Underlying Problems and Major
Research Issues Facing the US Air
Transportation System
George L. Donohue, Ph.D.
Professor, Systems Engineering and Operations Research
Director, Center for Air Transportation Systems Research
2nd International Conference on Research in Air
Transportation - ICRAT 2006
Belgrade, Serbia and Montenegro
June 24, 2006
CENTER FOR AIR TRANSPORTATION SYSTEMS RESEARCH
Credits
Research Team at GMU that have contributed to
these Insights:
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Rudolph C. Haynie, Ph.D. (2002), Col. US Army
Yue Xie, Ph.D. (2005)
Arash Yousefi, Ph.D. (2005)
Loan Le, Ph.D. Candidate (expected 2006)
Danyi Wang, Ph.D. Candidate
Babak Jeddi, Ph.D. Candidate
Bengi Mezhepoglu, Ph.D. Candidate
Dr. Lance Sherry, Exec. Dir. CATSR
Dr. John Shortle, Assoc. Prof. SEOR, CATSR
Dr. C.H. Chen, Assoc. Prof. SEOR, CATSR
Dr. Karla Hoffman, Prof. SEOR, CATSR
CATSR
Outline
 Worldwide Generic Problems in Air
Transportation
Economic System of Systems
Stochastic Safety Process Control
Airspace Designs are not Optimum
 US has some Unique Problems in Air
Transportation
Little Concern for Passengers Quality of Service
Airport Congestion Regulations Chaotic
 Future Research should focus more on:
Passenger Metrics and less on Aircraft Operations
Metrics
Stochastic Metrics and Regulations
Economic System Control Mechanisms
CATSR
CATSR
Economic System of Systems
Air Transportation is a Complex Adaptive
System (CAS) Problem
CATSR
• Essential Elements of a CAS:
• Complex
– Multiple Agents with many variables always working on the Edge of
Stability
– Possess Strong Non-linear Interrelationships but Try to bring some
Order out of Chaos
• Spontaneous and Self Organizing
– Multiple Independent Agents Optimizing different Object Functions
(i.e. constantly Learning and Adapting)
• Evolutionary
– constantly demonstrating Emergent Behavior
• Requires a Different Modeling Approach that Includes
ALL Relevant Strong Feedback Loops
Air Transportation System:
Agents, Inter-relationships, Adaptive Behavior and Stability
CATSR
Capacity Offset
Suppliers of Air
Traffic Flow
Services
Suppliers of Air
Traffic
Infrastructure
Suppliers of Air
Transportation
Services
Total Seats
Seats, Parking,
Rental Cars
Enplanements
Demand for Air
Transportation
Services
Regional Markets (Businesses, Citizens)
( = weeks, Variations: Daily, Weekly, Seasonal, Econ Cycles)
CAS Control Problem: Example Question
What is Impact of ADS-B ? Plausible Futures?
ADS-B
Initiatives
Delays, Flat
Fees & Taxes
Seats,
Parking,
Rental
Cars
Airfares, +
fees, taxes,
delay costs
Enplanements
Regional Markets (Businesses, Citizens)
( = weeks, Variations: Daily, Weekly, Seasonal, Econ
Cycles)
CATSR
Modernization requires understanding
system “pressure points” and
“tipping points” (i.e. nonlinearities)
Signaling Mechanisms DRIVE Air
Transportation System
• Balance Capacity and Demand
(by signaling scarce resources)
• Incentivize Innovation
Strong Signals (i.e. PRICES) yield:
• Effective Use of Scarce
Resources (e.g. yield
management, aircraft
assets,…etc)
• Vibrant Innovation in Airlines,
and Aircraft Manufacturers
sectors (see Real Yield)
Weak Signals (e.g. Delays, Flat Fees &
Taxes) yield:
• Unpredictable day-to-day
Operations
• Difficulty Valuing Service (e.g.
