Communications Network Model for Air Traffic Management David Luong University of California, Los Angeles Department of Mechanical and Aerospace Engineering UCSC UARC STI Moffett Field, CA Mentor: Gano Chatterji University of California, Santa Cruz NASA Ames Research Center Moffett Field, CA September 2008

2 My Background UCLA ‘12 –Master’s Student in Mechanical Engineering Systems and Controls Swarthmore ‘06 –B.S. Electrical Engineering –B.A. Economics Personal Interests: Juggling and Skiing

3 Motivation Aggregate models of air traffic relate center counts to net flow of aircrafts into centers as a function of time. They are based on fitting a linear systems model to center and landing count time histories with departure count time history as the independent variable. Not suitable for predicting traffic counts with airport arrival/departure and center count constraints.

4 Outline Objective Communications Network Model Results Validation Three Control Methods Conclusions Future Work

5 Objective Develop a model that is suitable for predicting center counts with airport arrival/departure constraints and center count constraints.

6 Approach Build a communications system model in MATLAB. Validate center count results obtained by model against ACES center counts. Show that the model is useful for predicting center counts with center capacity constraints.

7 Model of Air Traffic Network

8 Aggregate Model Output of a center i depends on its state x i. Input is number of departures d i.

9 Data Structure for Communications Network Aircraft Object Aircraft IDOriginDestinationRoute Departure Time Center List Transit Time List ZLAZABZKCZAUZOBZNY T1T2T3T4T5T6 LAXJFK UAL028AM PST

10 Multi-Input Multi-Output Model ZDC ZMA ZTL ZJX Center Input Stack Transit time met? Yes No Output Queues Departure Queue Flow Valves Landing Stack Input from NeighborsOutput to Neighbors ZDC ZMA ZTL Key difference w.r.t aggregate model- notions of transit time, queuing delay, and constraint from neighbor center.

11 Results: Overview Validate model with ACES data. Show effects of center-wide counts and delays with center capacity constraints. Compare control strategies by analyzing localized and system-wide delays.

12 Model Validation ACES counts based on detailed trajectories Climb, cruise, descent simulated. Model counts based on simple transit time calculations Flight plan distance and cruise speed used.

13 Center Count Constraints

14 Control Strategies  Unconstrained Centers accept all outputs from neighbors  Fairness Centers accept aircrafts from neighbors using round- robin  Self-interest Each center departs its own aircrafts before accepting from neighbors  Min-max Minimize the maximum of input queue delays

15 ZAB Center Count Constraint

16 ZDV Center Count Constraint

17 ZLA Center Count due to ZAB & ZDV Constraints

18 ZLA Center Delay

19 Center-wide Delay

20 Conclusions Model validates, but can be improved. Greedy strategy beneficial for the center itself. Fairness strategy better for system-wide delays.

21 Future Work Use ACES flight trajectories for transit time calculations. Develop sector-level model. Compare sector level model results against ACES results when sector capacities are reduced.

22 Acknowledgements Gano Chatterji Yun Zheng Folks in 142 Simulation Lab UARC Systems Teaching Institute for supporting my internship.

23 Questions?