National Airspace (NAS) Capacity and Performance
Models for FAA Strategy Simulator
[Lab or
Research Group
Logo]
Dzung Nguyen, Ravi Sankararaman, Dr. Michael O. Ball, University of Maryland, College Park
Objective
Rho (ρ): Measure of airport utilization
Create high level model that relates airspace characteristics to
overall airspace performance for use in FAA Strategy Simulator.
An airport operation is either a flight departure or flight arrival
Average Delay per flight
What is Strategy Simulator?
t1
O
t2
Strategy Simulator is a high level decision support tool for the
Federal Aviation Administration to analyze the impact of new
technologies for the entire NAS and devise new operational
concepts and procedures.
Some examples of problems addressed
•“Right” combination of demand management and infrastructure
investments
•New FAA cost-recovery models
•Introduction of major new ATC technology
NAS- wide cumulative ρ distribution
Where does it fit?
A day-to-day ρ distribution is created across 32 NAS airports.
Ex. Increasing the number of
service flights to meet high
demands (eg. Christmas)
Medium - Level
Strategy Simulator Model Overview
Reference
Demand
Fleets & Schedule
trips offered
travel costs
travel times
Trip
Attractiveness
Flight
Delays
services
offered
money paid
Fleet
Finances
trips taken
Fleets
Cargo demand
Airport
Capacity
Enroute
Capacity
ATC
Infra
structure
Airline
Ecology
ATC
Controllers
taxes
services &
capacity used
ρ99
Rho95
Conclusion
NAS Performance Model
ATC & Airports
capacity
offered
Scheduled
flights
Market
Clearing
Effect on GDP
Passenger
Delays
median, 95th and 99th
percentile of operations.
•ρ95 exhibited better
prediction power than the
other utilization measures.
ρ95
FAA
Budgets
Aviation
Trust
Fund
* UMD contributions for the model have been circled in red. R&D team consists of UMD, Ventana
Systems, LMI, UC Berkeley, VPISU, MIT
VMC capacity
for airport
Scheduled
demand for
airport
IMC capacity
for airport
ρ95 for airport
% time with
IMC for airport
airport ρ95 for
NAS
% demand
covered by
airports
airline robustness
factor
Airspace ρ for
NAS
Rho95
Passenger delay measure is derived using the Average delay per
flight and Probability of Cancellation metric.
• ρ50, ρ95 and ρ99 denote the
ρ50
ρ
Ex. GDP planning or
sequencing of incoming flights
Low - Level
Passenger &
Cargo
Rho95
Average Passenger Delay
Ex. Building a runway to
increase capacity
High -Level
# Operations scheduled during [t1,t2]
Capacity (in # operations) during [t1,t2]
% of operations with
ρ ≤X
Strategy
Simulator
time
ρ ([t1,t2]) =
Probability of Cancellations
Performance Metrics (NAS-wide daily model)
Non-scheduled
demand for airport
(GA Traffic)
These functions map NAS capacity and demand into average flight
delays and cancellation probabilities as well as passenger delay
estimates
Ongoing Research Areas
Average flight
delay
Flight
cancellation
probability
airline creep
factor
Passenger
performance
metric
•Extending the model to weekly, monthly and yearly model
•Refine passenger model based on real data
•Contribution of Passenger Load factor on flight delay
•Incorporation of unscheduled flights
•Consider schedule robustness effects