Optimum Airspace Partitioning for Center/Sector Boundary Design Arash Yousefi George L. Donohue Research Sponsors: NASA ARC, FAA, Metron Aviation Inc. 1 st International Conference on Research in Air Transportation - ICRAT 2004, November , Zilina, Slovakia

Current Sectorization Has Historical – Not Analytical Origins

Traffic Is Not Uniformly Distributed Among ARTCCs – Productivity Overhead Concern Source: FAA Factbook, March URL:

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

Optimization Parameter: ATC Workload (Modeling)  ATC workload is divided to 4 variables 1.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: 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.

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 conflicts 2.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.

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 Optimization Representation 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-Period Design 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 d5d5 d4d4 d3d3 d2d2 d1d1 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)

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