Airport Landing Slots, Labor
Salary Negotiations)
• Dormant Innovation Cycles
Dr. Lance Sherry and Benji Mezhepoglu
CATSR
Stochastic Safety Process Control
- Solid Theoretical Foundation
NOT BEING APPLIED TO ATM
Air Transportation Safety is a Stochastic
Characterization and Control Problem
CATSR
• International Safety Standards do not recognize
that they are Regulating Stochastic Processes that
have at least 2 Statistical Parameters that MUST
BE CONTROLLED
• Research results of :
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Dr. Rudolph C. Haynie (2002)
Dr. Yue Xie, (2005)
Mr. Babek Jeddi, (in progress)
Prof. John Shortle
Operations Around a Typical High
Capacity US Airport
(Mr. Babak Jeddi, research in progress)
Detroit
Airport (DTW)
CATSR
Sample Landings on 21L:
GMU Processed Multilateration Data
Distorted Scale
Correct Scale
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Data Analysis Process to Estimate:
IAT, IAD and ROT pdf’s
Airplane i
Threshold
Airplane i+1
Aircraft Type
Heavy
Large
Large
Small
Runway
Threshold
10:23:14
10:24:28
10:26:16
10:28:32
Leave Runway
10:24:04
10:25:13
10:27:12
10:29:28
...
...
...
Col. Clint Haynie, USA PhD., 2002
Yue Xie, PhD. 2005
CATSR
Runway Occupancy Time (ROT) at
AAR = 40 Arr/Rw/Hr
49 seconds
40 Ar/Rw/Hr
=90 seconds
• 669 samples for all aircraft types, peak IMC periods
• Sample mean is 49.1 sec.
• Sample std. dev. is 8.1 sec.
CATSR
Inter-Arrival Time (IAT)
SAFETY ?
40 Ar/Rw/Hr
LOST
CAPACITY
• IMC
• 3 nm pairs
• 523 samples (during peak periods)
• Fit: Erlang(40;11,6): mean 106 sec, std. dev. 27 sec.
CATSR
Inter-Arrival Distance (IAD)
SAFETY ?
ADS-B
RSA
LOST
CAPACITY
Schedules,
TFM, RTA
• IMC
• 3 nm pairs
• 523 samples (during peak periods)
• Fit: Erlang(1.5;0.35,6): mean 3.6 nm, std. dev. 0.86 nm.
CATSR
ROT vs. IAT to find Simultaneous Runway
Occupancy (SRO) Probability: est to be ~1 x 10-3
Runway
Occupancy
Time
(sec)
SRO
Region
Inter-Arrival Time (sec)
• Freq (IAT < ROT) ~= 0.0016 in peak periods and
0.0007 overall (including non-peak
periods)
• IMC: 1 / 669= 0.0015 in peak periods
• Correlation coefficient = 0.15
CATSR
ROT vs. IAT to find Simultaneous Runway
Occupancy (SRO) Probability: est to be ~1 x 10-3
Runway
Occupancy
Time
(sec)
SRO
Region
Inter-Arrival Time (sec)
•Question:
•Should P(SRO)= 1 x 10-6 /Arrival?
1 x 10-5 /Arrival?
1 x 10-4 /Arrival?
CATSR
Runway Occupancy Time (ROT) and
Increased AAR to 45 Arr/Rw/HR
45 Ar/Rw/Hr
• 669 samples for all aircraft types, peak IMC periods
• Sample mean is 49.1 sec.
• Sample std. dev. is 8.1 sec.
CATSR
Inter-Arrival Time (IAT)
SAFETY ?
45 Ar/Rw/Hr
LOST
CAPACITY
• IMC
• 3 nm pairs
• 523 samples (during peak periods)
• Fit: Erlang(40;11,6): mean 106 sec, std. dev. 27 sec.
