Temporal Deviations from Flight Plans:
New Perspectives on
En Route and Terminal Airspace
Professor Tom Willemain
Dr. Natasha Yakovchuk
Department of Decision Sciences & Engineering Systems
Rensselaer Polytechnic Institute
Troy, NY
willet@rpi.edu
Acknowledgements
ƒ Seminal ideas from
ƒ Ed Meyer, FAA
ƒ Helpful comments from
ƒ Dave Knorr, FAA
ƒ James Bonn and Joe Post, CNA Corp.
ƒ Mike Ball, U Maryland
ƒ Bob Hoffman, Metron Aviation
ƒ American Airlines OR group
ƒ Financial support from
ƒ FAA Free Flight
2
How this all got started…
Q: I wonder, how long does it take to fly from A to B?
A: It varies a lot.
Q: Is this a problem?
A: Yes. Consistency is an important service metric.
Q: Why does the time vary so much?
A: Some of the variation is predictable, but a lot is surprising and troublesome.
Q: How can we quantify the surprises?
A: Look at deviations from flight plans.
Q: Do the deviations reflect en route problems?
A: Mostly. But there are also systemic effects, like correlations across different
routes, or problems with flights going to the same destination from many
origins.
Q: How can we trace the deviations to their various sources?
A: Invent a new way of analyzing ASPM data.
3
Agenda
ƒ Deviations from flight plans
ƒ Sources of deviations
ƒ Row+column analysis to estimate deviations
ƒ System implementation
ƒ Samples of system outputs
ƒ Alternative approach using new ASPM En Route data
ƒ Row+column analysis of excess en route distance
ƒ Evidence of ripple effect back from runway congestion to
excess en route distance flown
4
Variation in flight times
Average daily airtime for flights from
Atlanta to Boston for year 2001
150
airtime
140
Í Average airtime fluctuates
(due to winds aloft, weather
and congestion in airports,
etc.)
130
Í Flight plans anticipate
“normal problems”
120
110
100
date
Ð Shift attention to
Deviation = Actual Airtime –Planned Flight Time
2 types of Deviations: “ETE” and “G2G”
5
Deviations from flight plans
Deviations from flight plans for flights
from Atlanta to Boston for year 2001
deviation from flight plan
20
Í Deviations from flight plans
measure unanticipated
problems, or “surprises”
Í Common factors for different
flights are considered as
systemic sources of deviations
10
0
-10
-20
date
Ð Decompose deviations into four
sources:
System + Origin airspace + Destination airspace + En route airspace
6
Sources of deviations
• System effects
– 9/11 attacks, bias in data collection system
• Origin effects
– restricted departure gates, runway configuration
• Destination effects
– LAHSO unavailable, reduced AAR
• En route effects
– convective weather, MIT, circular holding
7
FAA data as a two-way table
Destination airport
ƒ 31 major US airports
Origin airport
ƒ Each table represents one day of
operations
ƒ One observation per cell,
averaging over all flights on route
ƒ Analyzed ASPM data for January
’01- June ’03
ƒ Presence of structural ‘holes’ and
outliers complicates estimation
problem
structural ‘holes’
8
Fragment of the table
Origin airport
Destination airport
9
Row + Column analysis
en route effects
(residuals)
destination
origin
1
2
1
y11 y12
2
y21 y22
yn1 yn2
1
m
… y1m
… y2m
…
…
n
…
…
linear additive
model
yij = µ + αi + βj + εij
1
2
2
…
ε11 ε12
ε21 ε22
n
destination
effects
m
…
…
ε1m
ε2m
…
…
ynm
origin
effects
εn1 εn2
β1
β2
α1
α2
…
…
εnm
αn
…
βm
µ
system
effect
10
Toy example of row+column analysis
Avg Deviations
ORD
Origin
ORD
CVG
CLE
DTW
PIT
12
12
13
24
10
CVG
1
-3
2
4
-2
Destination
CLE
6
5
4
7
3
DTW
3
3
0
7
0
PIT
2
-1
-1
0
-2
24 = 2 + 4 + 10 + 8
En route Effects
ORD
Origin
11
ORD
CVG
CLE
DTW
PIT
0
2
0
8
CVG
0
-1
1
0
Destination
CLE
0
0
-2
-2
DTW
0
1
0
1
PIT
1
-1
1
-1
1
0
-2
1
4
2
System implementation
We created a turnkey system implemented in SAS that produces:
ƒ Detection of outliers for next-day analysis of NAS operations
ƒ Map-based displays
ƒ Statistical graphics
ƒ Datasets for further analysis in one-off statistical studies
Examples of system outputs follow.
