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3D Sectors Design By Genetic Algorithm Towards Automated Sectorisation Marina Sergeeva, Daniel Delahaye, Catherine Mancel (ENAC) Leila Zerrouki and Nick Schede (EUROCONTROL) 5th SESAR Innovation Days, Bologna, 2015 1 Sergeeva M. (ENAC), 3D

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3D Sectors Design By Genetic Algorithm Towards Automated Sectorisation

Marina Sergeeva, Daniel Delahaye, Catherine Mancel (ENAC) Leila Zerrouki and Nick Schede (EUROCONTROL) 5th SESAR Innovation Days, Bologna, 2015

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

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Introduction

An elementary sector is defined as a volume of the

airspace, within which the air traffic controller can perform his controlling function.

Area Control Centers (ACC) consists of several

elementary sectors.

When traffic demand capacity issue is identified or

change of traffic patterns occurs a new sector design of the ACC should be proposed.

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

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Motivation

Implementation of the Free Routing concept.
Continuous air traffic growth congested

airspace.

Limited flexibility of current sector design.
Complexity of the sector design task.

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

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Sector design problem

The airspace sector design problem consists in

searching a partition of a given airspace domain D into a set of N operationally workable sectors [s1, ... ,sn].

The quality of the sector design is evaluated

according several geometrical and operational constraints.

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

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Problem Description  workload imbalance between elementary sectors

The workload is computed as: (crossing time for all aircraft) * (time to monitor one aircraft) + (total number of conflicts) * (time to solve one conflict)

 ATC coordination workload

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

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Problem Description

 the number of flight re-entry events  the number of entry conflict points close to the sector borders  the number of short crossings  shapes of sectors such as ”balconies”  connectivity

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

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Pre-processing step

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

7 3D airspace discretized using grid cells (<5NM) projected on the 2D plane New cells built using k-means algorithm

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Preparing airspace blocks for sector building process

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

8 Airspace blocks in 3D

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Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

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ACC Airspace 3D blocks

Preparing airspace blocks for sector building process

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Sector building process

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

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3D extension Process of building sectors in 2D

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Solution representation

The solution of the sector design process can be

represented in the following form:

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

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With number of building blocks > 100 and the number of

layers > 2 the number of possible combinations is big stochastic optimization is required.

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Evolutionary Algorithm

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

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Objective Function

workload imbalance between all elementary sectors
total flow cuts
total number of re-entries
total number of short-crossings
total number of entering conflicts
total number of “balconies”
- proportion coefficients

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

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ASTAAC Interface

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

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Results obtained with ASTAAC

for Maastricht/Amsterdam Airspace (EDYYDUTA)
for 12/07/2014 11h-13h (peak hours)
proportion coefficients of the objective function:

Scenario 1: =

0.3, = = 0.25,

=0.4,

Scenario 2: =

0.6, = = 0.25,

=0.5,

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

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Results: Scenario 1

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

16 Upper layer Lower layer Traffic data: for 12/07/2014 11h-13h for Maastricht/Amsterdam Airspace (EDYYDUTA).

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Results: Scenario 1

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

17 Scenario 1 Original/Reference Scenario

sector workload short‐cross e‐conflicts re‐entr 4273 4 1 4058 3 1 2 3642 3 3 3695 1 4 3923 3 5 4426 4 sector workload short‐cross e‐conflicts re‐entr 3008 1 993 4 2 5663 4 3 1928 2 1 1 4 8493 1 3 5 3140 6 2

Traffic data: for 12/07/2014 11h-13h

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Results: Scenario 1

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

18 Traffic data: for 12/07/2014 11h-13h

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Results: Scenario 2

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

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sector workload short‐cross e‐conflicts re‐entr 2817 3 1 6116 2 1 2 3183 3 1 3 3327 1 4 5073 1 5 3501 2

Scenario 2

sector workload short‐cross e‐conflicts re‐entr 3008 1 993 4 2 5663 4 3 1928 2 1 1 4 8493 1 3 5 3140 6 2

Original/Reference Scenario Traffic data: for 12/07/2014 11h-13h for Maastricht/Amsterdam Airspace (EDYYDUTA).

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Results: Scenario 2

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

20 Traffic data: for 12/07/2014 11h-13h

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Conclusions

Proposed sector design algorithm is able to provide very

satisfying design regrading workload balancing and sectors shape.

Proposed algorithm is flexible and can be calibrated according to

different preferences.

Next step includes adding new complexity metric for the

workload evaluation.

Algorithm can be adapted for Dynamic Sector Configurations.

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm

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Thank you for your attention.

Sergeeva M. (ENAC), 3D Sectors Design by Genetic Algorithm