Factors Influencing
Estimated Time En Route
Thomas R. Willemain, Harry Ma
Natasha Yakovchuk, and Wesley Child
Dept. of Decision Sciences & Engineering Systems
Rensselaer Polytechnic Institute Troy, NY 12180
NEXTOR Conference on Air Traffic Management and Control
June 2003
Work supported by FAA contract #DTFA01-98-00072
Willemain et al. NEXTOR 6/03
1
Outline
1.
2.
3.
Why interest in ETE?
Trends in ETE
ETE distributions
–
–
–
Means, std deviations and relative variability
Shapes of distributions
Modeling ETE distributions w/lognormal mixtures
•
4.
Standardized ETE
–
–
5.
6.
A new way to characterize carrier behavior
ANOVA for Factors affecting ETE
Boxplots by equipment and carrier
Variation in filed and flown routes
Are carriers padding their ETEs?
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1. Why Interest in ETE?
• ETE is basis for analysis of deviations from flight
plans (see Yakovchuk & Willemain, next talk at
this conference), so should be understood.
• Average ETE should remain relatively constant
but can actually vary a lot: Why?
– MEM-CVG: 76 ð 58 minutes during winters ‘98-’01
– BWI-LGA: 36 ð 52 minutes
“
“
“
• ETE distributions may tell us about differences in
carriers’ behavior.
Willemain et al. NEXTOR 6/03
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2. Trends in ETE
• ASPM data on ETEs
• Studied winters of ’98-’99, ’99-’00, ’00-’01,
’01-’02 to minimize effect of convective
weather
• Some OD pairs had large and consistent
changes
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OD Pairs with Strong Trends
Origin
MEM
MEM
IAD
CVG
CLE
CLT
CVG
JFK
MEM
PHL
DCA
BWI
Destination
CVG
IAH
CLE
CLT
IAD
CVG
MEM
BOS
STL
DCA
EWR
JFK
'98-'99
76
98
72
73
62
80
80
43
57
33
43
48
PHL
PHL
DFW
CLE
PIT
BWI
IAD
EWR
IAH
PIT
CLE
LGA
41
23
41
31
34
36
Average ETE
'99-'00
'00-'01
73
66
85
78
68
60
65
63
58
51
76
73
79
76
41
39
53
51
32
30
42
40
48
44
43
24
42
31
34
42
43
25
42
34
40
51
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'01-02
58
77
57
58
50
66
68
37
50
29
37
43
Avg Annual
% Change
-8%
-8%
-7%
-7%
-7%
-6%
-5%
-5%
-4%
-4%
-4%
-4%
44
26
46
39
45
52
3%
4%
4%
9%
11%
13%
5
OD Pairs with Strong Trends
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3. ETE Distributions
• ASPM data
• Approximately 60,000 individual flights
• Variables
– ETE, in minutes
– Route: 28 routes (14 OD pairs) with many flights by at
least 2 major carriers between major airports
– Carrier: 6 majors (coded “AAA” to “FFF”)
– Month: Jan-May 2002
– Hour of day: departure times from 0600 to 2200 local
– Equipment: 10 types of aircraft (e.g., 73x)
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Routes Chosen for Study
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Counts of Flights
OD pair
ATL
ORD
EWR
LAX
EWR
ORD
SFO
LAX
ORD
PHL
DTW
CLT
MIA
ATL
IAH
ATL
DTW
EWR
DTW
ATL
ATL
CLT
DTW
DEN
SFO
PHL
IAH
CLT
AAA
1666
897
2578
2253
2520
BBB
Carrier
CCC DDD
3129
1566
1876
242
EEE
1995
896
3240
6268
3096
1121
1398
1964
1030
FFF
1638
1191
2557
1767
2060
2030
2122
2241
1733
936
257
Willemain et al. NEXTOR 6/03
606
599
949
1032
Range/Average
65%
60%
53%
206%
60%
6%
59%
11%
69%
8%
16%
43%
45%
120%
9
Average ETE (minutes)
OD pair
ATL
ORD
EWR LAX
EWR ORD
SFO
LAX
ORD
PHL
DTW
CLT
MIA
ATL
IAH
ATL
DTW EWR
DTW
ATL
ATL
CLT
DTW DEN
SFO
PHL
IAH
CLT
AAA
93
302
106
58
102
BBB
Carrier
CCC DDD
87
300
104
53
EEE
88
297
102
55
98
77
87
96
75
FFF
99
75
83
94
85
37
75
89
39
146
127
148
310
306
123
