 DAC Capacity Management Process Enhancement

In order to achieve the second sub-objective of COTTON project (see section 2.2.1), OBJECTIVE 2: Identify and promote the benefits of Trajectory-Based Operations (TBO) to develop innovative demand/capacity models based on Dynamic Airspace Configuration (DAC) and Flight Centric ATC (FCA) solutions; COTTON WP3 T3.2 COTTON Enhanced Capacity Management in DAC has accomplished the following tasks:

Specification of complexity and uncertainty requirements for DAC as an input to WP2.

Identification of DAC use cases affected by the use of advanced complexity metrics.

Definition of the enhanced capacity management processes for DAC.


The result of these tasks is reported in D3.1 Enhanced Capacity Management considering DAC and FCA in Trajectory Based Operations [4].

The enhancement of DAC capacity management proposed within COTTON aims at avoiding capacity buffers by using more precise ways of predicting the complexity and workload of traffic. COTTON has performed an analysis of this current process and proposed improvements to reduce uncertainty and subjectivity of the results, addressing in particular the following aspects:

Integration of trajectory-based complexity metrics for the estimation of workload associated to a configuration;


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Establishment of the Overload period identification based on the use of complexity metrics and above-mentioned associated capacity thresholds (sustained and peak overloads);

Better characterisation of hotspots at a level of granularity smaller than the controlled airspace, therefore supporting the implementation of DCB measures with more precise impact (e.g. modify the trajectories -in time, laterally or vertically- to avoid smaller pieces of volumes);

Integration of uncertainty of traffic demand within the DCB processes, considering diverse types of sources, variability degrees of freedom and characterisation approach (probability distributions, variability ranges, equally probable sets, etc.);

Customisation of the process for each planning horizon: long, medium and short-term. This customisation addresses the DAC Use Cases to be performed, the complexity metric used, the thresholds, the use of uncertainty and the definition of overloads and hotspots.

DAC Capacity Management process along the DCB timeline includes ten operational use cases for which the assessment of Airspace/Sectors traffic load is required. It has been analysed how these operational use cases (activities/process) shall benefit from the introduction of the above-mentioned improvements.

Moreover, the work performed has included the execution of a validation exercise [6] consisting of the emulation of the process of optimisation of airspace configuration in short-term planning phase [4]. The high-level validation objective was to validate the feasibility and improvements of the DAC solution by evaluating the benefits with regards to the baseline DAC process. To this end, the validation exercise has been based on:

1. The execution of a software programme developed in COTTON as configuration optimiser, providing the optimal configurations according to the baseline and enhanced DAC processes;

2. The execution of Fast Time Simulations (FTS) to evaluate the effectiveness of the optimal configuration obtained in DAC baseline process versus DAC enhanced process, by means of a set of indicators defined to evaluate the benefits expected to be delivered by the enhanced processes in Capacity, Cost Efficiency, Safety and Human Performance.

The evaluation of the complexity of any configuration has been based on a cost-function accounting for:

Cognitive Complexity, estimating controllers’ mental workload through a function combining important abstractions, namely flows interactions, potential conflicts, number of flights in evolution (climb, approach) and number of flights out of standard flows;

Sector Shape factors of each configuration, namely angles and vertices, flows orientation and position, sector size and crossing points distance.

2.3.4 Integrated DAC/FCA Capacity Management Process Development

In order to achieve third sub-objective of COTTON project (see section 2.2.1), OBJECTIVE 3: Explore Dynamic Airspace Configuration (DAC) and Flight Centric ATC (FCA) solutions integration; COTTON WP3 T3. Capacity Management processes integrating DAC and FCA solutions has accomplished the following tasks:


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Specification of complexity and uncertainty requirements for Integrated DAC/FCA DCB as an input to WP2.

Identification of new use cases that are specifically defined to ensure the integration of the two modes of operations (DAC/FCA).

Definition of an Integrated DAC/FCA capacity management process.

Delineation of airspace to DAC, FCA modes. Specification of a function establishing the methodology and criteria to allow the identification of portions of airspace where either DAC or FCA may be applied and when, thus defining its opening scheme.

A fast time simulation was carried out to validate the integrated enhanced DAC and FCA capacity management solution proposed in COTTON. The objective was to preliminary assess whether the combined use of the two advanced capacity management systems is technically feasible, operationally acceptable and offers improvements in terms of Capacity and Cost-efficiency, in comparison with the deployment of only one capacity management solution separately.

2.4 Key Project Results

2.4.1 Complexity Metrics

2.4.1.1 Results from the Complexity Metrics assessment

The results of the study of the impact of trajectories' uncertainty in existing complexity methodologies permitted identification of the key elements generating complexity, called Complexity Generators. The final list of the Complexity Generators classified into four main categories is shown in Table 1.

Table 1. List of categories and its complexity generators

MAIN COMPONENT CATEGORY COMPLEXITY GENERATOR

AIRSPACE AND TRAFFIC FLOWS

FLOWS AND AIRSPACE STRUCTURE

Flows distribution

Number of interaction points

Number of main flows

AIRSPACE ORGANIZATION

Presence/proximity of restricted airspace

Airspace uses

Distribution of crossing points and their proximity to airspace boundaries

AIRSPACE DISTRIBUTION Airspace Volume

Airspace Geometry

TRAFFIC

TRAFFIC MIX

Altitude AC changes

Altitude AC distribution

Speed AC distribution

TRAFFIC DENSITY Occupancy

Traffic entry


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MAIN COMPONENT CATEGORY COMPLEXITY GENERATOR

Distribution of flight time per aircraft under ATCO responsibility in the given timeframe

TRAFFIC INTERACTION

Number of conflicts predicted

Time difference at crossing points

Vertical and horizontal convergence (diverging, constant or converging)

ATC TASK AND HUMAN

PERFORMANCE

OPERATIONAL PROCEDURES

Coordination procedures

Vectoring and operational restrictions

Transition and changes in configuration

CONFLICT RESOLUTION Degrees of freedom of the controller in the resolution strategy of the conflict (e.g. procedural or supporting tools limitations)

SUPPORT SYSTEMS

CDR Support and monitoring System

Coordination support tools

System failure

OTHERS Weather conditions

The Complexity Generators were evaluated using Causal Bayesian Models to identify the most influencing ones. Figure 4 shows an example of these models, detailed description of the models is given in COTTON D2.1 report [2].


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Figure 4: Bayesian network for assessing Complexity Uncertainty under DAC operational framework in the short-term horizon

Then, the state-of-the-art complexity assessment methodologies were classified accounting for their capability to support each environment and time planning phase based on the Bayesian network models and their capability to account for the most influencing Complexity Generators (Table 2). This analysis was done to select the ones in better position to be developed within COTTON project in order to get advance complexity metrics better adapted to DAC, FCA and integrated DAC/FCA capacity management process.


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The colour codes in the Table 2 represent at which level a given metric is adapted and could be adapted to a certain environment and time horizon. Hence, red signifies that metric is hardly applicable to given environment and forecast time horizon; orange, metrics could be adapted with certain limitations; and finally, green represent metrics fully adapted for a given usage.

Table 2. Complexity generator cross-analysis

METRIC

DAC FCA

Long Term Medium

Term Short Term Long Term

Medium Term

Short Term

PHARE

SOLUTION SPACE

INPUT-OUTPUT APPR.

TRAJECTORY UNCERTAINTY

COGNITIVE COMPLEXITY

TBX

DYNAMIC DENSITY

ALGORITHM APPR.

GEOMETRICAL APPR.

TRAJECTORY FLEXIBILITY

DYNAMIC SYSTEM APPR.

PROBABILISTIC APPR.

FRACTAL DIMENSION

It could be concluded that metrics belonging to the same group behave similarly. Conflict-interaction based metrics are mainly adapted to short-term and execution phases with possibility of adaptation to medium-term; cognitive based metrics to medium-short horizon; while intrinsic metrics (that neglect procedures and human-in-the-loop) are mostly adapted to long-term phases.

Furthermore, Table 2 reveals that there is no ultimate complexity metric that completely suits the requirements of DAC and FCA environments for every forecast time horizon. In addition, for every environment and time horizon there are several metrics that are or could be adapted and that do not necessarily cover the same complexity generators, have same output formats, etc. For this reason, in the framework of COTTON project, metrics that are suitable for multiple applications are considered preferable due to the possibility of complementing each other in the complexity assessment process (this statement was proven later in complexity metric assessment tests). Additionally, SESAR complexity assessment methodologies STEP1 v3 (SESAR Joint Undertaking, 2016) were prioritized due to the fact that because they have already been validated in the SESAR P04.07.01.

Finally, three complexity metrics were selected for further development in the COTTON project: geometrical approach, cognitive complexity metric and solution space metric:

Solution Space metric considers the availability of aircraft solution spaces (i.e., re-routing opportunities) as a function of traffic intent, aircraft capabilities; sector geometry and sector disruptions (e.g. prohibited airspace).


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Cognitive Complexity estimates controllers’ mental workload through a function combining important abstractions, namely flows interactions, potential conflicts, number of flights in evolution and number of flights out of standard flows.

Geometrical metric is based on proximity and convergence between aircraft. For each pair of aircraft, the proximity is calculated as the exponential function of their relative distance and the convergence is calculated as the level of variation of their relative distance.

Table 3 summarizes the application of the selected metrics. Further details about metrics selection process are provided in COTTON D2.2 report [3].

Table 3. Selected complexity metric application

SELECTED METRIC Long Term Medium Term Short Term

Dynamic Airspace Configuration

GEOMETRICAL APPR. COGNITIVE

COMPLEXITY


GEOMETRICAL APPR. SOLUTION SPACE

SOLUTION SPACE COGNITIVE

COMPLEXITY GEOMETRICAL APPR.

Flight Centric ATC

GEOMETRICAL APPR. COGNITIVE

COMPLEXITY


GEOMETRICAL APPR. SOLUTION SPACE

SOLUTION SPACE COGNITIVE

COMPLEXITY GEOMETRICAL APPR.

2.4.1.2 COTTON Advanced Complexity Metrics

Solutions space metric is established on the fact that air traffic controller workload for short-term time scales is largely determined by unexpected separation provisions and network disruptions (e.g., unpredicted weather cells). How many re-routing opportunities a controller has depends on the following constraints and their relationship: aircraft dynamics and performance (speed), metering constraints, location, size and evolution of no-fly zones (weather cells, prohibited airspace), surrounding aircraft, separation norms, and airspace geometry. Hence, the complexity can then be described as a function of the ratio of the number of feasible probe states (available airspace) over the total number of considered probe states (total airspace) in a pre-defined range of directions and speeds (Figure 5). Uncertainty of the demand is considered as additional buffer over separation norms.

