Optimum Airspace Partitioning for Center/Sector Boundary Design

1. Optimum Airspace Partitioning for Center/Sector Boundary Design Arash Yousefi George 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

2. Current Sectorization Has Historical – Not Analytical Origins

3. Traffic Is Not Uniformly Distributed Among ARTCCs – Productivity Overhead Concern Source: FAA Factbook, March 2004. URL: http://www.atctraining.faa.gov/factbook

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

5. Optimization Parameter:ATC Workload (Modeling) • ATC workload is divided to 4 variables • Horizontal Movement Workload (WLHM), • Conflict Detection and Resolution Workload (WLCDR), • Coordination Workload (WLC), • Altitude-Change Workload (WLAC). • In each sector or volume of airspace during a given time-interval: More 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.

6. 24 nm=0.4 degree lat/long over FL310 FL210-FL310 below FL210 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. Large enough to capture conflicts Small enough for enough resolution

7. 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.

8. Optimum Airspace Design Process Post-processing & visualization Data Pre-processing Simulation/Optimization • Create hex-cell mesh • In 3 layers • 2,566 in each layer WL calculation for each hex-cell for 10 min bins TAAM Simulation Airspace Complexity Visualizer (ACV) Actual traffic from ETMS • Last Filed routes • ~45K daily flights Traffic variables Defining design-period Create seeds for potential sectors Representation of new sector boundaries Optimization Hex-cell assignments

9. TAAM Simulation • ~45 K Daily Flights from ETMS • Last Filed routes • Run Time=8.5 hrs

10. WL Trend Throughout the Day Low altitude layer High altitude layer

11. Design Period Defining a Design-Period

12. Clustering Hex-cells to Construct sectors/Centers

13. 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? 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) • 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)

14. Not opened 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

15. Clustering Algorithm for ARTCC Boundary Design dmax d4 d5 d1 Seed j d2 d3 Hex-cell center i

16. MINIMIZE (variation of workload among ARTCCs)

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

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

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

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

21. Reducing # of ARTCCs to 18

22. ABQ • Optimization 1- MIN WL Variation & 2- MIN SUM distance & 3- MIN MAX distance JFK, WL=58,760 Reducing # of ARTCCs to 5

23. ABQ • Optimization 1- MIN WL Variation & 2- MIN SUM distance & 3- MIN MAX distance Reducing # of ARTCCs to 4

24. Clustering Algorithm For Sector Design • Given Optimum Center Boundaries, Find the Optimum Sector Boundaries • Similar to Center Boundary problems • Combinatorial minimization problem MIN (variation of workload among sectors) • 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)

25. 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