Optimum Airspace Partitioning for Center/Sector Boundary Design Arash Yousefi George L. Donohue...
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Transcript of Optimum Airspace Partitioning for Center/Sector Boundary Design Arash Yousefi George L. Donohue...
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