Figure 5. Aircraft solution space area


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Given the experimental status of the solution space metric, the fact that currently no (workload) capacity thresholds can be defined based on the complexity ratio offered by the metric, a first attempt has been made to link the metric to expected workload levels. The assessment of the solution space metric has been conducted by a Subject Matter Expert (SME) trial, where one air traffic controller was asked to rate the expected workload levels at regular time intervals during the playback of several traffic scenarios : structured/unstructured ATS routes and with/without no-fly zones.

After the data had been gathered, a correlation analysis was done between the metric responses and the ISA ratings. As a reference, the ISA ratings were also correlated to sector occupancy, allowing for investigating differences between the solution space and occupancy metrics in predicting workload. Figure 6 shows the results of the correlation analyses of the scenarios with structured routes and without no-fly zone. Correlation analysis revealed that in all scenarios correlation the correlations for both sector occupancy and the solution space metric are quite high (> 0.75), confirming that number of flights as a Complexity Generator is adequately covered in the solution space approach. Secondly, analysis has shown higher correlation in the unstructured traffic scenarios.

Figure 6. Correlation results of the scenarios with structured routes and without no-fly zone.

Cognitive Complexity is a method to estimate controllers’ mental workload through structure-based abstractions such as flows interactions, potential conflicts, number of evolving flights and number of non-standard flows. The cognitive complexity is tailored to sector complexity evaluation, hence, improvements in COTTON were focused on DAC application, including sector shape parameters such as: convexity, flow orientation and position, sector vertical size and crossing point – border distance.

The Cognitive Complexity under demand uncertainty was evaluated using probability distribution of each flight involved in the metric calculation. Then, the output expected is a probability distribution function of the complexity metric at each instant of calculation. The methodology used (as shown in Figure 7) is based on Monte Carlo simulations as follows:

To build the probability distributions of the Cognitive Complexity at t, the calculation considers both the flights that are nominally within the sector at time t plus the flights that are nominally out of the sector but for which the probability of being within the sector is higher that certain value.

Once the set of flights to be considered in the calculations is identified, the calculation of the Cognitive Complexity is performed N times:

o N is the number of runs of the Monte Carlo simulation needed to achieve results with statistical validity;


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o In each run, the temporal error of Estimated Time of Over-fly (ETO) for each flight of the set is chosen randomly but according to the corresponding probability distribution of error;

o Taking into account the error obtained for each flight of the set, the flights are re-positioned according to it;

o Each run results in a value of Cognitive Complexity for t.

After N runs, there are N values of Cognitive Complexity for time t. From this set of values, it is built a probability distribution of the Cognitive Complexity and from which the cumulative probability of Cognitive Complexity being below or above certain value can be calculated.

Figure 7. Probability Distribution of Cognitive Complexity at time t.

Geometrical approach represents a measure of intrinsic complexity related solely to the traffic structure by computing the disorder of the position and speed vector. Hence, complexity is calculated as a function of the proximity -used to characterize the geographical distribution of aircraft in the given volume of airspace- and convergence indicator -used to quantify the geometric structure of the speed vectors of aircraft present in a given geographical zone.

Geometrical Complexity (GC) approach is equally adapted to the application to DAC and FCA environment, since metric value represent contribution of each aircraft in the total complexity. Hence, improvements in COTTON project were mainly focused on the coverage of additional Complexity Generators not explicitly covered by the initial metrics model. This includes: number of aircraft in the control working position, number of evolving flights, distance of the sector boundary to the major interacting areas, number of coordination, etc.

Complexity assessment under uncertainty using Geometrical approach considered the Monte-Carlo simulation approach to estimate distribution function of the complexity random variable, based on Borel's law of large numbers (Figure 8).


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Figure 8. Complexity values with and without demand uncertainty

In order to test applicability of Cognitive Complexity (CC) and Geometrical Complexity (GC) methodologies within DAC concept of operations, an analysis comparing the cognitive and geometric complexity metrics was performed. The analysis included:

A predictability test, comparing predicted and actual complexity values for both complexity metrics

A performance test, comparing the actual complexity values of both complexity metrics and the actual occupancy.

The analysis encompassed the scenarios presented in D4.2 VALR Exercise 2 corresponding to DAC concept of operations [6]. Figure 9 and Figure 10 show an example of the evolution of predicted (blue colour) and actual (purple colour) complexity of both complexity metrics (Cognitive on the left figure and Geometrical on the right figure) for the sector TERI and two scenarios with and without demand uncertainty.

Figure 9. CC and GC predictability comparison for sector TERI T-12 hours forecast horizon and without demand uncertainty


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Figure 10. CC and GC predictability comparison for sector TERI T-12 hours forecast horizon and with demand uncertainty

The results confirm, in the case of both metrics, that the introduction of demand uncertainty information into the complexity assessment increase the metric predictability. Geometric Complexity, in general, has higher temporal variations of the complexity value than cognitive metric, induced by the fact that complexity assessment in geometrical approach considers traffic situations, that are very sensitive, and not traffic flows as in cognitive approach. However, for both metrics, it is noticeable metric temporal stability is increased with introduction of the demand uncertainty information into the complexity assessment.

Further, the metrics performance test (example figure in Figure 11) confirmed that the general tendencies of Geometric complexity and Cognitive complexity during the simulation period are similar. However, Geometric approach, as stated before, has higher temporal variation of the complexity values due to its sensitivity to the traffic situation change, like occupancy counts, contrary to the Cognitive complexity value evolution that is much smoother. On the other hand, the correlation of Geometric complexity value with Occupancy per minute is higher in the tested scenarios.

Figure 11. Comparison between actual CC and GC for sector TERI

Driven by the fact that evolution of both metrics has similar tendencies, while Geometric approach gives closer view to the occupancy and geometry of the traffic situation, and Cognitive approach gives more information about the mental workload of the ATCos, makes metrics complementary one to other and their combined use promising. More about complexity metrics development activities and assessment tests can be found in D2.2 report [3].


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2.4.1.3 COTTON Human Performance Tests Complexity Metrics

The last activity in the complexity metrics process of development was to identify the impact that Complexity Generators have on human response regard to concerning the air traffic control actions in DAC and FCA scenarios, hence, identify the strengths and the weaknesses of DAC and FCA on the ATCO workload, carried out in the Human Performance Tests.

The choice of the CG (among the ones presented in [2] was based predominantly on their influence on the complexity in the short-term forecast horizon. Additional criterion was that CG represent relevant influencing factor in both DAC and FCA concept of operations, enabling later comparison of their influence in different environments. Consequently, the following CGs were selected: Occupancy & Traffic Entry (CG1), Number of Conflicts Predicted (CG2), Aircraft in Evolution (CG3), No Standard Flights (CG4).

The influence level of the Complexity Generators was measured using three sensors collecting: visual activity, brain activity and heartbeat. Classified parameters are shown in Figure 12.

Figure 12. Sensors of human response, parameters and their theoretical evolution with increasing activity

Different scenarios were designed using the EuroScope tool, simulating different capacity management operational environments: reference (current), DAC and FCA. Human Performance tests enabled collection of the ATCO’s physical and cognitive conditions during the simulation runs, that were further analysed using casual and statistical models and ANOVA approach.

The interpretation of the results of the ANOVA was carried out from two different perspectives:

Considering the level of influence induced by the evolution of each CG and ATC solution using Parameter Influence Response Metric, which evaluates the degree of variation of the human response to the variation in the input values;

Considering the level of impact of each CG and ATC solution using Parameter Severity Response Metric.

The Convolution of an ATCO task Matrix (Table 4) enabled the classification of the total influence of a given CG into four categories, going from Low (green) to Very High (red).


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Table 4. Convolution of an ATCO task Matrix.

CONVOLUTION OF AN ATCO TASK MATRIX

INFLUENCE

SEVERITY

D C B A

1 D1 C1 B1 A1

2 D2 C2 B2 A2

3 D3 C3 B3 A3

In the final analysis, every CG was classified into one category/cell from Table 4. Table 5 shows the final classification for each pair of scenario (Reference, DAC, and FCA) and Complexity Generator.

Table 5. Convolution metric matrix results.

CG1 CG2 CG3 CG4

REF C1 B2 B1 B2

DAC B2 B2 A3 B3

FCA B2 A3 B2 B2

The results showed that DAC scenario is the most demanding scenario considering measured CG influence and severity, while current is the least demanding. The number of predicted conflicts CG2 is the most influencing CG in all scenarios. The CG severity has a greater variance compared to their influence.

In order to compare each of the scenarios and CGs, Figure 13 shows a distribution of each case in Influence and Severity.

Figure 13. Scenarios together with Complexity Generators Complexity Metric value.


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The Human Performance tests enable reinforcement of the hypotheses and conclusions made purely of the expert’s judgment and reported in [2]:

The anticipated conflicts represent fundamental parameter in the definition of complexity.

When applied to FCA, conflicts will generate a greater effort due to the necessary coordination between controllers.

In the FCA, the ATCOs could better anticipate the events that will take place in the future, which contributes to improving the complexity in this operational scenario.

Unforeseen aircraft (or unforeseen actions) generates greater complexity due to inability to foresee its effects.

Similarly, the evolving aircraft generates a greater effort, due to induced uncertainty of the conflicts or hot spots they will/could produce.

2.4.2 FCA Capacity Management Process

Based on expert analysis on how to integrate the identified improvements, as well as on the results of FCA validation exercise, COTTON has proposed an enhanced capacity management workflow in FCA. The following figure summarises this enhancement.

Figure 14: Enhanced FCA Capacity Management proposed by COTTON


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In the FCA environment, the technical feasibility of Geometric Complexity Metric developed in COTTON for Allocation purposes and described in COTTON D2.2 [3] was analysed. For this, the Geometrical Complexity Metric algorithm was inserted in DLR’s simulation platform (TrafficSim Platform), replacing the former algorithm used by DLR in SESAR2020 PJ.10-01b[25]. Geometric Complexity Metric distributed workload more evenly, thus better, than the algorithm used by DLR in the Budapest exercise of SESAR2020 PJ.10-01b in January 2019. However, no gold standard for workload measurement existed for this FTS, and it had to be expected that each algorithms favours “its own” allocation. In order to really find the best allocation based on workload prediction, Real Time Simulations with ATCOs in the loop and with constant workload ratings by the ATCOs would have to be used.