Range/Average
7%
2%
4%
10%
4%
3%
4%
2%
1%
4%
5%
1%
1%
3%
Smallest every time
Largest every time
Willemain et al. NEXTOR 6/03
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Standard Deviation of ETE
OD pair
ATL
ORD
EWR LAX
EWR ORD
SFO
LAX
ORD
PHL
DTW
CLT
MIA
ATL
IAH
ATL
DTW EWR
DTW
ATL
ATL
CLT
DTW DEN
SFO
PHL
IAH
CLT
AAA
8
33
16
4
14
BBB
Carrier
CCC DDD
7
37
18
2
EEE
9
33
16
5
14
4
6
10
12
FFF
15
5
4
9
5
4
12
7
6
18
16
15
36
35
16
Range/Average
27%
13%
9%
70%
10%
17%
39%
9%
1%
22%
48%
20%
2%
4%
Smallest every time
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Relative Variability (CV) of ETE
OD pair
ATL
ORD
EWR
LAX
EWR
ORD
SFO
LAX
ORD
PHL
DTW
CLT
MIA
ATL
IAH
ATL
DTW
EWR
DTW
ATL
ATL
CLT
DTW
DEN
SFO
PHL
IAH
CLT
AAA
9%
11%
15%
7%
14%
BBB
Carrier
CCC DDD
8%
12%
17%
4%
EEE
10%
11%
16%
8%
14%
6%
7%
10%
16%
FFF
15%
7%
5%
9%
6%
10%
16%
8%
15%
12%
12%
10%
12%
11%
13%
Range
2%
2%
2%
4%
2%
1%
2%
1%
0%
1%
5%
2%
0%
1%
Smallest every time
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ETE for LAX/SFO
Clear differences
across carriers
LAX-SFO:AAA
SFO-LAX:AAA
0.3
0.2
0.3
0.1
0.2
0.0
0.1
0.0
40
50
60
70
80
90
40
50
60
70
80
90
80
90
80
90
ETE
ETE
LAX-SFO:CCC
SFO-LAX:CCC
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.0
40
50
60
70
80
90
40
50
60
70
ETE
ETE
LAX-SFO:EEE
SFO-LAX:EEE
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.0
40
50
60
70
80
90
40
50
ETE
Long tail
60
70
ETE
Bimodal
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ETE for EWR/LAX
EWR-LAX:AAA
LAX-EWR:AAA
0.04
0.04
0.0
0.0
250
300
350
250
400
300
350
400
ETE
ETE
LAX-EWR:BBB
EWR-LAX:BBB
0.04
0.04
0.0
0.0
250
300
350
250
400
300
350
400
ETE
ETE
LAX-EWR:EEE
EWR-LAX:EEE
0.04
0.04
0.0
0.0
250
300
350
400
250
300
350
400
ETE
ETE
Effect of wind clearly visible when compare eastbound vs westbound ETEs
Willemain et al. NEXTOR 6/03
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ETE for ATL/CLT
ATL-CLT:CCC
CLT-ATL:CCC
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.0
30
40
50
60
30
70
40
50
60
70
60
70
ETE
ETE
CLT-ATL:FFF
ATL-CLT:FFF
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.0
30
30
40
50
60
40
70
50
ETE
ETE
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Fit CLT-ATL with
Mixture of Lognormals
0.8
0.6
Prob
0.0
0.0
0.2
0.2
0.4
0.4
Prob
0.6
0.8
1.0
CDF: circle-raw | triangle-fitted
1.0
CDF: circle-raw | triangle-fitted
40
50
60
70
35
40
CLT-ATL:FFF ETE
45
50
55
CLT-ATL:CCC ETE
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Parameter Estimates
Parameters for "Regular" Ops
Proportion
Mean
Std Dev
Parameters for "Irregular" Ops
Proportion
Mean
Std Dev
Route
Carrier
ATL to CLT
FFF
CCC
98%
98%
35
35
2
1
2%
2%
49
46
5
5
CLT to ATL
FFF
CCC
88%
85%
42
38
3
1
12%
15%
54
46
8
5
SFO to LAX
EEE
AAA
98%
52%
52
59
2
2
2%
48%
65
58
3
4
LAX to SFO
EEE
AAA
58%
79%
58
58
4
5
42%
21%
55
58
2
2
Use these 5 parameters to characterize carrier flight planning behavior
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4. Standardized ETE
• Standardized ETE for a route on a given day
= ETE / {Avg of all ETEs for that route}
• Average Standardized ETE = 1.0
• Allows combining of data from all 28 routes
• Focuses attention on variation around the
average
• Distribution is rather symmetric in middle but has
a small % (but large #) of extreme outliers
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ANOVA for Standardized ETE
Coeff
of Var
R-Square
Most of variance
unexplained: wind?