12
Timeplot of system effects (ETE)
Note
bias
September 12, 2001
13
March 13-14, 2002
June 27, 2002
Timeplot of system effects (G2G)
Sept 13, 2001
14
Outlier detection for next-day analysis
February 15, 2001
Interesting days in 2002
26
15
Boxplots of destination effects, 2001
PHL
SFO
LGA
16
LAS
Timeplot of destination effects at LGA
Why the sudden reduction in variability?
Why did we lose all the “good” numbers?
D e sti n a ti o n e ffe c ts fo r L G A (y e a r 2 0 0 1 )
15
10
5
0
-5
-10
-15
-20
17
Timeplots of origin and destination effects
18
Timeplots of en route effects to ORD
19
Routes with most en route variation
20
Two types of airport operation
Ð Origin versus destination
effects for LAX (G2G):
negative correlation
Ð Origin versus destination
effects for MEM (G2G):
no correlation
airport: MEM
airport: LAX
10
10
dest.effect
15
dest.effect
20
0
5
-10
0
-20
-5
-20
-10
0
orig.effect
21
10
-5
0
5
10
orig.effect
15
20
Regional patterns in G2G origin effects
las
lax
san
phx
22
Alternative approach using new ASPM data
•
•
•
ASPM now provides data based on crossing lines at {0, 40, 100,
200, mid, 200,100, 40, 0} miles along the GCR between origin
and destination.
“En route” ≡ 100 to 100 mile portion.
Comparison with analysis of deviations
– Advantage: Reduces effect of terminal airspace on en route data
(but still some spillover).
– Disadvantage: Fails to reduce effects of anticipated en route
problems (e.g., wind) on en route data.
– Same: Can also apply row+column analysis to “pure” en route data.
•
Enables new analyses, such as
– Exposing the link between terminal airspace congestion and en
route performance
– Use of opposite direction flights to compare variability from wind vs
variability from en route ATC (not shown here).
23
Row+column analysis of excess en route miles
DOT 32 Airports on 01Oct03
Average of ExcessMiles
Destination
Origin
ATL
BOS
ATL
22
BOS
4
BWI
1
1
CLT
13
CVG
4
12
DCA
2
1
DEN
8
4
DFW
9
28
DTW
9
3
EWR
3
FLL
6
16
IAD
1
2
IAH
10
40
JFK
4
LAS
8
4
LAX
7
9
LGA
3
MCO
4
10
MDW
7
3
MIA
5
22
MSP
17
13
ORD
8
2
PDX
24
PHL
2
PHX
15
9
PIT
3
4
SAN
14
6
SEA
8
14
SFO
10
12
SLC
12
10
STL
3
10
TPA
4
21
Grand Total
7
11
24
BWI
4
1
CLT
3
1
CVG
2
14
2
2
4
17
2
9
30
5
13
5
5
1
20
22
9
10
19
8
9
2
9
11
20
10
10
9
12
22
14
9
12
DCA
2
2
1
4
4
17
10
9
10
12
8
1
7
4
5
10
10
22
4
8
5
11
9
10
9
15
14
29
2
7
6
4
10
17
7
8
9
11
12
12
17
10
25
6
15
12
10
15
13
9
DEN
8
22
14
15
7
6
4
5
22
13
19
6
14
1
5
15
6
2
12
2
3
3
11
4
12
9
6
10
1
6
11
9
DFW
6
40
5
10
13
7
3
17
12
23
5
5