Geometrical Complexity Metric uses aircraft proximities instead of interactions. It was assumed however that the relationship between uncertainty and interaction detection could be transferred to proximity determination as well. If only proximity as a value and no threshold like in the interaction or no-interaction decision of the Reference Complexity Algorithm from SESAR2020 PJ.10-01b is used, then the influence of uncertainty is even less than compared to the Reference Complexity Algorithm. If traffic allocation is based on aircraft interactions, the separation threshold has to be increased to account for uncertainty. If aircraft proximities are used instead, the influence of uncertainty is even less as there is no threshold decision.

The results of the FTS validation exercise performed on FCA short-term processes are presented in the following table, extracted from COTTON D4.2 [6]. It summarises the validation objectives per KPA studied and the high level results in each of them.

Table 6: FCA Validation Exercise Summary of Results per KPA

Validation Objective Success Criterion Exercise #01 Validation Results

Assess technical feasibility of COTTON improved Capacity Management processes in FCA short-term planning phase.

The implemented FCA processes converge in an optimal Demand and Capacity Balancing

Geometric Complexity Metric seems to distribute workload more evenly, thus better, than the algorithm used by DLR in the Budapest exercise of SESAR2020 PJ.10-01b in January 2019. However, no gold standard for workload measurement existed for this FTS, and it had to be expected that each algorithms favours “its own” allocation. In order to really find the best allocation based on workload prediction, Real Time Simulations with ATCOs in the loop and with constant workload ratings by the ATCOs would have to be used

Assess potential benefits in Capacity of COTTON improved Capacity Management processes in FCA short-term planning phase

No significant negative impact on DCB due to trajectory uncertainty

In the FCA Concept, if traffic allocation is based on aircraft interactions, the separation threshold has to be increased to account for uncertainty. If aircraft proximities are used instead, the influence of uncertainty is even less as there is no threshold decision.


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2.4.3 DAC Capacity Management Process

Based on expert analysis on how to integrate the identified improvements, as well as on the results of DAC validation exercise, COTTON has proposed an enhanced capacity management workflow in DAC. The following figure summarised this enhancement.

Figure 15: Enhanced DAC Capacity Management proposed by COTTON

The results for each set of UCs/ planning phase are the following:

Long-term planning phase - Sector Design (UC-01, UC-02, UC-03)

Advanced complexity metrics do not change the sector design process; they however enrich the current process with a higher granularity of the assessment of the trajectories, their interactions and their complexity. In the design phase, the main benefit of the use of complexity metrics (apart from the design algorithm itself used in the metrics) is allowing the user to select the most complex traffic days, in contrast with nowadays selection of the “busiest” traffic days in terms of counts.

By splitting the sector design process into different stages in line with the proposed use cases above, it is possible to use different metrics at different stages of the Sector Design process and at different times along the ATM timeline.

The quantification of the accuracy of the complexity values used for the airspace design process allows a more accurate identification of critical airspace zones for which an adapted design can


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be provided, reducing the risk of facing inappropriate sector configurations in medium and short-term planning.

Medium-term planning phase - Sector Configuration (UC-04, UC-05, UC-06)

In the medium term, with more certainty on traffic data, certain design elements from the DAC options can be further refined in line with possible scenarios in traffic demand and their resulting complexity. Refinements can be triggered by an automated process (based on certain thresholds) or manually. In an automated process, this triggering of design refinement could be a continual process providing the user can set limits as to the extent of the changes in expected complexity (linked to demand changes and level of uncertainty) or the weather forecasts. The aim is to ensure that the Sector Design and Configuration fits with the training framework of the ANSP (in terms of manageable complexity) and system support to ATCO’s within the ANSP.

Due to the amount of different sector configurations possible thanks to the DAC concept, a precise assessment is required to select the most appropriate configuration and path/scheme to reach the configuration best meeting the complexity of the traffic. Automation is a key enabler, allowing the analysis of a wide amount of possible options and providing support in the estimation of residual overloads and other parameters as compared to the thresholds established by the ANSP strategy. After validation of the sector configurations, a catalogue of workable sector configurations will be available.

Short-term planning phase - Integration with DCB (UC-07, UC-08, UC-09, UC-10)

In the short term, the benefits of the enhanced complexity metrics and uncertainty quantification are most realised due to the dynamic nature of traffic predictions that are closer to the day of operations. In this timeframe, complexity metrics are able to capture changes in traffic demand due to the finer granularity compared to today’s operations looking only at counts. With increasing certainty of the traffic data, the enhanced complexity metrics can be used to their optimum to review the sector configurations required to meet the traffic complexity. Again, this can either be through an automated approach (with some user control), whereby the changes in complexity and certainty trigger a change on configurations proposed, or through a manual approach. In either case, the result will aim to meeting the Local and Network Performance targets.

Furthermore, DCB measures applied to resolve imbalances can be applied on a trajectory by trajectory basis in order to reduce the complexity of a situation rather than today’s method of reducing the amount of flights. In combination with the Complexity measurement, the use of uncertainty quantification allows the user/ FMP to act on trajectories with an expected confidence that the outcome will be as intended. The uncertainty quantification therefore in the short-term time line aids decision-making process for Sector configuration and DCB measures by quantifying the probability that firstly, traffic scenario will happen, and secondly, the effect that such measures / configurations will have on the traffic scenario.

As introduced in section 2.3.3, a validation exercise has provided a first estimation of the benefits of an enhanced DAC Capacity Management process integrating Cognitive Complexity plus Sector Shape metrics and uncertainty, and using a certain level of automation in support of decision-making. The exercise was focused on short-term planning phase, and worked in a particular scenario with a particular implementation proposal of COTTON enhanced process. The applicability considered was en-route for medium to high complexity. The Use Cases (see Figure 15) addressed have been:


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• UC6 “Identify initial local/FAB sector configurations matching local/network performance targets”.

• UC7 “Identify remaining hotspots and/or critical traffic volumes”.

The sectors considered in the exercise were on an evolution of the current sectorisation towards DAC airspace: current sectors of Castejón (CJI), Teruel (TER) and Zaragoza (ZGZ) in Spanish FIR. They have been further split on Teruel North/ South and Zaragoza North/ South, further vertical division Flight Level set at FL365, to separate evolving flows from overflights. In total, 12 basic volumes have been defined for the studied area for the day of operation of 5th July 2017 from 19:00 to 20:00 (analysis period). All possible combinations of these basic volumes result in 33 workable sector configurations.

The selection of the optimal combination of sectors took into account the predicted complexity values of the proposed sector configurations for the day of operation. Declared flight plans (and an estimation of uncertainty in their execution) were used to predict complexity for each sector. Furthermore, a cost function chose the best configuration according to several fixed criteria for operational purposes, such as average complexity over the analysis period, peak values, balance of complexity between sectors and other factors related to sector geometry.

Three scenarios were evaluated:

• Scenario#1 Reference, using Cognitive Complexity as input metric to the cost function to find the optimal configuration;

• Scenario#2 Solution 1, using an enhanced metric (Cognitive Complexity plus Sector Shape), as defined in COTTON D2.2 [3];

• Scenario#3 Solution 2, using the enhanced metric and integrating uncertainty.

For the input traffic, it has been used one-hour traffic demand for the area studied at T-12h, T-3h, T-1h and T-30min, being T the start time of the one-hour period studied. Uncertainty considered was time uncertainty in Estimated Times Over (ETOs) the waypoints across the DAC area for all flights. For each flight, the uncertainty of the estimated time over a point at t is given by an error distribution of Actual Time Over (ATO) minus ETO. This error distribution is a discrete set of possible error values, each one equally probable. Error distributions have been obtained by comparing one-month flight plans with flown traffic at the time horizons considered in the exercise.

The optimal configurations obtained for each scenario were later implemented in a FTS tool to evaluate indicators in different KPAs. The results of the exercise are presented in the following table, extracted from COTTON D4.2 [6]. It summarises the validation objectives per KPA studied and the high level results in each of them.

Table 7: DAC Validation Exercise Summary of Results per KPA


Assess technical feasibility of COTTON improved Capacity Management

The implemented enhanced DAC process converges in an optimal configuration

COTTON solution (proposed metrics, algorithms and computation process) is suitable for the DAC environment and covers the requirements of the DAC CM process for the execution of the selected


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processes in DAC short term planning phase.

Use Cases. However, the computational effort is high for the time horizon considered. It is assumed that in a full-developed system this drawback will be mitigated by the use of more powerful tools.

Assess potential benefits in Capacity of COTTON improved Capacity Management processes in DAC short term planning phase.

The chosen Capacity indicators show an improvement when comparing Solution versus Reference Scenario.

The selected complexity metric shows better detection rates for overloads than for underloads in all scenarios. The remaining hotspots are better mitigated in Solution Scenarios, but the results show that the cost-function needs refinement to discard also configurations with underloads.

Assess potential benefits in Cost-efficiency of COTTON improved Capacity Management processes in DAC short term planning phase.

The chosen Cost-efficiency indicators show an improvement when comparing Solution versus Reference Scenario.

The solution achieves better sector configurations in terms of cost-efficiency with either reduced number of sectors or reduced number of OCC/min per sector and a more balanced workload among sectors.

Assess potential benefits in Safety of COTTON Solution improved Capacity Management processes in DAC short term planning phase

The chosen Safety indicators show an improvement when comparing Solution versus Reference Scenario.

The results show reduced number of conflicts and conflict severity in Solution Scenarios with regards to Reference Scenario.

Assess potential benefits in Human Performance of COTTON improved Capacity Management processes in DAC short term planning phase

The chosen Human Performance indicators show an improvement when comparing Solution versus Reference Scenario.

No benefit in this KPA could be demonstrated as the number of control interventions is not significantly reduced and the balance between sectors is not achieved.

More details on the results of each of the objectives are provided below:

Technical Feasibility

The COTTON DAC solution converges in an optimal configuration very similar to the one selected by the ANSP for the day of operation. COTTON solution proves that is able to propose a feasible configuration from a single set of input traffic equalling the selection based on extensive experience and knowledge of the target airspace.

The other aspect evaluated is the computational time and effort to implement the COTTON DAC Solution. The computational requirements have to fit with the time available at each time horizon of


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the DAC process, in this case short-term planning horizon. Computation times of the experimental tool used in the execution of the validation exercise are not compatible with DAC process requirements, especially at -1H and -0.5H. However, this drawback is assumed to be overcome with the development and implementation of solution in an industrial operational environment, since times in the exercise have been calculated using a very first prototype with desktop computers.

