Transcript Donohue-Wye-Woods
Air Transportation System Limitations, Constraints and Trends
George L. Donohue March 19-20, Wye Woods Conference Center © George Donohue 2002
George Mason University Transportation Lab
Demand has grown Faster than National Infrastructure
2 14.0
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1960
Relative Growth in Transportation Modes Source: DOT Statistics
1965 1990 1970 1975
Year
1980 George Mason University 1985 1994
Air Carrier Bus Truck Car
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Initial Observations and an Hypothesis
FACTS:
Airspace above Airport Runway Thresholds (Operational Capacity) is a Limited, Nationally Allocateable Commodity
National Airport and Airspace Management Infrastructure growth has seriously lagged behind Growth in Air Transportation Demand
Utilization of this Capacity Commodity is Constrained by Airline Schedule Conflicts, Delay Tolerance, FAA Ground Delay Programs and Aircraft Safety (i.e. Aircraft Spacing)
HYPOTHESIS:
A DoT Supervised Auction System may be Required to Efficiently allocate Airport Capacity within Delay and Safety constraints
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Incentives for Operational Improvements and Modernization
Key Decision Points
DP 1 NATCA Contract Negotiations and Controller Mass Retirement Threat (Avg. Age=50 + Service=25) ~2007
DP 2 DP 3 Termination of Slot Controls - 2007 Sector Congestion and limits of Radio Frequency Spectrum Availability ~ 2010
Transition Barriers
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Ground Based Infrastructure Airborne Equipment Labor Issues L--- M ----H L----M--- H L----M--- H Regulation Required Culture Change L----M--- H L----M--- H Communication Bandwidth LACK OF INCENTIVES TO CHANGE !!!!
L--- M ----H
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Outline
Limitations on Air Transportation Capacity
Safety, Capacity and Delay
System Network Effects
Future Security Effects
Observations
Future Vision
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Operational Capacity is a Limited Commodity
C MAX
= 2 C AR MAX S
i (XG) i R i {Airports} –
K A K (t) S = f ( Safety, {Airspace Management Intervention}
ATC , Wake Vortex, etc.
) ~ 0.6
A K (t)
= (A/C REQUEST – A/C ACCEPT ) ~ [ 0 to >1,000] A K (t) = f ( GDP:Weather, Sector Workload Constraints )
C AR MAX ~ 64 Arrivals/Hour (set by Runway Occupancy Time)
R i = Number of Runways at i th Airport
XG i = Airport Configuration Factor at i th Airport
i = 1 to N, where N is approximately 60 Airports
K = 1 to M, where M is typically much less than 100 Sectors
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Regional Distribution of Airport Infrastructure is Uneven TABLE 1 Regional Air Transportation Capacity Fraction For (57) Major Airports NUMBER Estimated
%
Avg 8 yr HUB # A/C TURN Number Ops/Hr Cap97/ Growth REGION R/W POINTS MODEL 1997 CapMAX Rate % NORTH EAST PACIFIC SOUTHWEST PACIFIC NORTHWEST NOTHERN MIDWEST ATLANTIC COAST CENTRAL MIDWEST WEST SOUTHEAST FLORIDA & LATIN AM SOUTH SOUTHWEST TOTAL
14 9 22 42 13 12 22 21 14 27
196
420 262 353 773 269 205 415 424 322 380
3823
348 403 693 1090 438 237 758 776 602 892
6239
294 298 455 684 241 131 405 391 287 433
3620 84 74 66 63 55 55 53 50 48 48 58
9 10 8 32 8 3 9 -2 18 16
11 % NATIONAL TOTAL TAF 1997 ENP X10E6
54 43 62 99 31 19 62 54 48 59
532 89 OPERATIONS 2012
1,950,000 2,205,000 3,364,000 5,522,000 1,701,000 1,496,000 3,180,000 2,704,000 2,114,000 3,468,000
27,704,000 78 1997
1,645,786 1,670,280 2,549,603 4,040,088 1,347,458 1,114,207 2,270,307 2,190,557 1,608,673 2,424,105
20,861,064 77
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Donohue and Shaver, TRB 2000
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Airport Diseconomies of Scale
1.20
1.00
0.80
0.60
0.40
0.20
0.00
0 Airport Runway Diminishing Returns XG Factor Power (XG Factor) 2 4 RUNWAYS 6
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Non-Linear Network Characteristics
200 150 100 50 0 0 350 300 250 500 450 400 EXAMPLE OF AIR TRANSPORTATION SYSTEM NON-LINEARITY LINEAR SYSTEM NON-LINEAR SYSTEM 100 200 300 TOTAL AIRPORT CAPACITY (OPS/HR) 400 500 3 Airport Network at 100 operations each + 50 operations increase
NAS is a Highly Non-Linear, Adaptive System
Controller-in-the-Loop
AOC-in-the-Loop
Independent Network Schedules
Stochastic In Nature May exhibit Chaotic Behavior under Some Conditions
Additive Improvements DO NOT result in Additive Increases in NAS Capacity
ie. pFAST, Runways, etc.
