Transcript Day - 1 Sub-Session 2

FAA Air Traffic Organization (ATO)
Weather and Operational Performance
Presented to:
FPAW 2016 Summer Meeting
Presented by:
John Gulding
Date:
2 August 2016
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
2
Metric Inter-Dependencies
• ANSP Investment
• Traffic Management
Initiatives
• Airspace Design
Schedule Delay
ATC/ATM
performance
• Airport
Maintenance
• Airport Expansion
Capacity &
Airport
Infrastructure
New Technology
Other Drivers
(SAA)
• Schedule Peaks
• Crew Scheduling
• Equip problems, etc.
Operator
Schedules &
Flight
Preferences
Weather
• Low Visibility
• High wind
• Convective weather
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
4
ATFM Delay by Category - FY2016
5
ATFM Delay by Region - FY2016
6
ATFM Delay by Facility- FY2016
Largest Increases in Delay
EWR-Wind & Visibility
ZMA-Volume
MSP-Visibility & Wind
DCA-Visibility
Largest Decreases in Delay
ORD-Equipment
PHL-Visibility & Snow/Ice
JFK-Runway/Taxi
BOS-Wind & Visibility
Other Important Changes
SFO
-Increase in Wind delay
-Decrease in Visibility delay
7
Total TMI Wind Delays FY2016
The top 5 airports highlighted in red constitute 89% of total TMI wind delays.
TOTAL Delay (Core 30) FY2016
600,000
400,000
300,000
200,000
100,000
Airport
8
SLC
PHX
DTW
DFW
SEA
LAX
DCA
IAD
CLT
MEM
IAH
PHL
BOS
DEN
MSP
LAS
JFK
ORD
EWR
LGA
-
SFO
Delay in Minutes
500,000
TMI Wind Delays - JFK
Most wind delays occurred in October, December, March, and April
TMI Delays by cause (JFK)
TMI wind delays (JFK) FY 2016
Thunderst
orms
Volume 4%
18,000
16,000
Low 4%
Visibility
6%
12,000
10,000
Wind
42%
Snow/Ice
7%
8,000
6,000
4,000
2,000
01-OCT-15
02-OCT-15
04-OCT-15
18-OCT-15
23-OCT-15
28-OCT-15
10-NOV-15
12-NOV-15
13-NOV-15
14-NOV-15
18-DEC-15
22-DEC-15
27-DEC-15
29-DEC-15
12-JAN-16
11-FEB-16
13-FEB-16
16-FEB-16
14-MAR-16
17-MAR-16
18-MAR-16
24-MAR-16
28-MAR-16
29-MAR-16
31-MAR-16
01-APR-16
03-APR-16
07-APR-16
08-MAY-16
16-MAY-16
Delay in Minutes
14,000
Multi-taxi
Rwy
2%
Constructi
on
1%
Date
9
9
Low
Ceilings
34%
Linking Wind Conditions to Delay
700
35
600
30
500
25
400
20
300
15
200
10
100
5
0
00:00
06:00
12:00
18:00
00:00
Start Time (GMT)
OPSNET_WIND_DELAYS
10
METAR_WIND_SPEED
0
06:00
Wind Speed (Knots)
TMI WIND DELAYS - JFK 28-Oct-2015
TMI WInd Delay (Minutes)
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.
Capacity Efficiency
Calculating Demand
• Demand based on Filed Times or Empirically by a Best
Achieved Trajectory
8.0%
7.0%
Benchmark Time 13.6 Minutes
6.0%
Percentage
5.0%
4.0%
3.0%
2.0%
1.0%
37
36
35
34
33
32
31
30
29
28
27
26
25
24
23
22
21
20
19
18
17
16
15
14
13
12
11
9
10
8
0.0%
Travel Time 40nm to Touchdown
PHL Large Aircraft
• Flight Demand is from Benchmark Arrival Time (un-impeded
time) until Actual Arrival Time
Arrivals into JFK
April 15, 2016 HR 19:00 -1959
April 30, 2016 HR 12:00 -1259
40nm
40nm
JFK
JFK
22L, 22R - 39 Arrivals, TAER 88.64
13L, 22L - 30 Arrivals, TAER 100
12
Flight Efficiency KPI – EnRoute
Actual vs. Flight Plan vs. Great Circle
vs Best Achieved vs. Wind Optimal
40nm
Actual
Flt Plan
100nm
Impact of Special Activity Airspace
14
Impact of Weather
March – 481 Flights
8.3 nm Excess Dist.
June – 363 Flights
32.6 nm Excess Dist.
15
Performance Metrics Reporting
Is 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
16