4
10
10
14
5
14
6
8
5
11
8
12
11
3
14
6
6
12
10
DTW
4
5
5
3
7
11
18
4
11
9
9
6
24
16
4
6
9
5
6
5
21
47
9
7
8
5
10
EWR
6
5
19
7
21
1
FLL
6
20
60
9
12
20
30
20
11
10
22
29
14
14
9
2
27
9
1
17
19
9
21
10
9
26
12
13
IAD
3
1
4
8
26
11
6
2
59
9
14
4
9
IAH
7
24
28
16
28
19
6
23
19
40
14
23
13
16
11
JFK
17
15
2
45
3
8
53
20
25
7
26
6
17
8
6
43
12
11
17
10
15
8
17
3
5
13
11
11
13
11
14
21
6
42
16
9
26
15
14
45
19
LAX
12
45
23
17
8
LGA
9
2
2
6
14
10
9
13
24
7
23
11
28
12
12
9
27
6
62
1
3
17
11
11
8
3
11
6
22
5
6
10
12
17
11
18
19
17
7
21
19
16
12
12
2
8
10
11
29
5
36
8
6
7
10
2
12
MCO
3
15
15
1
2
14
9
11
6
11
25
32
11
19
9
15
11
10
LAS
14
22
18
12
8
11
1
29
9
1
13
9
15
8
18
14
13
12
13
30
3
19
13
16
6
3
4
1
6
3
8
1
4
10
8
7
6
17
15
MDW
3
15
11
7
18
7
8
11
MIA
4
36
29
6
17
11
31
14
9
12
14
26
7
22
13
13
7
4
9
2
4
1
10
5
4
ORD
10
5
24
12
10
8
6
9
19
10
12
6
9
16
8
24
12
67
20
16
24
18
6
20
3
8
7
6
14
9
5
8
10
PDX
15
PHL
6
17
3
4
6
8
6
7
9
1
16
7
12
6
8
11
7
8
11
19
6
31
4
2
7
14
2
4
15
8
10
12
7
29
18
11
4
4
5
MSP
16
12
22
17
20
10
4
5
5
9
3
15
15
9
4
4
10
4
1
20
17
12
6
6
25
15
8
10
7
11
PHX
18
14
14
10
9
9
5
8
10
22
65
22
3
14
1
27
7
34
8
8
18
7
PIT
2
2
7
SEA
40
18
12
8
13
6
6
7
27
3
8
13
3
17
8
23
SLC
10
12
26
2
20
11
24
1
5
54
13
6
13
7
7
29
5
2
28
24
39
12
6
5
11
3
4
8
27
14
8
1
18
9
17
1
1
6
5
5
2
11
6
2
12
13
6
20
11
7
11
7
1
6
9
56
22
5
2
STL
17
32
8
9
7
19
13
13
17
1
7
24
7
17
10
1
10
18
14
SFO
15
50
34
19
12
17
8
0
2
4
SAN
29
6
8
3
9
4
2
3
8
3
5
2
9
1
1
16
8
8
6
11
13
16
9
6
5
2
13
7
4
8
11
5
9
12
8
4
3
4
14
6
5
2
11
3
8
9
3
8
TPA Grand Total
2
10
23
17
10
17
2
9
11
11
7
9
8
8
16
14
6
8
16
14
18
9
11
6
19
17
14
12
10
17
10
16
10
11
4
6
17
20
10
5
7
11
14
12
23
14
3
10
11
10
11
7
9
9
12
11
12
Some airports are consistently good or bad
Average Excess Miles for DOT32 Airports
20
Avg as Destination
IAH
15
10
5
MDW
0
0
5
10
Avg as Origin
25
15
20
Residuals show out-of-pattern routes
Identify best and worst 1% of routes for further analysis
Residuals
Origin
ATL
BOS
BWI
CLT
CVG
DCA
DEN
DFW
DTW
EWR
FLL
IAD
IAH
JFK
LAS
LAX
LGA
MCO
MDW
MIA
MSP
ORD
PDX
PHL
PHX
PIT
SAN
SEA
SFO
SLC
STL
TPA
26
Destination
ATL
BOS
12
-9
-11
-16
4
-3
1
-3
-8
5
-3
-1
14
5
-5
-7
-8
-2
-6
-9
-5
22
-6
3
-5
1
0
-3
-3
-1
6
-3
-8
5
12
3
5
-4
18
-6
5
-5
-2
-5
7
-5
2
4
3
1
9
4
-2
1
-3
9
BWI
-7
-17
CLT
-10
-12
CVG
-7
-2
-13
-7
-7
9
-12
1
12
3
1
9
-2
6
4
4
2
-3
DCA
-6
-13
-4
9
-5
1
-8
6
-6
2
3
-4
2
9
7
4
7
-7
-6
-2
6
3
-1
-2
3
-6
-4
-1
-3
-3
5
-6
-10
-8
2
0
1
-1
-1
5
20
-9
-6
0
-8
4
1
-10
-1
6
-4
4
7
5
0
15
-4