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Airline Schedule has a Strong Effect on Network Performance – Model Prediction to 20% Airport Capacity Increase DPAT Simulation, benchmark capacity, airports ranked by delay extent, with sector 3 MITRE DPAT MODEL 2.5
Linear System Response 2 1.5
1 Average Arrival Delay ( queue on runway) 0.5
0
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Outline
Limitations on Air Transportation Capacity
Safety, Capacity and Delay
System Network Effects
Future Security Effects
Observations
Future Vision
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Capacity vs. Delay Penalty
IAD Delay
600 Delay 400 200 0
Arrivals [3] “ACE 1999 Plan,” CD-ROM. Federal Aviation Administration – Office of system capacity.
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Depart
NY LaGuardia: A non-Hub Maximum Capacity Airport
1 Arrival Runway
1 Departure Runway 45 Arrivals/Hr (Max) 80 Seconds Between Arrivals 11.3 minute Average Delay
77 Delays/1000 Operations 40 min./Delay
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New York LaGuardia Airport Arrival- Departure Spacing VMC
14 60 50 ASPM - Apr 2000 - Visual Approaches ASPM - Oct 2000 - Visual Approaches Calculated VMC Capacity Optimum Rate (LGA) 40 30 20 10 40,40 0 0 10 20 30 40
Departures per Hour
50 60
Each dot represents one hour of actual traffic during April or October 2000
45 Arr./Hr/RW @ 80 sec separation DoT/FAA
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Atlanta: A Maximum Capacity Fortress Hub Airport
2 Runways – Arrivals 2 Runways – Departures 50 Arrivals/Hr/RW – Max 72 Seconds Between Arrivals 8.5 minutes Average Delay 36 Delays/1000 Operations 38 min./delay
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Atlanta Airport Arrival-Departure Spacing VMC
120 100 80 60 40 20 0 0 100,100 ASPM - April 2000 - Visual Approaches Calculated VMC Capacity Optimum Rate (ATL) 20 40 60 80
Departures per Hour
100 120
Each dot represents one hour of actual traffic during April 2000
50 Arr/Hr/Rw @72 sec Separation DoT/FAA
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LAX ATL STL ORD SEA MSP LGA SFO PHL EWR IAD DTW DFW CLT PIT JFK BWI DEN 0
Major US Airport Congestion
0.1
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Queuing Delays Grow Rapidly J. D. Welch and R.T. Lloyd, ATM 2001 0.8
DEMAND / CAPACITY RATIO 0.7
0.9
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Aircraft Arrival Rate: Distance-Time Relationship
80 70 60 50 40 30 20 10 0 0 1 2 3 4 DISTANCE ( NMi) 5
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120 Knots 130 Knots 140 Knots 6 7 Spacing (sec) 60 72 90 120 180
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35 30 25 70 sec 20 15 SAFETY 10 5
LGA Aircraft Inter-Arrival Time Distribution
LGA Arrival Seperation Histogram μ = 134 sec., σ = 73 sec.
LGA VMC CAPACITY 96 Sec. WV Separation Expected Value 0 0 -5 50 100 150 200 250 300 Aircraft Inter-Arrival Time (seconds) 350
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Possible Relationship Between Safety and Capacity: ATM Technology Effect 140.00
120.00
100.00
80.00
60.00
40.00
20.00
0.00
Hypothesis: SAFETY-CAPACITY SUBSTITUTION CURVES US 15 HULL LOSS/YR 5 HULL LOSS/YR 2 HULL LOSS/YR S-C @ CURRENT TECH.