CATSR
CATSR
New Airspace Design Paradigms
ATC Workload is not Uniform and
Airspace Designs are Not Optimum
• Current Airspace Designs in most countries predate modern computer Modeling and
Optimization era
• Controller Workload can become the Capacity
Limitation in some Airspace
• Current Controller Workload can be Decreased
with Center and Sector Optimized Re-design
• All New digital Data-Link and Automation
Systems will Benefit from Re-designed, workload
balanced airspace
Based on Research results
of Arash Yousefi (2005)
CATSR
WL as a continuous function of Lat, Lon, and
Time (Arash Yousefi, Ph.D. 2005)
WLt = f(  , )
where :
f is a generic function
t denotes the time interval
CATSR
Planar Projection of Workload Function ( WLt )
CATSR
Results of Center Boundary Re-design:
An Example
CATSR
CATSR
Passengers are Our Forgotten
Customers
- They Pay the Bills & Suffer the
Penalties for Poor performance
Passenger Quality of Service Metrics are
NOT Currently used for System Control
CATSR
• Most Research Emphasis has been on Flight
Delay and Airline Economic Benefits from
Reduced Fuel Consumption
• Little attention has been placed on the Passenger
Quality of Service (PQOS) or on the real Lost
Human Productivity
• Lost Passenger Productivity (GDP) due to System
Inefficiencies may EXCEED Airline fuel burn
Losses
• Flight Cancellations are as Important to
Understand and Model as Flight Delays
Recent Observations on Flights in the US 35
OEP Airport Network (2004)
• Total Passenger Trip Delay (TPTD) metric
defined (Danyi Wang (2006) work in progress)
• OEP 35 Airport Network:
• 3,000,000 flights, 1044 segments
• 20.5% delayed > 15 min (52,100,000 Hours Delayed)
• 1.78% flights cancelled (34,300,000 Hours Delayed)
• At $30/Hr = $2.6 Billion/yr Lost GDP
Productivity
CATSR
The Air Transportation System can be Modeled as
a Two Tiered Flow Model
• A two tiered flow model: the Vehicle Tier and the
Passenger Tier (Ms. Danyi Wang, research in Progress)
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Vehicle Tier Key Performance Index (KPI): Flight Delays, # of Delayed Flights, Cancelled
Flights, On-Time Flights, % of Delayed Flights, Cancelled Flights, On-Time Flights, etc.
Passenger Tier KPI: Passenger Trip Delay
Passenger Trip Delay = function (“Vehicle Flight Performance”, “Passenger Factor”)
CATSR
Strong Non-Linear Relationship Exists between Flight
Disruptions, Load Factors, Time and Total Passenger Delay
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Results:
• Average Passenger Delay grows Exponentially with load factor, especially
for days with high flight delays and cancellations.
• Low Service Frequency and Flight Disruptions late in the day contribute
significantly to the delay of disrupted passengers
Bratu & Barnhart (2005), Bratu (2003) and Sarmadi (2004)
CATSR
CATSR
Airports Need Some Schedule
Regulation for Safe, Efficient
and Predictable Transportation
US Does Little to Regulate
Airport Congestion
• Flight Schedules Drive Much of the Flight Delays
Observed in the US Air Transportation System
• Schedules are Uncoordinated (Anti-Trust Laws)
• Largely Unregulated by Arrival Slot Allocations
• These Delays at Hub Airports Impact the entire Air
Transportation Network
• Regulators are Concerned about the Adverse Effects of
Slot Regulation (for Congestion Management) on the
Private Service Provider’s Decisions on what Markets to
Serve
• i.e. What network connectivity and frequency
would result from profit maximizing airlines if
Capacitated Airport nodes were Regulated?
• This Question can be formulated as a Network
Commodity Flow Optimization Problem (Ms. Loan Le,
summer 2006)
CATSR
Excess of demand and severe congestion
at NY area airports: a 40-year old reality CATSR
Timeline recap of congestion management measures
HDR at EWR, LGA, JFK,
DCA, ORD
Perimeter rule at LGA, DCA
1969
- Limited #IFR slots
during specific time
periods
- Negotiation-based
allocation
early 1970s
Deregulation
1978
Removal of HDR
at EWR
Slot
ownership
AIR-21
1985
4.2000
Use-it-orlose-it rule
based on
80% usage
Exempted from
HDR at LGA
certain flights
to address
competition
and small
market access
Excess of demand and severe congestion
at NY area airports: a 40-year old reality CATSR
Timeline recap of congestion management measures
Lottery
AIR-21 at LGA
Apr-00
Jan-01
Removal of HDR
at ORD
Jul-02
End of HDR.