9
1
7
3
DEN
0
8
0
9
-2
0
-8
-1
11
-3
11
-11
3
-6
-3
8
-3
-1
-3
-5
-1
-5
2
-8
5
1
-1
1
-3
0
2
DFW
-3
24
-10
2
4
0
-3
10
0
6
-4
-8
-4
1
2
4
1
-2
-3
3
-4
0
-5
4
1
-6
4
1
-2
2
DTW
-5
-11
-11
-5
-1
4
6
-8
-5
-1
-9
-7
16
8
-5
-3
-7
-4
-3
-5
8
38
0
-3
0
-5
EWR
-6
-6
6
-2
5
-10
FLL
-11
-3
37
-6
-5
5
17
0
-3
-10
2
8
2
2
-4
-6
8
-3
-7
5
3
-4
8
-3
0
15
-2
IAD
-7
-16
-6
1
13
3
-12
-14
34
-11
-1
-12
-7
IAH
-10
-1
4
0
9
2
-9
7
-2
15
-5
5
4
7
0
JFK
2
0
-11
26
-10
-15
30
-2
8
-10
8
-13
1
-5
-12
23
-4
-4
-4
5
-2
6
-3
-4
2
-14
-4
-3
-4
2
-16
25
-2
-9
7
1
-2
26
-5
-11
-1
1
-7
-2
-5
11
LAX
-2
24
3
5
-6
-5
5
-1
11
-9
-2
-14
10
LGA
-6
-10
2
-6
43
-11
-5
-11
-7
-8
1
1
6
-4
0
0
0
-2
2
2
4
1
-4
7
3
2
2
-4
-1
-1
15
-4
15
-5
-4
-7
-6
-16
-1
MCO
-7
-2
-2
-7
-9
6
2
-3
-2
-2
2
9
-4
5
-3
-1
1
4
LAS
4
5
2
4
-2
-5
-9
8
-6
-10
2
-10
1
-1
8
5
4
6
2
16
-6
6
-3
4
2
-4
-6
-6
0
4
-2
-1
5
-6
-3
-7
2
MDW
-2
4
1
4
8
1
-1
MIA
-12
13
6
-9
0
-4
17
-6
-5
-8
-3
1
-14
7
-4
-3
-5
-7
5
-4
-4
-2
5
1
-1
ORD
0
-12
8
4
1
0
-8
-4
2
0
-7
-8
0
7
-1
13
-6
46
4
0
6
-3
10
-9
1
-3
-3
5
5
-3
-2
PDX
9
PHL
-4
11
-5
-7
3
-2
-4
-5
-5
-8
6
-3
1
-1
-3
0
-6
3
1
-8
12
-1
-3
-2
4
-3
2
4
2
-7
3
0
12
1
-2
-11
-2
MSP
7
-4
7
10
10
3
-2
-7
-2
-3
-13
6
-2
-4
-3
-4
2
-5
-3
5
7
PHX
5
-6
-5
-1
-5
-3
-5
-8
-1
5
45
9
-19
-3
-12
13
-1
14
-4
-1
4
-8
-2
PIT
-6
-13
-1
-5
15
4
2
1
-4
SEA
29
-1
1
-4
3
-2
-10
-3
12
-3
3
2
-5
4
0
10
SLC
2
-3
12
-10
1
-2
6
-4
-6
38
4
-3
5
-6
-1
3
-12
0
-5
-13
14
-5
-9
13
0
20
-3
1
-2
-7
-8
-4
-3
5
0
-4
-14
1
-10
2
-15
-14
0
-2
-3
-1
-3
-1
-2
2
7
-4
8
2
-2
4
-1
-6
-3
-7
-6
-4
0
43
6
-5
-10
-11
-7
5
-4
-7
STL
10
18
-5
4
-2
8
15
-4
4
-4
-8
-2
4
SFO
0
28
12
5
-3
2
-4
-6
0
0
SAN
19
1
1
-8
8
-3
-11
1
-5
-6
3
6
2
-4
TPA
-8
6
-7
-7
1
-2
0
3
-2
3
-1
-13
3
3
7
7
-2
-2
3
-5
-1
-8
-7
-6
-1
-10
-3
7
-2
-6
-4
3
-4
0
5
-1
10
-2
3
9
-6
0
-5
Runway congestion ripples back to en route
5
Destination: ATL
15
25
Destination: DFW
Excess Enroute Miles
20
5
Destination: ORD
20
Caveat: En route data
excludes flights of
less than 300 miles, so
are undercounted.
5
5
15
25
Number of Arrivals in Previous Quarter Hour
27
Summary
• Have data sources and analysis tools to monitor en route
problems using multiple metrics
– Use existing ASPM datasets
– Both air time vs ETE and block time vs G2G
– Excess en route distance and time
– Row+column analysis applies to both data sources
• Have multiple uses
– Next-day analysis of troubles in NAS for ATCSCC
– Analysis of longer-term issues at airports or routes
– One-off scientific studies
28
More importantly…
29