S-C @ REDUCED SEP.
2.00
4.00
6.00
8.00
MILLION DEPARTURES/HULL LOSS
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10.00
12.00
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Outline
Limitations on Air Transportation Capacity
Safety, Capacity and Delay
System Network Effects
Future Security Effects
Observations
Future Vision
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The Semi-Regulated Market Does Not Act to Minimize Delay: LGA Air 21 Impact
22 LaGuardia Airport 200 180 160 140 120 100 80 60 40 20 0 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Time of Day
Maximum Hourly Operations Based on Current Airspace & ATC Design Historic Movements AIR-21 Induced Svc.
Source: William DeCota, Port Authority of New York
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Annual and Seasonal Delay Trends (
Note Possible Effect of Air 21 on LGA & System) OPSNET Total System Delays 60
50 40 30 20 10 0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Month
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2000 1999 1998 1997 1996 1995
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Outline
Limitations on Air Transportation Capacity
Safety, Capacity and Delay
System Network Effects
Future Security Effects
Observations
Future Vision
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Observations
Approximately 10 of the Top US Hub Airports are Operating close to Maximum Safe Capacity Demand / Capacity Ratio’s Greater than 0.7 lead to Very Rapid Increase in Arrival and Departure Delays
Higher Delays Lead to Loss of Schedule Integrity
25 New Runways Not a Solution
Airline Hub and Spoke Network System Produces a Highly Non-Linear, Connected System
Weather, Security or Terminal Delays Propagate
System Wide
Airline Schedules are part of the Problem & Solution
ATC Sector Controller Workloads and Weather also Produce Network Choke-Points that Produce Capacity Constraints
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Observations (cont.)
100% EDS Baggage Screening will either Increase Delays or Travel Block Times for Commercial Ops
Current Regulations on Airlines and Airports do not provide Incentives for either Safe or Efficient Operations
Airlines are over-scheduling Major Airports
ATC is spacing Aircraft at the limits of current technology leading to growing safety concerns
Airlines are moving to Smaller aircraft to increase frequency of operations and profitability, leading to increased congestion and delays
Airlines are resisting modernizing their aircraft with the technology required to decrease spacing and increase capacity
Incentives are to be last to equip
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Outline
Limitations on Air Transportation Capacity
Safety, Capacity and Delay
System Network Effects
Future Security Effects
Observations
Future Vision
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Vision: Incentives for Operational Improvements and Modernization
Brief Summary of Vision: Major Hub Airports will Allocate Slots by DoT Auctions:
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Both Strategic, Near Term and Spot Auctions
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Peak runway loading will be reduced to Government Established Safety and Capacity optimized schedules
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Aircraft Size will be driven by a combination of airline profits and maximum enplanement opportunities Business travel will migrate to Travel on Demand via air-taxi or private aircraft ownership and operation Increased En-route Traffic density will be accommodated by Aircraft Self Separation-Technology-Equipped Flight Corridors Auctions will provide incentives for aircraft technology insertion and a government contract to provide enhanced benefits
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George Donohue
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Outline
Limitations on Air Transportation Capacity
Safety, Capacity and Delay
System Network Effects
Future Security Effects
Observations
Future Vision
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Key Airport System Flows
MIT Queuing Model Entry Fix Arrivals Arrival Paths Dept.
Paths Runways Taxiways Departures Ramp Gates Pax Screen Passengers Check-In ID Ckd Bag Screen Bags/Cargo Gnd Trans Drop-off Parking Departure Fix Airside Groundside
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Baggage: Actual and Spread Demand for 1998 DFW Case (RAND Study) 1000 900 800 700 600 500 400 300 200 100 0 6 Scheduled Demand, No Spread Total Check-In Bags = 56,516 7 8 9 10 11 12 Demand Spread = 30 mins (flat) 13 Time of Day 14 15 16 17
Dr. R. Shaver George Mason University
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Planned Time of Arrival According to Passenger Propensity to Accept Risk “The Passenger’s View” 120 105 90 75 60 45 30 15 0 100.0%
32 R. Shaver, RAND Machines Deployed = [17:24] Machines Deployed = [19:28] Machines Deployed = [20:29] Machines Deployed = [22:32] Machines Deployed = [24:34] Machines Deployed = [15:22]
10.0% 1.0% Probability that Bag Misses Plane
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0.1%