Capacity

The assessment of capacity improvement has been based on two indicators:

- Number of unpredicted overload or underload (O/U) periods per sector.

- Number of remaining hotspots per sector: the (predicted or unpredicted) hotspots that cannot be mitigated thanks to the sector configuration optimisation process.

The first indicator evaluates, at sector level, the hit, false alarm and misses ratio of the O/U. Therefore, this indicator provides an estimation of the robustness of the chosen complexity metric facing uncertainty in demand. The second indicator has evaluated the number of peak and sustained overloads that are still present when using the actual demand at each time horizon and for the optimal configuration in each scenario.

The results for this objective, for forecast horizons 12H and 0.5H, have presented benefits in Solution Scenarios with respect to the Reference Scenario, both in terms of predictability of overloads and suitability of the sector configuration proposed. The effectiveness of the cost function has been demonstrated since it has discarded successfully configurations with peak overloads. Solutions 1 & 2 have similar improvement in the reduction of remaining hotspots, although Solutions 1 is more robust on overload prediction. The incorporation of uncertainty to the complexity evaluation adds instability to the overload prediction making it very dependent on the percentile of probabilistic Cognitive Complexity used to declare a hotspot.

Cost-efficiency

To evaluate cost-efficiency, the number of sectors in the optimal configuration for solution scenarios has been compared with the number of sectors in the reference configuration. The criteria to decide whether this objective has been achieved are:

If there is no reduction of the number of sectors, then there shall be a reduction of the average OCC/min and/or a more balanced CC among the sectors.

If there is a reduction of number of sectors, the average OCC/min and balance of CC between sectors shall be below a manageable level (i.e. no extra peak or sustained overloads with regards to the reference).

Associated indicators have shown a clear improvement with Solution Scenarios, either reducing the number of sectors while keeping average OCC/min and workload within acceptable levels, or reducing/ balancing average OCC/min and/or workload while maintaining the number of sectors. In the case that the number of sectors is maintained, the reduction/ balancing of OCC/min and/ or workload has been considered a net improvement since there is no negative trade-off with other KPAs (capacity and/ or HP).


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Safety

Two indicators have been defined to evaluate the success criterion associated to this KPA: the number of conflicts and the conflicts severity. The severity of a conflict between a pair of aircraft has been defined based on three parameters: lateral distance (NM in 2D), vertical distance (ft) and remaining time to conflict (seconds).

To evaluate this objective, the optimal configuration for each scenario at each time horizon has been implemented in a FTS tool and the forecasted demand traffic (according to the time horizon) has been run. All the detected conflicts have been scored, and the total sum of conflict severity obtained for the corresponding configuration.

The results for this objective have shown that for all time horizons the values of the indicators are reduced or at least maintained, except for 12 hours horizon with decrease in number of conflicts but increase in the severity indicator. In this case, the increase by only 2% has been be considered manageable.

Human Performance

In order to assess the effect of COTTON Solution Scenarios on Human Performance, the variation of the Air Traffic Control Interventions required in the Solution Scenarios with regards to the Reference Scenario has been qualitatively analysed based on the FTS outputs, which has implemented ENAIRE/CRIDA conflict resolution model. The interventions considered have been further categorised by level of difficulty in four levels.

Depending on the optimal configuration applied in each Solution Scenario, different number and levels of interventions have been obtained. The effect of sectorisation in the number of interventions is linked to the simulation rule that prioritise resolutions of conflicts not creating new conflicts within the same sector. Different sectorisations can give rise to choosing different resolutions, and that creates a domino effect, changing the output of the simulation.

Although the number of required control interventions has generally decreased in Solution Scenarios, this decrease has been below 10% in most of the cases, thus not considered enough to conclude in an improvement. Moreover, in many cases one of the sectors of the optimal sector configuration proposed has resulted overloaded with regards to the reference proposal, failing to find a more balanced workload distribution. For this reason, from the perspective of the evaluation of the needed ATCo interventions per sector, COTTON Solution Scenarios have not been able to provide a benefit with regards to the Reference Scenario in the framework of the validation exercise performed. This preliminary assessment based on FTS needs to be completed using Real Time Simulations.

2.4.4 Integrated DAC/FCA Capacity Management Process

Based on expert analysis on how to integrate the identified improvements, as well as on the results of integrated DAC/FCA validation exercise, COTTON has proposed a capacity management workflow in an integrated DAC/FCA environment. The following figure summarised this enhancement.

Integrated DAC/FCA process includes FCA and DAC use cases, however new use cases were identified to ensure the integration of the two modes of operations. Additionally, some of the FCA use cases in long-term planning are adapted and further developed in this environment. The figure below displays five use cases for integrated DAC/FCA proposed by COTTON and fully developed in [4] .


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Figure 16: Enhanced DAC/FCA Capacity Management proposed by COTTON

The results for each set of UCs/ planning phase are the following:

Long-term planning phase – Analysis of Complexity patterns and FCA maximum extension compatible with DAC design (UC-01, UC-02)

In the long-term process, the decision whether or not to implement FCA mode of operations is taken by the ATS service Provider based on complexity patterns analysis.

The integrated DAC/FCA solution offers two different modes of operation, the Fixed FCA DFL and the Dynamic FCA DFL. The process for both modes of operations differs slightly:

When determining fixed FCA DFL operations, complexity is analysed in a FL by FL basis for the entire day, the lowest flight level in which complexity is at a sufficient level to conduct FCA operations will be selected as the maximum extension of the FCA area.

Similarly, in dynamic DAC/FCA operations the complexity is assessed on a FL by FL basis but per time period (30minutes/1Hour/2Hour) depending on the tools and ability of controllers to be more dynamic. The FL’s are discarded for FCA operations whereby the complexity is too high.

The identification of the maximum extension of the FCA area has a direct impact on the DAC designs and configurations. DAC designs need to be compatible with the extension of the FCA areas identified; DAC elements need to provide the flexibility to deal with the dynamic changes of the FCA DFL. .


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Medium-term planning phase – Delineate and continually review DAC and FCA areas (UC-03, UC-04)

In the medium term, the process of delineating DAC and FCA operating areas starts by taking into account all performance area and their mutual trade-offs. For example, FCA could be fully applicable considering the result of the assessment of the maximum FCA area analysis (the previous use case) but the cost of transition from DAC to FCA in a specific time period is too high.

Therefore, delineation of the DAC and FCA Areas is a more pertinent process when considering Dynamic DAC/FCA mode of operations with the ultimate goal of defining the operating schemes and transition planning providing the lowest possible cost with minimum impact on the AU planned trajectories.

Short-term planning phase – Review and change DAC/FCA boundaries (UC-04, UC-05)

Performance of the delineation defined in the medium term is monitored from its initial identification in the medium term planning phase and during the short term process, continually assessing the quality of prediction (increasing/decreasing levels of uncertainty) or events which are affecting operating environment (e.g weather, military, ATCO availability.)

This process integrated within the standard DCB process and complementing it, takes into account the specificity of FCA operations, and moreover the cost of transition from a mode to another. It has to be emphasized that this level of dynamicity in decision-making and conversion from one to another mode of operations requires very high level of automation. That will enable to switch in the method of decision-making from semi-static based on predefined scenarios to fully dynamic where airspace is organised online in order not to provide just the maximum capacity but also lowest cost of the service.

Another element is that for quality support of the decision-making, it should be necessary to combine different complexity assessment methodology for different panning horizons and even different operational environments (e.g. geometrical complexity approach for the long-term and medium-term and solution space for short-term planning and execution phases).

A validation exercise was carried out. It consisted of the emulation of the process of capacity planning following the integrated DAC/FCA capacity management processes in medium-term planning phase through Fast Time Simulation runs using a traffic demand and advanced complexity metrics, incorporating uncertainty in the demand. The Use Cases covered by the exercise were DAC-UC5, DAC-UC6, DAC-UC7 for Dynamic Airspace Configuration, FCA-UC3, FCA-UC4, FCA-UC5 for Flight Centric ATC and DAC/FCA-UC3 and DAC/FCA-UC4 for integrated DAC/FCA.

The exercise implied the development and integration of geometrical complexity metrics and the assessment of uncertainty associated to the trajectories, the modification of Dynamic Airspace Configuration tools including decision criteria based on capacity expressed as a complexity threshold, as well as the development of a new trajectories/CWP allocation model for FCA solution based on trajectory complexity assessment.

The airspace used was the Hungarian Airspace (LHCCCTA ACC) in order to exploit the data and establish effective collaboration with Hungarian airspace experts involved in the exercise through a collaboration with EUROCONTROL, which are familiar with FCA concept. The high-level validation


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objective was to validate the improvements of the integrated enhanced DAC/FCA solution by evaluating the benefits concerning the isolated DAC and enhanced FCA processes.

Table 8: Integrated DAC/FCA Validation Exercise Summary of Results per KPA

Validation Objective Success Criterion Validation Results

Assess technical feasibility of Airspace/ATC mode allocation. Preliminary assessment of the operational usability of integrated FCA/DAC process supported by geometrical complexity metric and uncertainty assessment to delineate zones and periods allocated to FCA mode vs conventional/DAC ATC mode in medium-term planning.

Airspace/ATC mode allocation approach is defined with sufficient level of details for the specification of the support tool requirements and the definition of further research needs.

Even though it was not possible to use an automated support tool for the delineation of DAC/FCA, the process was described with sufficient details to enable the identification of tools requirements and definition of further research needs.

Provide evidence of preliminary operational acceptance of the new tool which supports FCA/DAC airspace allocator process, based on geometrical complexity assessment to determine which zones and periods go under FCA or DAC ATC mode.

Experts involved in FCA/DAC airspace allocation process consider that the airspace/ATC mode allocation proposed with the support of the tool is acceptable with regards complexity criteria and operational needs as specified in COTTON document D3.1 section 5 [4]

Despite the absence of the support tool, DAC/FCA boundaries delineated by both static and dynamic delineation methods have been successfully validated by the operational experts concluding in the operational feasibility of DAC and FCA in the allocated zones

Output provided with the support of the prototype tool to allocate ATC mode to airspace is in line with the operational needs/requirements as specified in COTTON document D3.1 section 5 [4]

The operational experts considered that the geometrical complexity values supporting the delineation of the airspace are in line with the operational expertise with regards traffic complexity.

Provide evidence of preliminary operational acceptance of the enhanced DAC tool (sector opening scheme optimiser), with incorporation of geometrical complexity metric and uncertainty assessment.

Experts involved in the enhanced DAC process consider that the proposed sector opening scheme is acceptable.

