Optimum Airspace Partitioning for Center/Sector Boundary Design

Arash YousefiGeorge L. Donohue

Research Sponsors: NASA ARC, FAA, Metron Aviation Inc.

1st International Conference on Research in Air Transportation - ICRAT 2004,

November 22-24 2004, Zilina, Slovakia

Current Sectorization Has Historical – Not Analytical Origins

Traffic Is Not Uniformly Distributed Among ARTCCs – Productivity Overhead Concern

Source: FAA Factbook, March 2004. URL:

http://www.atctraining.faa.gov/factbook

Aircraft Handled (000's), Jan-Dec 2003

2,975

2,959

2,852

2,805

2,713

2,595

2,274

2,228

2,182

2,131

2,053

2,041

2,021

2,005

1,780

1,701

1,684

1,601

1,461

1,272

0 500 1,000 1,500 2,000 2,500 3,000

ZOB

ZTL

ZAU

ZNY

ZID

ZDC

ZJX

ZME

ZMA

ZFW

ZKC

ZMP

ZAL

ZHU

ZBW

ZAB

ZDV

ZOA

ZLC

ZSE

Count, in thousands

Given: Demand Profiles and Airport locations Desired: Optimum Center/sector Boundaries?

Optimization Parameter:ATC Workload (Modeling)

ATC workload is divided to 4 variables1. Horizontal Movement Workload (WLHM),

2. Conflict Detection and Resolution Workload (WLCDR),

3. Coordination Workload (WLC),

4. Altitude-Change Workload (WLAC).

In each sector or volume of airspace during a given time-interval:

( , , , )TotalWL WLHM WLCDR WLC WLACMore details:Yousefi, A., Donohue, G. L., and Qureshi, M. Q., “Investigation of En route Metrics for Model Validation and Airspace Design”, Proceeding of the 5th USA/Europe Air Traffic Management R&D Conference, Budapest, Hungary, June 2003.

Airspace Partitioning for Optimum Boundary Definition Airspace of 20 CON US ARTCCs is divided to three altitude layers with

2,566 cells. Disregarding the existing Center and sector boundaries. Hex-Cells are airspace elements and we compute complexity and

workload metrics for each cell based on historic flight data and simulation.

24 nm=0.4 degree lat/long

over FL310

FL210-FL310

below FL210

1.Large enough to capture conflicts2.Small enough for enough resolution

Hexagonal Grid Selection Criteria

Common sides between hex-cells within a cluster. Computationally less expensive than triangle. Avoid the acute and right angles in triangle & rectangle

that may result to short transit times for aircraft passing close to the edges.

RectangleHexagon TriangleClustering Direction RectangleHexagon TriangleClustering Direction

Optimum Airspace Design Process

Create hex-cell mesh

In 3 layers

2,566 in each layer

Actual traffic from ETMS

Last Filed routes

~45K daily flights

TAAM Simulation

Defining design-period

Create seeds for potential sectors

OptimizationRepresentation of new

sector boundaries

Airspace Complexity Visualizer (ACV)

Hex-cell assignments

WL calculation for each hex-cell for 10 min bins

Data Pre-processing Post-processing & visualization

Simulation/Optimization

Traffic variables

TAAM Simulation

~45 K Daily Flights from ETMS

Last Filed routes

Run Time=8.5 hrs

WL Trend Throughout the Day

Low altitude layer

High altitude layer

Defining a Design-PeriodDesign Period

Clustering Hex-cells to Construct sectors/Centers

Clustering Algorithm for ARTCC Boundary Design

Given: Demand profile and location of current ARTCCs Desired: What are the best ARTCCs to be opened and

what is the best boundary?

SUBJECT TO: avoiding highly concave ARTCCS number of ARTCCs are given some other ordinary constraints (e.g. assignment of each hex-cell to a single ARTCC, etc)

MIN (variation of workload among ARTCCs)MIN (SUM of distances from each hex-cell to current Center locations)MIN (Maximum distance between the hex-cell and the seed)

Locational Analysis & Facility Location Problems

GIVEN:- I = {1, ..., n} set of candidate locations for facilities

- J = {1, ..., m} set of demand points

Candidate location for facility

demand point

Not opened

Seed j

Hex-cell center i

d max

d5

d4

d3d2

d1

1

max( )

( )

SUBJECT TO

...

...

n

ii

MIN d

MIN d

MIN variation of workload among sectors

Clustering Algorithm for ARTCC Boundary Design

MINIMIZE (variation of workload among ARTCCs)

MINIMIZE (SUM of distances from each hex-cell to the seed)

MINIMIZE (Max distance between the hex-cell and the seed)

ARTCC Boundary Re-design (Keeping 20 Centers, Changing the boundaries)

ARTCC Boundary Re-design (Keeping 20 Centers, Changing the boundaries)

ABQ

Reducing # of ARTCCs to 18

Reducing # of ARTCCs to 5

ABQ

JFK, WL=58,760 -Optimization 1- MIN WL Variation & 2- MIN SUM distance & 3- MIN MAX distance

Reducing # of ARTCCs to 4

ABQ

-Optimization 1- MIN WL Variation & 2- MIN SUM distance & 3- MIN MAX distance

Clustering Algorithm For Sector Design

Given Optimum Center Boundaries, Find the Optimum Sector Boundaries Similar to Center Boundary problems Combinatorial minimization problem

SUBJECT TO: sector contiguity avoiding highly concave sectors number of sectors is limited avoid extremely large sectors some other ordinary constraints (e.g. assignment of each hex-cell to a single sector, etc)

MIN (variation of workload among sectors)

Conclusion & Future Work

Clustering algorithms appear to produce reasonable results both for Center and Sector boundary design Result is Formally an Optimum Solution for Chosen Object Function

Optimization approach allows additional constraints (radar coverage, avoiding large airports close to boundaries, etc)

Cost - Benefit analysis for selection of best ARTCCs should be done (if goal is Overhead Reduction)

Extension of sectorization process for each altitude layer within each ARTCC Using Com or Nav Aids as seeds or put the seeds along the

major traffic flow paths One could use RAMS or FACET instead of TAAM

NOTE: As an academic research, so far the intention has been to develop a partitioning METHODOLOGY. Future IV&V and cost benefit analysis are essential