Communications Network Modelfor Air Traffic Management

David LuongUniversity of California, Los Angeles

Department of Mechanical and Aerospace EngineeringUCSC UARC STI

Moffett Field, CA 94035

Mentor: Gano ChatterjiUniversity of California, Santa Cruz

NASA Ames Research CenterMoffett Field, CA 94035

September 2008

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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

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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.

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Outline

• Objective

• Communications Network Model

• Results• Validation• Three Control Methods

• Conclusions

• Future Work

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Objective

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

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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.

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Model of Air Traffic Network

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Aggregate Model

• Output of a center i depends on its state xi.

• Input is number of departures di.

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Data Structure for Communications Network

Aircraft Object

Aircraft

ID Origin DestinationRouteDeparture

Time

Center

List

Transit

Time

List

ZLA ZAB ZKC ZAU ZOB ZNY

T1 T2 T3 T4 T5 T6

LAX JFKUAL02 8AM PST

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Multi-Input Multi-Output Model

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ZDC

ZMA

ZTL

ZJX Center

Input Stack

Transit time met?

Yes

No

Output Queues

Departure Queue

Flow Valves

Landing Stack

Input from Neighbors Output to Neighbors

ZDC

ZMA

ZTL

Key difference w.r.t aggregate model- notions of transit time, queuing delay, and constraint from neighbor center.

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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.

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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.

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Center Count Constraints

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Control StrategiesUnconstrained

• 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

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ZAB Center Count Constraint

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ZDV Center Count Constraint

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ZLA Center Count due to ZAB & ZDV Constraints

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ZLA Center Delay

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Center-wide Delay

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Conclusions

• Model validates, but can be improved.

• Greedy strategy beneficial for the center itself.

• Fairness strategy better for system-wide delays.

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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.

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Acknowledgements

• Gano Chatterji

• Yun Zheng

• Folks in 142 Simulation Lab

• UARC Systems Teaching Institute for supporting my internship.

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Questions?

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