Experts involved in the analysis, consider that the configuration and sector opening scheme proposed by DAC tool incorporating geometrical metric and uncertainty are improved in operational point of view.


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Validation Objective Success Criterion Validation Results

Output provided by the enhanced DAC tool is in line with the operational needs/requirements with regards sector opening schemes optimisation criterion as specified in COTTON document D3.1 section 5 [4].

The results provided by enhanced DAC tools fulfil all the requirements specified in D3.1.

Provide evidence of preliminary operational acceptance of the new FCA tool including trajectory-based geometrical complexity metric and uncertainty assessment used to allocate the trajectories to the CWP.

Experts involved in the FCA trajectory allocation to CWP supported by the new tool consider that the allocation proposed is acceptable.

Expert involved consider that the trajectory allocation proposed is globally acceptable. However, it should be improved to take into consideration the reallocation of trajectories to CWP during the under loaded periods.

Output provided by the FCA prototype is in line with the operational needs/requirements with regards trajectory/CWP allocation workability and optimisation criterion as specified in COTTON document D3.1 section 5 [4].

Output provided by the FCA prototype is in line with the operational needs/requirements regarding trajectory/CWP allocation workability and optimisation criterion as specified in document D3.1 section 5. Positive

Assess potential benefits in Capacity of COTTON integrated DAC/FCA solution with respect to DAC and FCA solutions applied in separated mode for Capacity Management in medium-term planning

The chosen Capacity indicators show an improvement when comparing Solution versus Reference Scenarios

-99.9% in overloads, +67% in underloads, although limitations in results

Assess potential benefits in Cost-effectiveness of COTTON with respect to DAC and FCA solutions applied in separated mode for Capacity Management in medium-term planning.

The chosen Cost-effectiveness indicators show an improvement when comparing Solution versus Reference Scenarios

-7.6% in Controller Position hours, although limitations in results


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2.5 Technical Deliverables

Reference Title Delivery Date Dissemination Level

Description

D1.1 Project Management Plan 23/03/2018 Confidential

D2.1 Impact of trajectories’ uncertainty in existing complexity methodologies

20/07/2018 Public

D2.2 Innovative complexity and workload assessment to support future Capacity Management processes in TBO

25/11/2019 Public

D3.1 Enhanced Capacity Management considering DAC and FCA in Trajectory Based Operations

29/11/2019 Public

D4.1 Validation Plan 04/06/2019 Public

D4.2 Validation Report 25/11/2019 Public

D5.1 Exploitation and Dissemination plan 23/03/2018 Confidential

D5.2 Exploitation and Dissemination Report 25/10/2019 Confidential

D5.3 Final Project Results Report 10/12/2019 Public

Table 9: Project Deliverables

In the following paragraphs, a brief summary of each deliverable is presented:

D1.1 Project Management Plan: describes the Project Management Plan (PMP) of COTTON project according to the guidelines described in the Project Execution Guidelines for SESAR 2020 Exploratory Research document [17]. It describes how the project management processes shall be executed during the project lifecycle. It sets up also the project overview and scope, as well as the project organisation and structure.

D2.1 Impact of trajectories’ uncertainty in existing complexity methodologies: the objective of this deliverable is to identify the impact of trajectories’ uncertainty in existing complexity methodologies. For this purpose, the document describes the following tasks and their results:

Analysis of the characteristics of the DAC and FCA applications affecting complexity management.

Analysis of the complexity metrics applied in ATM, together with the opinion of sector experts, with the final result of identifying key Complexity Generators in Capacity Management.

Application of causal models, developing a methodology to evaluate the effect of the trajectory uncertainty on the results of the complexity metrics.

D2.2 Innovative complexity and workload assessment to support future Capacity Management processes in TBO: The document describes the activities performed in COTTON in the search of


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complexity metrics better adapted to support DAC and FCA processes and integrating the consideration of uncertainty:

Assessment of the suitability of the complexity metrics.

Description of the development activities preformed to improve complexity metrics.

Metric comparison tests and identification of the reference value of sector capacity for them.

Human Performance test description and results on the most influencing Complexity Generators on the ATCO workload in the DAC and FCA operation environment.

D3.1 Enhanced Capacity Management considering DAC and FCA in Trajectory Based Operations: This document describes the work developed to provide an Enhanced Capacity Management solution for DAC, FCA and integrated DAC/FCA solutions comprising:

Specification of complexity and uncertainty requirements to support capacity management.

Identification of the use cases affected by the use of advanced complexity metrics.

Definition of the enhanced capacity management processes.


D4.1 Validation Plan: This document defines COTTON validation activities, including the development of the validation objectives and validation scenarios, the selection of traffic samples, the configuration of the tools that will be used and the specification of the performance indicators to be measured.

D4.2 Validation Report: describes COTTON validation results, including the analysed validation objectives and scenarios, the simulated Use Cases, the selection of traffic samples, the configuration of the tools that were used and the measured performance indicators.

D5.1 Exploitation and Dissemination plan: This document describes the exploitation and dissemination activities planned to be undertaken by COTTON partners. COTTON dissemination and exploitation activities were identified and developed to ensure the proper usability and exploitation of COTTON delivered Operational Improvements. Dissemination and Exploitation is oriented to ensuring that its outputs fully satisfy stakeholders’ needs, linking the relevant participation of COTTON partners within the SESAR industrial research and facilitating further research or operational exploitation of COTTON outputs.

D5.2 Exploitation and Dissemination Report: This deliverable presents the dissemination and exploitation activity performed during the two years of COTTON project. It collects and analyses dissemination and exploitation results, reached target audience and obtained feedback. Dissemination and exploitation activities are assessed against the dissemination goals identified in COTTON D5.1 Dissemination and Exploitation Plan.

D5.3 Final Project Results Report: This document summarises COTTON developed activities and results. It presents the evidence of COTTON Solutions benefits and operational feasibility. Complementarily, their technological risks and a preliminary Plan for next R&D phases.


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3 Links to SESAR Programme

3.1 Contribution to the ATM Master Plan

SESAR Solutions, according to the definition in [21], are ‘new or improved operational procedures or technologies that aim to contribute to the modernisation of the European and global ATM system’. Furthermore, in SESAR EATM Portal [22], solutions are described as outputs from the SESAR Programme R&I activities, which relate to an Operational Improvement (OI) step or a small group of OI steps and its/their associated enablers. Therefore, the improvements provided by each solution are structured on Operational Improvements (OI) steps.

The potential OI steps that COTTON proposes, based on the objectives and expected results of the project, are summarised in the table below.

This project maturity gate verifies whether V1 (TRL2) maturity has been achieved, and the potential of the Solution to deliver ATM benefits. These are the required conditions to allow for the transition from ER (applications oriented) to IR (phase V2 – TRL4), or to a complementary/extension of V1 (TRL2) validation, taking place within the IR part of the programme [24] .

Code Name Project contribution Maturity at project start

Maturity at project end

COTTON-CM-01

DAC Capacity Management process explicitly taking into account the uncertainty inherent to 4D trajectory planning

COTTON project characterises and incorporates trajectory uncertainty in the airspace design and configuration process and in DCB processes. Unlike in todays’ ATM system, where uncertainty is hardly used, leading to less efficient DCB measures and non-optimal configurations, as the input traffic data might not be optimum for the actual traffic.

The uncertainty of the demand is translated into probability of occurrence of hotspots and/or underload periods and used to make more effective decisions on the airspace design and configuration process.

FO/AO V1/TRL2

COTTON-CM-02

FCA Capacity Management process explicitly taking into account the uncertainty inherent to 4D trajectory planning

COTTON project characterises and incorporates trajectory uncertainty in DCB processes. Unlike in todays’ ATM system, where it is hardly used leading to less efficient DCB measures. This also affects CWP planning and traffic allocation within FCA process, which uses traffic demand predictions as input for each step in the rolling development. The number of CWP planned as well as the traffic allocation for

FO/AO V1/TRL2


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the input traffic data might not be optimum for the actual traffic.

Demand uncertainty is translated into probability of flight bunching occurrence and used to take more effective decisions on the planning of CWPs number and optimum allocation of traffic.

COTTON-CM-03

Traffic complexity and workload assessment adapted to Trajectory Based Operations including uncertainty

Current complexity assessment methodologies are linked to the traditional airspace organization with pre-defined sectors, whereas in DAC solution predefined sectors will not exist, and in FCA solution there will be co-existence of several controllers operating in the same airspace. Therefore, new approaches for complexity assessment are required to support CM processes. This operational environment consists of a set of improved complexity assessment methodologies to better support DAC and FCA Capacity Management and incorporating uncertainty in the complexity assessment.

FO/AO V1/TRL2

COTTON-CM-04

An overall European ATM framework for Capacity Management in TBO integrating Dynamic Airspace Configuration (DAC) and Flight Centric ATC (FCA) solutions

DAC and FCA concepts are developed separately in SESAR 2020, there is a need to identify and develop the complementarity and interaction between these two solutions as part of an overall European ATM Capacity Management framework. This operational improvement identifies the conditions for application of each of them, and defines boundaries and interfaces between them in order to provide significant overall performance benefits for the European ATM system.

FO/AO FO/AO

Table 10: Project Maturity

3.2 Maturity Assessment

SESAR Maturity Criteria [23] provides the maturity assessment criteria that COTTON OI shall satisfy at the end of the project in order to allow their evolution to IR phase. The assessment of these criteria is shown in the following table.


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Table 11: ER/ IR Maturity Assessment

ID Criteria Satisfaction Rationale - Link to deliverables - Comments

OPS.ER.1 Has a potential new idea or concept been identified that employs a new scientific fact/principle?

OK Yes, the deliverables D3.1 and D2.2 presented and developed the three COTTON solutions (Sector Shape, Cognitive Complexity and Geometrical metric), together with the introduction of trajectory uncertainty in Capacity management processes.

OPS.ER.2 Have the basic scientific principles underpinning the idea/concept been identified?

OK Yes, they are identified in D2.1 and GA, which set the principles for the development of the complexity metrics and DCB models revision.

OPS.ER.3 Does the analysis of the "state of the art" show that the new concept / idea / technology fills a need?

OK D2.2 analyses the state of the art concluding that there is a need for incorporating the use of Complexity in Capacity Management through the use of advanced Complexity metrics. The state of the art analysis includes DAC/FCA limitations, complexity metrics characteristics, demand models, etc.

OPS.ER.4 Has the new concept or technology been described with sufficient detail? Does it describe a potentially useful new capability for the ATM system?

