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Impact of FFP1 on NAS
Performance
Mark Hansen, Geoff Gosling,
Chee Chou Ong, Tatjana Bolic,
Wenbin Wei
November, 2000
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NEXTOR FFP1 Evaluation
Work Overview
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Part of much broader effort
Database Development (TASC)
Simulation (Seagull)
Normalization (UCB)
Safety Impacts (UCB)
Valuation (UCB)
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Normalization
q Translate before/after performance
comparisons to with/without
comparisons
q Focus on FFP1 terminal sites where
CTAS and/or SMA will be (has been)
deployed
q NEXTOR focus to date on ATL, LAX,
and DFW
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Normalization Approach
q Macroscopic
qAnalysis at daily lev el
qIncorporate all flight phases
q Exploratory
q Statistical
q Transferable
qUse widely available data sources
qApplicable to any terminal area
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Conceptual Framework
Demand
System Performance
¥En Route
Weather
¥Terminal Area
¥ATM
Conditions
at other
Airports
¥Aircraft
Time at
Origin
Airborne
Time
Total
Flight
Time
Taxi-In
Time
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Daily Flight Time Index (DFTI)
q Daily weighted average of flight times to a
given airport from a set of origins
q Flight time=
q Actual Arrival Time - Scheduled Departure Time
q Scheduled Flight Time+Departure Delay+Flight Time
Delay
q Time-at-Origin+Airborne Time+Taxi-In Time
q Origins have at least one completed flight in
each day of sample
q Weights reflect origin share of flights to
study airport over study period
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DFTI Time Series for LAX
220
DFTI (minutes)
200
180
160
140
120
100
J-95
J-96
J-97
J-98
J-99
J-00
J-01
Date
q Generally 140-160 minutes
q Spikes to over 180 minutes
q Seasonal Pattern
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30-Day Moving Average
220
DFTI(minutes)
200
180
Winter
Spring
160
Summer
Fall
140
120
100
Jan-95
Jan-96
Jan-97
Jan-98
Jan-99
Jan-00
Jan-01
Date
q Later fall and winter generally worst
q Summer 1999 worse than previous ones
q Delayed onset of typical winter pattern in 2000
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7-Day Moving Average with Components
200
Average Time per Flight (minutes)
180
160
140
120
Taxi-In
Airborne
100
At Origin
80
60
40
20
0
Jan-95
Jul-95
Jan-96
Jul-96
Jan-97
Jul-97
Jan-98
Jul-98
Jan-99
Jul-99
Jan-00
Date
q Time-at-origin is major source of variation
q Correlation betweem time-at-origin and airborne time
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Weather Normalization
q Based on CODAS hourly weather
observations for LAX
q Factor analysis of weather data
qCreate small number of factors that
capture variation in large number of
variables
qFactors are linear combinations of original
variables
qFactors correspond to principal ax es of Ndimensional data elipse
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X2
Factor Analysis with Two
Variables
First Factor
X1
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9-Factor Representation of
LAX Daily Weather
Factor
Interpretation
1 Warm temperatures throughout day.
2 VFR operations and absence of low cloud ceiling in the
morning.
3 VFR operations and absence of low cloud ceiling in the
afternoon.
4 High visibility throughout day.
5 Medium cloud ceiling throughout day.
6 High winds throughout day.
7 High ceiling cloud ceiling throughout day; evening
precipitation.
8 Precipitation in late morning and afternoon.
9 Precipitation in early morning.
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Demand Normalization
q Based on CODAS OAG data
q Capture number, strength, and duration of
demand surges above a given baseline rate
q Set of 12 metrics using baseline rates from
10 to 120 arrivals per hour
q Summarized by two demand factors: one for
higher baseline rates and one for higher
rates
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Flight Schedule Comparison
1200
Cumulative Number
1000
800
6/24/97
1/17/99
600
6/29/99
400
200
0
0:00
3:00
6:00
9:00
12:00
15:00
18:00
21:00
0:00
Time of Day
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Normalization for Conditions
at other Airports
q Consider airports included in DFTI
average
q For each compute daily average
departure delay for flights not bound to
LAX region
q Average airport departure delays using
DFTI weights
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Origin Airport Delay Time Series
50
45
40
Origin Airport Delay (min)
35
30
25
20
15
10
5
0
Jan-95
Jan-96
Jan-97
Jan-98
Jan-99
Jan-00
Jan-01
Date
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Performance Models
Yt = f (WX t , DMDt , ODELt ) + ε t
Where:
Yt is DFTI or DFTI component for day t;
WXt is vector of weather factors for day t;
DMDt is vector of demand factors for day t;
ODELt is average origin departure delay for
day t;
εt is stochastic error term.
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Functional Forms Considered
q Parametric
qLinear (with 3, 6, 9, and 12 weather
factors)
qQuadratic response surface
qNon-linear
q Non-parametric
q9 clusters based on 3 weather factors
q12 clusters based on 9 weather factors
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Linear Model Estimation Results
Variable
INTERCEPT
ODEL
WX1
WX2
WX3
WX4
WX5
WX6
WX7
WX8
WX9
DMD1
DMD2
2
ADJUSTED R
Description
Intercept
Origin airport departure delay
Warm daily temperatures
VFR ops, no low cloud ceiling in the morning
VFR ops, no low cloud ceiling in the afternoon
High visibility throughout day
Medium cloud ceiling throughout day
High winds throughout the day
High cloud ceiling throughout day
Precipitation in late morning and afternoon
Precipitation in early morning
Peak demand
Base demand
Estimate T - statistic P - value
138.055
567.065
0.0001
1.128
44.351
0.0001
-1.357
-12.101
0.0001
-0.988
-7.116
0.0001
-1.123
-7.583
0.0001
-0.449
-3.575
0.0004
1.440
10.555
0.0001
0.512
4.531
0.0001
0.911
4.172
0.0001
1.871
8.324
0.0001
-0.379
-2.614
0.0091
0.075
0.725
0.4685
0.440
4.574
0.0001
0.743
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Predicted Values (min)
Predicted vs Actual Values
210.00
200.00
190.00
180.00
170.00
160.00
150.00
140.00
130.00
130.00 140.00 150.00 160.00 170.00 180.00 190.00 200.00
Observed Values (min)
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Outliers
q Used TMU logs to investigate days for
which predictions have large errors
q Reasons for higher than predicted DFTI
qEast flow
qRadar outages
qAir Force One
qOver-stringent ground delay program
q No clear explanations for lower-thanaverage DFTI: No news is good news
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FFP1 Safety Impact
q Need to Address Safety Impact of FFP1
Tools
q Operational Error Rate Selected as Safety
Metric
q Significant Variation in Operational Error
Rate
q Over time at a given facility
q Across facilities
q Need to Better Understand Causes of
Variation
q Account for factors other than FFP1 tools
q Measure contribution of FFP1 tools
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NEXTOR Research on Safety Impact
q Identify Potential Safety-Related
Effects of FFP1 Tools
qBetter information for cont rollers
qChanges in controller activities
qChanges in traffic patt erns
qIncrease in traffic handled
q Review Previous Research on Factors
Associated with High Rates of
Operational Errors
q Statistical Analysis of Operational
Error Data
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Valuation Research
q Translate operational impact to
economic value
q Avoid traditional delay-centric approach
q NEXTOR work on two areas
qBuffered vs non-buffered delay
qInfering value from increases in observed
throughput rates
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