faa air traffic organization (ato) weather and operational ... · faa air traffic organization...
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FAA Air Traffic Organization (ATO)Weather and Operational Performance
Presented to:FPAW 2016 Summer Meeting
Presented by:John Gulding
Date:2 August 2016
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Management Objectives
• Can FAA identify/prioritize the constraints in the system?– Air Traffic Flow Management Delay, Taxi-Out Delay
• Is FAA making the most efficient use of capacity? – Capacity, Throughput, Capacity Efficiency
• Is FAA providing efficient flight trajectories to operators?– En-Route Additional Distance, Level Flight
• How will FAA respond to questions of Airline schedule delay, on-time performance?– On Time Performance, Change in Block Time
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• Low Visibility
• High wind• Convective weather
• Schedule Peaks
• Crew Scheduling
• Equip problems, etc.
• Airport Maintenance
• Airport Expansion
• ANSP Investment
• Traffic Management Initiatives
• Airspace Design
ATC/ATM performance
Capacity & Airport
Infrastructure
Weather
Operator Schedules &
Flight Preferences
New Technology
Schedule Delay
Other Drivers (SAA)
Metric Inter-Dependencies
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Data Sources
• Archived Trajectory and Flight Plan Data
• Aviation System Performance Metrics (ASPM)– Key Event Times: Scheduled Filed Actual, – Basic METAR
• Air Traffic Flow Management Delay (OPSNET)
• National Traffic Management Log (NTML)
• Weather Sources– METAR– NCAR Wind Data at 6-hour intervals
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ATFM Delay by Category - FY2016
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ATFM Delay by Region - FY2016
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ATFM Delay by Facility- FY2016
Largest Increases in DelayEWR-Wind & VisibilityZMA-VolumeMSP-Visibility & WindDCA-Visibility
Largest Decreases in DelayORD-EquipmentPHL-Visibility & Snow/IceJFK-Runway/TaxiBOS-Wind & Visibility
Other Important ChangesSFO-Increase in Wind delay-Decrease in Visibility delay
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Total TMI Wind Delays FY2016
-
100,000
200,000
300,000
400,000
500,000
600,000SF
O
LGA
EWR
OR
D
JFK
LAS
MSP
DEN
BO
S
PH
L
IAH
MEM CLT
IAD
DC
A
LAX
SEA
DFW
DTW PH
X
SLC
De
lay
in M
inu
tes
Airport
TOTAL Delay (Core 30) FY2016
The top 5 airports highlighted in red constitute 89% of total TMI wind delays.
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Most wind delays occurred in October, December, March, and April
-
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
01
-OC
T-1
5
02
-OC
T-1
5
04
-OC
T-1
5
18
-OC
T-1
5
23
-OC
T-1
5
28
-OC
T-1
5
10
-NO
V-1
5
12
-NO
V-1
5
13
-NO
V-1
5
14
-NO
V-1
5
18
-DEC
-15
22
-DEC
-15
27
-DEC
-15
29
-DEC
-15
12
-JA
N-1
6
11
-FEB
-16
13
-FEB
-16
16
-FEB
-16
14
-MA
R-1
6
17
-MA
R-1
6
18
-MA
R-1
6
24
-MA
R-1
6
28
-MA
R-1
6
29
-MA
R-1
6
31
-MA
R-1
6
01
-AP
R-1
6
03
-AP
R-1
6
07
-AP
R-1
6
08
-MA
Y-1
6
16
-MA
Y-1
6
De
lay
in M
inu
tes
Date
TMI wind delays (JFK) FY 2016
TMI Wind Delays - JFK
9
Wind42%
Low Ceilings
34%
Snow/Ice7%
Low Visibility
6%
Volume4%
Thunderstorms4%
Multi-taxi2%
Rwy Constructi
on1%
TMI Delays by cause (JFK)
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Linking Wind Conditions to Delay
0
5
10
15
20
25
30
35
0
100
200
300
400
500
600
700
00:00 06:00 12:00 18:00 00:00 06:00
Win
d S
pe
ed
(K
no
ts)
TMI W
Ind
De
lay
(Min
ute
s)
Start Time (GMT)
TMI WIND DELAYS - JFK 28-Oct-2015
OPSNET_WIND_DELAYS METAR_WIND_SPEED
OPSNET and METAR data are showing similar patterns. However, not exactly matching. To be further examined by looking at• different days• Trajectory
characteristics, arrival fix, runway used… etc.
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Capacity EfficiencyCalculating Demand
• Demand based on Filed Times or Empirically by a Best Achieved Trajectory
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
Travel Time 40nm to Touchdown
Pe
rcen
tag
e
PHL Large Aircraft
Benchmark Time 13.6 Minutes
• Flight Demand is from Benchmark Arrival Time (un-impeded time) until Actual Arrival Time
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JFK
Arrivals into JFKApril 30, 2016 HR 12:00 -1259 April 15, 2016 HR 19:00 -1959
13L, 22L - 30 Arrivals, TAER 100 22L, 22R - 39 Arrivals, TAER 88.64
40nm
JFK
40nm
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Flight Efficiency KPI – EnRouteActual vs. Flight Plan vs. Great Circle vs Best Achieved vs. Wind Optimal
40nm
100nm
Actual
Flt Plan
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Impact of Special Activity Airspace
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March – 481 Flights8.3 nm Excess Dist.
June – 363 Flights32.6 nm Excess Dist.
Impact of Weather
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Performance Metrics ReportingIs the metric/process useful?
• Does it lead to improvements in the system?– Data mining identifies specific scenarios for
mitigation.
• Will decision makers trust what is presented?– Weather, Airline Schedules, Airport Capacity– What are similar days?
• Capabilities beyond local METAR– ASPM like tables for Terminal/EnRoute– ASPM like tables for Forecast Weather