OK D3.1 describes in detail the processes and use cases consolidating COTTON concept as well as the new capabilities for the ATM system. This use cases are related to DAC and FCA environments.

OPS.ER.5 Are the relevant stakeholders and their expectations identified?

OK Yes, they are identified in D5.1 and D5.2 Exploitation and dissemination plan and report.

OPS.ER.6 Are there potential (sub)operating environments identified where, if deployed, the concept would bring performance benefits?

OK Yes, the En-route environment is the one identified by COTTON where the new concepts could bring valuable benefits. Also, the entire network would benefit from the COTTON solution.

SYS.ER.1 Has the potential impact of the concept/idea on the target architecture been identified and described?

OK Yes, it was analysed as part of D3.1, which contains a section to describe Enhanced DCB/DAC tools requirements.


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SYS.ER.2 Have automation needs e.g. tools required to support the concept/idea been identified and described?

OK D3.1 section “Enhanced DCB/DAC tool requirements” describes the supporting tools functions and requirements for this concept (demand forecast uncertainty assessment, enhanced complexity assessment, complexity thresholds evaluation method, identification of D/C imbalances with uncertainty assessment) and the particular needs for DAC, FCA and DAC/FCA integrated environments.

SYS.ER.3 Have initial functional requirements been documented?

OK Yes, they have been developed as part of WP3 activities and will evolve during the whole duration of the project. D3.1 presents this needed requirements. Also, in D2.2 the operational requirements for DAC and FCA are documented.

PER.ER.1 Has a feasibility study been performed to confirm the potential feasibility and usefulness of the new concept / idea / Technology being identified?

OK Yes, in the validation exercises, because the objective upon which VALP is develop is” To assess the potential usefulness and expected benefits of the refined Capacity Management processes as they are identified in WP3”.

PER.ER.2 Is there a documented analysis and description of the benefit and costs mechanisms and associated Influence Factors?

OK Yes, they are described in the GA and are further developed in the Exploitation and Dissemination Report

PER.ER.3 Has an initial cost / benefit assessment been produced?

Not applicable

No, it is not foreseen as part of COTTON activities.

PER.ER.4 Have the conceptual safety benefits and risks been identified?

Not applicable

No, it is not foreseen as part of COTTON activities

PER.ER.5 Have the conceptual security risks and benefits been identified?

Not applicable

No, it is not foreseen as part of COTTON activities

PER.ER.6 Have the conceptual environmental impacts been identified?

OK Yes, they are considered as part of the BIM identified in the GA, further developed in the exploitation and dissemination report, and considered as part of the Validation activities


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PER.ER.7 Have the conceptual Human Performance aspects been identified?

OK Yes, there were identified in the HF tests are also made in the framework of the project to support the development complexity metrics

VAL.ER.1 "Are the relevant R&D needs identified and documented?

OK They are described in the VALR and Conclusions Report

TRA.ER.1 Are there recommendations proposed for completing V1 (TRL-2)?

OK They are described in the VALR and Conclusions Report, where there are also some specific recommendations in the VALR for DAC and DAC/FCA integrated environment.


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4 Conclusion and Lessons Learned

4.1 Conclusions


Based on the limitation of existing complexity assessment methodologies presented in the D2.1 [2] and the identified DAC/FCA capacity management process requirements [4], the D2.2 report [3] presents the overview of the development activities of selected complexity assessment methodologies to include the trajectory uncertainty and to address the newly defined requirements of DAC and FCA capacity management process.

Each of selected metrics: Solution space, Cognitive Complexity and Geometrical metric, is described and mathematical formulation presented, followed by the detailed description of the improvements. Results of the preliminary verification tests performed to check that implemented improvements corresponds to the designed expectations. Finally, limitations of each methodology are listed and approaches for the further development explained.

Based on the metric improvements and requirements coverage, COTTON metric development activities are evaluated as successful.

Complexity metric assessment tests support the positive assessment of COTTON advanced complexity metrics. The Solution Space metric test has shown that metric implicitly considers the most influencing Complexity Generator presented in more mature and well-established complexity metrics. Additionally, the solution space provides a visual representation of possible re-routing opportunities (or a lack thereof) that would resonate well with how controllers generally solve problems.

Test on Geometrical and Cognitive metrics confirmed that metric temporal stability is increased with introduction of the demand uncertainty information into the complexity assessment. Same test revealed complementariness of two methods: the first covers more precisely instantaneous geometrical aspects of the traffic situations, while the second considers other mental workload factors.

Human performance test closes the loop of COTTON activities to analyse and further improve Complexity metrics supporting Capacity Management Processes. This test validates the hypothesis set along the project to build COTTON enhanced Complexity Metrics related to the influence of the Complexity Generators on the ATCOs effort in different operational environment (DAC, FCA and integrated DAC/FCA) and reinforced the results of the 1st COTTON workshop with respect to the most influencing Complexity Generators.

4.1.2 Enhanced Capacity Management Process

COTTON project proposes a holistic capacity management framework tackling different areas of improvement for two SESAR capacity solutions Dynamic Airspace Configuration and Flight Centric ATC, the detailed development of this proposal is contained in COTTON deliverable D3.1 [4].


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While traffic complexity and uncertainty of the demand are currently handled by the use of capacity buffers that are mainly evaluated through empirical approach, relying on operational expertise, COTTON approach proposes a conceptual approach supported by validation activities.

The idea is to exploit the information available within the trajectory-based operations processes, be it planned, actual or forecasted, to quantify the complexity of the traffic and its uncertainty, therefore plan the capacity resources that best fit the demand and optimise capacity cost.

In the same vein, COTTON explores the potential of integrating DAC and FCA solutions as an overall inclusive capacity solution through the analysis of the operational process that support its implementation and the assessment of its potential benefits in terms of capacity and cost-efficiency.

The work achieved in COTTON started with the adaptation of the use cases developed in DAC and FCA with the introduction of complexity assessment and uncertainty along the DCB timeline. As for the integration of DAC and FCA concepts, new use cases and operational workflows have been developed.

Through the analysis of the operational processes, the requirements for traffic complexity assessment and uncertainty quantification have been derived for DAC, FCA and integrate DAC/FCA processes.

The three-complexity assessment metrics enhanced in COTTON (see section 4.1.1) have been assessed against these requirements and further evolved to better address them. Following this work, an expert judgment analysis of the applicability of the metrics and associated uncertainty assessment have been carried out to provide practise guidelines of the best use of complexity metrics along the capacity management process.

The enhancement of the capacity management through complexity and uncertainty assessment required the adaptation of the automated tools that support that process. Since the identification of overloads and underloads has been adapted to that purpose, requirements for the enhancement of the sector opening scheme optimiser, sector configurations builder, the trajectory allocation to the Controller working position for FCA, as well as the delineation of boundaries for the integrated DAC/FCA process have been identified.

COTTON validation and organised workshops results conclude on a high potential operational acceptance of COTTON Enhanced Complexity metrics to support the different Capacity Management Use Cases along the DCB timeline. In particular, the use of Geometrical Metric and Cognitive metric in the capacity management tools/functions showed that it:

Fully exploits the potential of DAC concept providing a larger range of configuration solutions that flexibly adapt to the demand variations.

Better anticipate the identification of overloads and therefore proposes more efficient DAC solution in the day of operations.

Better allocate the trajectories to the Controllers Working Position for FCA with an improved workload balance.

Provide a consistent delineation of DAC/FCA boundaries corresponding to the traffic complexity levels that can be accommodated by DAC and FCA ATC modes respectively.


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Uncertainty has no negative impact on capacity in Flight Centric ATC as demonstrated in the validation of the processes of allocation of Traffic and Opening and Closure of CWP processes integrating Trajectory Uncertainty.

Uncertainty integration improves DAC configuration plan stability and offers an opportunity of increased flexibility to reflect the ANSPs business strategy in the Sector Configuration process.

Several potential operational applications of Solution Space complexity have been identified for DAC and FCA use cases along the DCB timeline, amongst them, Solution Space graphical interface has been seen as promising to support aircraft allocator decision-making. However, due to technical reasons, it could not be confirmed through COTTON validation.

4.1.3 Concept Benefits Assessment

COTTON fast time simulations have provided qualitative and quantitative results characterising the concept benefits COTTON solution provides.

The application of the COTTON Enhanced FCA use cases in the short-term planning phase, results in no negative impact on Capacity due to the fact of integrating uncertainty in the decision-making process. It shows however, an improvement of capacity when introducing the use Geometric Complexity Metric. The impact on Cost-efficiency, Human Performance and Safety were not investigated for Flight Centric ATC as they depend on the definition of a tolerable maximum complexity per Air Traffic Controller in FCA, which has not been investigated yet.

The following conclusions on performance were made for the application of the COTTON Enhanced DAC use cases in the short-term planning phase:

Capacity: COTTON enhanced complexity metrics show better detection rates for overloads than for underloads, which is also due to the absence of a criterion in the cost function for discarding configurations with underloads. It must be highlighted that better hotspot prediction allows for better planning of airspace configuration and more efficient use of capacity. The optimization of configuration leading to less remaining hotpots creates extra capacity thanks to the re-distribution of traffic associated to the configuration, and thus allows for a potential increase in the traffic accommodated. Furthermore, this improvement in planning allows better implementation of DCB measures. When introducing uncertainty assessment in the CM processes, Being Solution 1 Scenarios (integrating COTTON complexity enhancements without uncertainty) the ones showing better capacity performance.

Cost-efficiency: Associated indicators show a clear improvement with COTTON DAC Solution, either reducing the number of sectors while keeping average OCC/min and workload within acceptable levels, or reducing/balancing average OCC/min and/or workload while maintaining the number of sectors. In the case that the number of sectors is maintained, the reduction/ balancing of OCC/min and/ or workload is considered a net improvement since there is no negative trade-off with other KPAs (capacity and/ or Human Performance).

Safety: COTTON DAC Solution Scenarios also bring reduced number of conflicts and conflict severity with regards to Reference Scenario. In any case, the analysis of this KPA based on FTS is partial and Safety should be further analysed with higher fidelity simulation environment.


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Human Performance: Indicators chosen for this KPA are not showing improvement: control interventions are not considerably reduced and the balance between the sectors is not improved with regards to the Reference Scenarios simulated. This imbalance can be expected, since the cost function is giving more weight to the overall complexity values than the balance between sectors. Regarding control interventions and their difficulty, the assessment is based on the conflict resolution algorithm of the selected FTS tool, which is not as representative of a real environment as Real Time Simulations would be.