0.25
Source
Only 2 factors had
much influence
6.68
DF
Eqpt
Airline
Citypair
Hour
Eqpt*Airline
Month
Eqpt*Citypair
Day
Eqpt*Hour
Eastward(Citypair)
Airline*Hour
Airline*Month
Month*Day
Hour*Citypair
Eqpt*Month
Airline*Citypair
Month*Hour
Month*Citypair
Eqpt*Day
Day*Citypair
Airline*Day
Day*Hour
9
5
13
16
21
4
58
6
129
14
78
20
24
173
35
16
64
52
53
78
30
96
Root
MSE
0.067
Type I
SS
35.11
10.32
3.81
3.39
3.71
0.64
5.22
0.43
6.02
0.59
3.27
0.79
0.85
5.82
1.15
0.40
1.47
0.79
0.70
0.85
0.24
0.66
S_ETE
Mean
1.00
Mean
Square
3.90
2.06
0.29
0.21
0.18
0.16
0.09
0.07
0.05
0.04
0.04
0.04
0.04
0.03
0.03
0.02
0.02
0.02
0.01
0.01
0.01
0.01
F
Value
Pr > F
873.40
462.11
65.57
47.50
39.54
35.79
20.17
15.97
10.45
9.47
9.39
8.86
7.94
7.53
7.34
5.57
5.13
3.40
2.96
2.45
1.78
1.53
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
0.0053
0.0006
Willemain et al. NEXTOR 6/03
(a)
19
1.6
1.4
1.2
1.0
0.8
S.ETE
1.8
2.0
2.2
Std ETE by Equipment
A3XX
B72X
B73X
B74X
B75X
B76X
DC9X
FXXX
MD8X
N.R
EQPT
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Std ETE by Carrier
2.2
2.0
1.8
1.6
S.ETE
1.4
1.2
1.0
0.8
AAA
BBB
CCC
DDD
EEE
FFF
Airline
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Confirms earlier
comparisons using
mean and std dev
Std ETE by Carrier
AAA
BBB
Airline
CCC
Mean
1.030
1.003
0.980
1.010
0.987
1.004
SE of Mean
0.001
0.001
0.001
0.001
0.001
0.001
75%Quantile
1.078
1.035
1.009
1.041
1.020
1.035
50%Quantile
1.023
0.996
0.975
1.000
0.975
0.991
25%Quantile
0.978
0.956
0.942
0.964
0.939
0.954
IQR
0.100
0.080
0.068
0.077
0.081
0.081
n
11,313
6,693
11,785 6,432
DDD
EEE
FFF
Willemain et al. NEXTOR 6/03
16,700 6,543
22
5. Spatial Variation
• Data from POET for 10 Sep - 7 Nov 2002;
average of 400 flights per route.
• Routes filed can differ greatly by carrier.
• Routes flown can differ greatly from routes
filed.
• ETE variability cannot be traced primarily
to differences in routes.
– 3% relative variability in length of filed routes
– 11% relative variability in ETE
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CLT-IAH Routes: Planned & Flown
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6. Are Carriers Padding ETE?
• “Padding” = adding extra time so that on-time
performance will look better
• If padding, then deviation = actual - ETE will be
negative (though “better” to pad G2G than ETE)
• Results
– Often, longest ETEs do tend to be overestimates, so
data give the appearance of padding
– Interpretation: padding vs heroic recovery?
– And all carriers seem to have the same patterns
Willemain et al. NEXTOR 6/03
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od.pair: ORD.EWR
od.pair: ORD.PHL
60
20
-20
late
-40
72
94
116
71
od.pair: MIA.ATL
92
113
od.pair: ORD.ATL
90
50
-10
0
75
90
105
80
110
140
ete
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od.pair: EWR.LAX
od.pair: EWR.ORD
60
20
-20
late
-40
260
320
380
100
od.pair: DTW.EWR
120
140
od.pair: EWR.DTW
60
20
-20
-40
60
80
100
72
94
116
ete
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od.pair: CLT.DTW
od.pair: CLT.IAH
50
20
0
late
-40
70
90
110
110
od.pair: ATL.ORD
30
-20
-10
110
170
od.pair: CLT.ATL
60
80
140
140
35
50
65
ete
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Summary
• ETE is a dynamic, stochastic quantity.
– ETE can have sustained trends.
– ETE is influenced by several factors.
– Most influence is “random”: errors in wind forecasts?
• Carriers differ in their ETE planning.
– Some are consistently different.
– Part of the difference is from route planning.
– Evidence of “padding-like” behavior for “irregular” ops
• Can model ETE distributions.
– Fit mixtures of lognormals.
– Use estimated parameters as new way to
characterize carriers’ behavior.
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Acknowledgements
• FAA/Free Flight
– Dave Knorr
– Ed Meyer
• CNA Corp
– Joe Post
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