Finally, from the application of the COTTON Enhanced DAC/FCA use cases validation, it was shown, that DAC alone and combination of DAC and FCA modes of operation, using dynamic allocation, is highly performant in terms of Capacity and Cost-efficiency. That should bring significant benefit in respect of the baseline operations. Regarding the application of combination of DAC and FCA mode of operations with static delineation is questionable from performance point of view when compared to pure DAC operations. The potential benefits are too small to justify for the cost of the change and maintenance of two different operational modes.

Based on these results, it can be concluded that the improvement of overload identification allowing for better planning of airspace configuration together with optimised traffic allocation to balance the CWPs workload for both DAC and FCA, lead to a more efficient use of capacity and could potentially create extra capacity. However, further research is required within a complete DCB process including the integration of trajectory measures to ascertain the full impact on capacity.

Thanks to the combined use of complexity and DAC sectorisation principles, multiple sector configuration solutions can be provided to reduce overloads and balance the workload while maintaining the number of sectors, which concluded in net improvement of cost-efficiency.

Integrated DAC/FCA process has also shown improvements in terms of Capacity and Cost-efficiency.

When introducing uncertainty assessment in the Capacity Management process in view to improve overload prediction and thus the effectiveness of the capacity solution, validations showed an important increase of overload detection based on complexity with uncertainty, which was logically accompanied with an increase of controller’s hours to cope with the detected overload. These results show that the resulting solution is conservative as it aims to cover all identified overloads and their potential safety risk. This leads us to conclude that quantification of uncertainty should be part of an overall risk management strategy at all the planning phases. A strategy that ANSPs must develop according to their performance objectives in terms of capacity, cost, safety, etc.

4.2 Technical Lessons Learned


Solution Space

The main limitation of the solution-space metric is that it is currently not linked to sector capacity and controller workload. It only gives an indication how much flexibility aircraft have to circumvent potential hazards, i.e. conflicts and sector disturbances. Additionally, the metric does not yet incorporate wind effects, which plays an important role in 4D trajectory management since each aircraft has to meet an absolute time target at a specified waypoint. These limitations, together with a better way to capture uncertainty over a longer time frame, are opportunities for improvement.


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Although no well-founded conclusions can be drawn based on the results from one controller, and the fact that rather unrealistic traffic scenarios were used, the SME assessment did provide some insights regarding the usefulness of the solution space metric and its applicability to both DAC and FCA.

The main benefit of the solution space metric is that it implicitly combines and codes many parameters that are present in more mature and well-established complexity metrics (i.e., cognitive complexity and geometric complexity), such as the number of aircraft, sector geometry, routing structure, route interactions, etc. Additionally, the solution space is a visual representation of possible re-routing opportunities (or a lack thereof) that would resonate well with how controllers generally solve problems. The clear downside of this implicit coding in the form of solution spaces is that it does not seem to provide good results under all circumstances. Only in traffic situations featuring many route crossing and route interactions, the solution space metric appears to be at its best potential.

As currently no thresholds for the solution space complexity ratio can be defined related to opening or closing a CWP (in FCA) and/or re-configuring the airspace (DAC), the metric could be useful as a scale factor (or as an additional term) in the Cognitive Complexity and Geometric Approach complexity models instead of serving as a stand-alone metric. For example, especially in situations featuring sudden airspace restrictions and/or situations with many crossing traffic close to or during the TBO execution phase, the solution space metric could help to make the other complexity models more accurate in predicting workload levels and capacity limitations.

Cognitive Complexity Metric

The main limitation of Cognitive Complexity is the difficulty of incorporating trajectory uncertainty (multiple trajectory alternatives for a flight) and thus its application for long-term evaluations. This drawback is related to the conception of the metric as a tactical tool, so that the adaptation to long-term planning phase requires reconsidering the very structure of the calculations. With current formula it would be possible to account for certain level of trajectory uncertainty, but the computation time will be a limiting factor for applicability.

The computation time for obtaining statistical validity of metric values when incorporating uncertainty in time is currently too high so as the metric can support decision making in very short-term planning. Nevertheless, this limitation is more linked to the metric coding and the computation capability of the machines than to the metric concept itself, so it is likely to be overcome in the near future.

Besides, the metric is focused on workload assessment based on allocation of ATCOs per sector. For sector-less environments such as FCA, again the structure of the calculations would need to be reviewed in order to re-focus the assessment per set of flights rather than per sector.

Geometrical Complexity Metric

The main limitation of geometrical metric is inability to consider all complexity generators, identified by DAC/FCA capacity management processes requirements mainly for the short-term phase. In addition, geometrical complexity only relates to a part of the ATCO workload, not taking procedures and controllers’ physical tasks in the complexity assessment, hence its use in the short-term and execution phase is limited.

Furthermore, trajectory position uncertainty, that is mainly relevant for the complexity assessment in short-term phase, is difficult to use due to needed input data consisting of the position and speed vector at every instance of time. Although position uncertainty could be easily modelled using


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longitudinal and lateral position deviation, defining speed distribution to corresponding position is not trivial. Possible approximative approach would be to consider only position uncertainty but to assume same speed vector to all possible positions at the given time that is equivalent to the speed vector of the nominal position for that time instance.

The approach to calculate reference complexity value in the case of the demand uncertainty considers calculation of the complexity expected value taking into account probability distribution of the position time uncertainty and trajectory uncertainty. The computation of the reference value is very efficient at does not require huge computational effort. However, there is no closed-form solution to evaluate probability distribution of the complexity metric, that must be estimated using simulation. This is computationally very intensive process and additional approaches will be explored. For example, Markov’s inequality and/or Chebyshev’s inequality could be used to estimate probability that complexity value will be lower or in the given bound. These bounds in addition to the referent value might provide sufficient information to efficiently perform capacity management process.

The metric value interpretation is also listed as a limitation since capacity value must be experimentally estimated. In the proposed approach, for the capacity referent value identification, dispersion of calculated ‘acceptable’ complexity values represents a big problem that require further research.

Human Performance Tests

The evaluation of the impact that Complexity Generators in human response in DAC and FCA scenarios

through human performance experiments confirmed the key complexity generators identified through

expert judgment.

The fact of interpreting results under both perspectives, influence and severity, avoids tasks’ classification error as convoluted according to parameters’ evolution when these are found in levels that show low convolution. It could be that a parameter varies with tasks’ level of difficulty variation and even in this case, not be able to reach critic levels. However, when comparing influence and severity results, similar patterns are observed.

The designed exercises and tests, as well as the proposed metrics, allow a comparison of reference, DAC and FCA scenarios. It does not try to obtain absolute values, but to establish a relative comparison between scenarios and complexity generators. This methodology can be applied to evaluate other scenarios or significant events.

The proposed matrix classifies the difficulty observed in the human response taking into account the two perspectives: influence and severity.

Considering the COTTON project objectives, the results obtained reinforce some assumptions, made in the definition of complexity metrics, that were purely based on the expert’s judgment.

- The anticipated conflicts are a fundamental parameter in the definition of complexity. In particular, when applied to FCA, conflicts will generate a greater effort, due to the necessary coordination between controllers.

- The FCA scenario allows the controller to anticipate the events that will take place in advance, which contributes to improving the complexity in a scenario.


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- Unforeseen aircraft (or unforeseen actions) will generate greater complexity due to the lack of forecast of the effect that will occur in the scenario.

- The evolving aircraft will generate a greater effort, due to the need to foresee the conflicts or hot spots they will produce.

To continue with its application and obtain more conclusive results, the lines of development must focus on two areas:

Calculation hypothesis: The chosen weights for the three categories, Stress Level, Cognitive Working Memory Load and Cognitive Load due to Visual Attention, are result of experts ’judgement. It has been considered that Stress Level has the biggest impact in human response followed by Cognitive Load due to Visual Attention, in regard to cognitive load, which also has a bigger impact in the ATCO compared to Cognitive Working Memory Load, given the fact that information , which is going to be processed, needs, in advance, to be identified.

These conclusions have been based on different references and previous studies, but it is necessary to deepen these hypotheses

Simulation exercises: The simulation exercises have been developed on a platform that has limited capabilities, which has made some ATCO functions adapted to the possibilities of the tool. It is necessary to improve the exercises and the possibilities of the simulation platform to improve the conclusions and results of the exercises.

4.2.2 Capacity Management Process

Besides the conclusions presented in 4.1.2 on the use of complexity metrics in the CM process, improvements of the operational process and supporting tools have been identified and should encompass the following:

Complexity metrics enhancement to capture all processes impacting complexity in FCA mode of operations, including the coordination workload due to conflict resolution by two different controllers.

Introduction of stability criteria in the requirements of DAC sector configuration optimisation process, such as the frequency and magnitude of the sector configuration changes, in order to restrain its sensitivity to the traffic complexity, which could be operationally counterproductive.

Reinforcement of sector workload balancing criteria in the cost function of DAC sector opening scheme and sector configuration optimiser in order to reduce the number of underloads, which could potentially impact capacity and cost performance.

Definition of trajectory re-allocation process within FCA trajectory allocation to potentially enhance capacity and cost performance.

Analysis of the combined usage of complexity metrics (solution space, cognitive complexity and geometric complexity metrics), since they provide complementary information that could be exploited to enhance the capacity management process. The requirements for automated tools and HMI supporting this combined usage should also be defined.


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Refinement of the description of the capacity management process by including the definition of Human and Automation roles in the process, taking into account the results of the validation.

Refinement and fine-tuning of uncertainty and probabilistic complexity to ensure that capacity solutions are identified according to the ANSP business strategy and properly presented to the user to facilitate decision making according to this strategy. Further integration of uncertainty models shall incorporate the upcoming improvements in the demand prediction model are incorporated (as for example to account for other sources of uncertainty such as trajectory uncertainty or meteorology).

Integrate the capacity management process within DCB process to assess the trade-offs between configuration changes or short-term trajectory-based measures, allowing the user to assess the performance of all proposed measures. The requirements for automated tools and HMI supporting this integrated DAC/DCB process should also be defined.

Analysis of the impact of ARES/DMA on complexity and capacity management process.

Analysis is required to ascertain what level of DAC dynamicity is most compatible and optimal with FCA mode of operations (both static and dynamic).

Once demand models for long-term and mid-term planning become available, analyse the impact of complexity assessment with uncertainty as a seamless capacity management process taking into account ANSPs business strategy. The requirements for automated tools and HMI supporting this capacity process should be also defined.

It must be noted that within COTTON project only the two FCA use cases identified for short-term planning phase (FCA Use Case 4 “Opening and closure of CWP” and FCA Use Case 5 “Allocation of Traffic entering the FCA”) were analysed in the Fast Time Simulation. The impact of these use cases on Cost-efficiency (CEF2), Human Performance (HP) and Safety (SAF) were not investigated in this exercise as they depend on the definition of a tolerable maximum complexity per Air Traffic Controller in FCA which has not yet been investigated neither in SESAR2020 PJ.10-01b nor COTTON. Further research and development activities on these Key Performance Areas in short-time planning phase as well as validations in medium-term and long-term planning phase should be analysed in the next steps.

Finally, as the integrated DAC/FCA process within COTTON project does not achieve the maturity level V1- TRL 2, staying in the fundamental research phase, a further development is required.

4.2.3 Concept Benefits Assessment

The performance of the workload prediction algorithms for allocation in FCA could only be measured using the same algorithms. For a more profound conclusion, a Real Time Simulation with ATCOs in the loop and constant workload rating by humans is recommended.

Within COTTON project only the two FCA use cases identified for short-term planning phase (FCA Use Case 4 “Opening and closure of CWP” and FCA Use Case 5 “Allocation of Traffic entering the FCA”) were analysed in the Fast Time Simulation. The impact of these use cases on Cost-efficiency (CEF2), Human Performance (HP) and Safety (SAF) were not investigated in this exercise as they depend on the definition of a tolerable maximum complexity per Air Traffic Controller in FCA which has not yet


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been investigated neither in SESAR2020 PJ.10-01b nor COTTON. Further research and development activities on these Key Performance Areas in short-time planning phase as well as validations in medium-term and long-term planning phase should be analysed in the next steps.

For the DAC environment recommendations were made for concept refinement, technical feasibility, performance assessment and representativeness of results:

The stability of the optimal configuration selection along time horizons must be ensured to avoid unnecessary changes in very short-term with no significant increase in performance benefits and Human involvement in the decision-making is crucial to account for aspects that cannot be fully automated.

Cost functions used need refinement in terms of Weight of Sector Shape metric, underload detection, stability of configuration selection and strategy of the ANSP.

The presentation of potential benefits of each configuration per KPA should be provided as part of the solution, in order to help decision-making. The development of an HMI is necessary to ensure than the solution is usable by LTMs and that they can tune the presentation of results to their needs.

The study should be enlarged for more scenarios to account wider set of operational scenarios, greater or lower level of uncertainty, consideration of other types of uncertainty and further testing of configuration results in FTS.

For the integrated DAC/FCA environment the following recommendation were defined:

ATFCM short-term planning should be integrated with the ATC planning in the frame of INAP both for DAC and combined DAC / FCA.

Procedures and more sophisticated tools for manpower (ATCO) planning and rostering should be in place to enable full benefit of Complexity prediction in DAC and combined DAC and FCA.

RTS should be performed in order to validate further thresholds and the buffers used for decision making.

Allocation and Re-allocation process used in FCA should be fully integrated with the DAC processes in order to explore the potential benefits.

4.3 Plan for next R&D phase (Next steps)

COTTON provides input to SESAR IR Wave 2, where the work done is planned to continue developing, but starting with a higher level of maturity in almost all of the COTTON delivering OIs. These OIs, shown in section 3.1, aim to contribute to the already existing SESAR Solutions. The candidates SESAR wave 2 solutions COTTON, which may benefit from COTTON improvements are the following:

Solution 44: Dynamic Airspace Configurations (DAC).

Solution 45: Enhanced Network Traffic Prediction and shared complexity representation.

Solution 73: Flight Centred ATC and Improved Distribution of Separation Responsibility in ATC.


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After those solutions are further developed in Wave 2, a follow-up of COTTON, meaning a further research, would be needed to focus on developing technical enablers for the enhanced DAC and FCA processes, including the integrated FCA and DAC process. In all these capacity management processes will be necessary to take into account the different complexity metrics and consider the uncertainty of the demand. COTTON follow-up activities should take into account the conclusions and technical recommendations contained in this report, section 4) as well as integrate the advance made in the demand models carried out in so far in for example SESAR wave 2 Solution 53 Improve Tactical Trajectory Prediction.

The technical enablers mentioned above would be the tools supporting the execution of the DAC and FCA processes operationally. These enablers could achieve its full development and integration within DCB at the industrial research phase, potentially in Wave 3, when tools will be further developed (up to pre-deployment TRL), together with the addressing of the integration of FCA and DAC together with the required supporting tools.

Figure 17. COTTON possible Plan for next R&D phases.


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5 References

5.1 Project Deliverables

[1] COTTON, ‘Project Management Plan’, D1.1, v00.01.00, March 2018

[2] COTTON, ‘Impact of trajectories’ uncertainty in existing complexity methodologies’, D2.1, v00.01.01, July 2018

[3] COTTON, ‘Innovative complexity and workload assessment to support future Capacity Management processes in TBO’, D2.2, February 2019.

[4] COTTON, ‘Enhanced Capacity Management considering DAC and FCA in Trajectory Based Operations’, D3.1, v00.01.01, June 2019

[5] COTTON, ‘Validation Plan’, D4.1, v00.03.00, June 2019

[6] COTTON, ‘Validation Report’, D4.2, v00.01.00, November 2019

[7] COTTON, ‘Dissemination Plan’, D5.1, v00.01.00, March 2018

[8] COTTON, ‘Dissemination Report’, D5.2, v00.01.00, October 2019

5.2 Project Publications

[9] F. Gómez and E. Puntero, “COTTON Workoshop #1 Minutes,” Madrid, 2018.

[10] Alligier R, Gianazza D, Durand N. “Machine learning applied to airspeed prediction during climb.” In: 11th USA/Europe ATM R&D Seminar; 23-26 June 2015; Lisbon, Portugal.

[11] Knorr D and Walter L. Trajectory uncertainty and the impact on sector complexity and workload. In: SESAR Innovation Days, 29 November – 1 December 2011; Toulouse, France

[12] Mueller TK, Sorensen JA, Couluris GJ. Strategic aircraft trajectory prediction uncertainty and statistical sector traffic load modeling. In: AIAA Guidance, Navigation and Control Conference; 05-08 August 2002; Monterrey, California, USA.

[13] E. Casado et al, “Techniques to determine trajectory uncertainty and modelling”. In COPTRA D2.1, February 2017; Madrid, Spain.

[14] Marina Sergeeva, Daniel Delahaye, Leila Zerrouki, Nick Schede, “Dynamic Airpace Configurations Generated by Evolutionary Algorithms”, DASC 2015 IEEE/AIAA 34th Digital Avionics Systems Conference, Sep 2015, Prague, Czech Republic. IEEE, pp.1F2-1-1F2-15/978-1-4799-8939-3, Digital Avionics Systems Conference (DASC), 2015 IEEE/AIAA 34th.

[15] Marina Sergeeva, Daniel Delahaye, Catherine Mancel, Leila Zerrouki, Nick Schede. 3D Sectors Design by Genetic Algorithm Towards Automated Sectorisation. 5th SESAR Innovation days, Dec 2015, Bologna, Italy.


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[16] Project website

5.3 Other

[17] Project Execution Guidelines for SESAR 2020 Exploratory Research, Edition 01.00.00, 08/02/2016

[18] European ATM Master Plan

[19] COTTON, 783222_Annex 1 - Description of the action (part B), - COTTON-H2020-SESAR-2016-2; ER3-03-2016 “Optimised ATM Network Services: TBO”.

[20] Name of project, Title of document, Identification number, Edition, date

[21] SESAR solutions explained, SESAR website: https://www.sesarju.eu/activities-solutions

[22] SESAR EATM Portal website: https://www.eatmportal.eu,

[23] SESAR, SESAR Maturity Criteria_ (1.5), 15th October 2018.

[24] SESAR, Project Handbook (Programme Execution Guidance), 18th December 2018

[25] SESAR2020 PJ.10-01b, V2 VALR, 2019

[26] EuroScope, 2019. [Online]. Available: https://www.euroscope.hu/wp/.

[27] SESAR, SESAR Solution 08.01 Validation Report (VALR) for V2 - Part I, 2019.

https://www.atmmasterplan.eu/

https://www.sesarju.eu/activities-solutions

https://www.eatmportal.eu/

https://www.euroscope.hu/wp/


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A.1 Acronyms and Terminology Term Definition

AC Aircraft

ACC Area Control Centre

ANSP Air Navigation Service Provider

AO Aircraft Operator

APPR Approach

ARES Airspace Reservation

ATC Air Traffic Control

ATCo Air Traffic Controller

ATFCM Air Traffic Flow and Capacity Management

ATM Air Traffic Management

ATO Actual Time overfly

ATS Air Traffic Service

AU Airspace User

BIM Benefit Impact Mechanisms

CC Cognitive Complexity

CDM Collaborative Decision Making

CEF Cost Efficiency

CG Complexity Generator

CM Capacity Management

COTTON Capacity Optimisation in Trajectory-Based Operations

CWP Controller Working Position

DAC Dynamic Airspace Configuration

DCB Demand Capacity Balancing

DFL Dynamic Flight Level

DMA Dynamic Mobile Area


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EEG Electroencephalography

ER Exploratory Research

ETO Estimated Time of Over-fly

FAB Functional Airspace Block

FCA Flight Centric ATC

FIR Flight Information Region

FL Flight Level

FMP Flight Management Position

FTS Fast Time Simulation

GA Grant Agreement

GC Geometric Complexity

HF Human Factors

HMI Human Machine Interface

HP Human Performance

ID Identifier

INAP Integrated Network Management and extended ATC Planning Function

IR Industrial Research

ISA Instantaneous Self Assessment

KPA Key Performance Area

LT Long Term

MT Medium Term

NM Network Manager

OBJ Objective

OCC Occupancy

OI Operational Improvement

PHARE Program for Harmonised ATM Research in EUROCONTROL

PMP Project Management Plan


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REF Reference Scenario

RTS Real Time Simulation

SAF Safety

SESAR Single European Sky ATM Research Programme

SESAR Programme The programme that defines the Research and Development activities and Projects for the SJU.

SJU SESAR Joint Undertaking (Agency of the European Commission)

SME Subject Matter Expert

SWIM System Wide Information Management

TBO Trajectory-Based Operation

TBX Trajectory-Based Complexity

TCM Traffic Complexity Management

TRL Technological Readiness Level

UC Use Case

VALP Validation Plan

VALR Validation Report

WL Workload

WP Work Package

Table 12: Acronyms and technology

d5.3 final project results reportcotton-er.eu/results/cotton d5.3 final project results...d5.3 final...

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