What’s next?
Jan-07
Excess of demand and severe congestion
at NY area airports: a 40-year old reality CATSR
Timeline recap of congestion management measures
Lottery
AIR-21 at LGA
Apr-00
Jan-01
Removal of HDR
at ORD
Jul-02
End of HDR.
What’s next?
Jan-07
Declining Trend of aircraft size: Fewer
Passengers at Constant Congestion Delay
CATSR
Small Aircraft & Low load-factor Flights:
High Delay & Lost Airline Revenue
?
CATSR
Congestion management options
 Laissez-faire: AIR-21
 HDR
 Airport expansion
Building new runway, new airport?
Develop reliever airports?
 Administrative options:
Collaborative scheduling
Bilateral? Multilateral?
 Market-based
Congestion pricing
Auction
Question: What is the best use of runway capacities?
 What markets get to stay at their current airport?
 What should fly to other substitutable airports?
 What is the right fleet mix and frequencies?
CATSR
Modeling airline flight scheduling:
Approaches
Model individual airlines
– Infinite number of competition behaviors
– New entrants?
– Limited data and inherent data noise
Model a Benevolent Single Airline
– Incorporates some competition requirement
– Best schedule that could be achieved
 benchmark for congestion management incentives
– Aggregate data reduce noise
Problem statement
Assuming the government as a benevolent single airline in NYC,
how would that airline optimize the flight schedule to LGA/EWR/JFK?
CATSR
New York LGA case study
CATSR
A few statistics:
Operations Throughput:
flights
Average Flight Delay:
Seat throughput:
Average aircraft size
Number of regular markets*
Average segment fare:
Revenue Passengers:
93,129
38 min
8,940,384 seats
96 seats
66 (277)
$133
6,949,261
Modeling Assumptions
 target period: Q2, 2005
 45 minutes turn-around time for all fleets
 75% load factor
 Fuel cost: $2/gallons
 Only existing fleets
Market daily frequencies and
geographical distribution: actual data
CATSR
Results: Profit maximizing service levels
CATSR
for unconstrained capacity scenario
(unconstrained scenario)
Markets decreasing:
BOS
DCA
FLL
RDU
ORD
ATL
PHL
DFW
CLT
…
7446
6842
4224
3622
6248
4834
2010
2618
3224
Results: Maximizing service levels at 10
ops/runway/15min
CATSR
Throughput
maximizing:
Profit
maximizing:
BOS
DCA
FLL
RDU
ORD
ATL
PHL
DFW
CLT
…
BOS
DCA
FLL
RDU
ORD
ATL
PHL
DFW
CLT
…
74 58
68 60
4444
3636
62 50
48 32
20 12
26 22
32 20
7446
6842
4424
3622
6248
4834
2010
2618
3224
Throughput Maximizing service level
at 9 ops/runway/15min
Throughput
maximizing:
BOS
DCA
FLL
RDU
ORD
ATL
PHL
DFW
CLT
CHM
GSO
IND
BUF
74 58
68 60
4444
363620
62 5044
48 3230
20 12
26 2218
32 20
26
20
18
12
18
12
22
16
CATSR
Throughput Maximizing service level
at 8 ops/runway/15min
Throughput
maximizing:
BOS
DCA
FLL
RDU
ORD
ATL
PHL
DFW
CLT
CHM
GSO
IND
BUF
DTW
74 58
68 60
4444
30
363620
62 5044 34
48 3230
20 12
10
26 2218
32 20
26
20
18
12
18
12
22
16
32
20
CATSR
Summary of results for LGA
Non-monotonic behavior for profit maximizing schedules
Monotonic behavior for seat throughput maximizing schedules
CATSR
Directions for Future Research
Future Research should focus more on:
Passenger Metrics and less on Aircraft
Operations Metrics
Stochastic Metrics and Regulations
Optimum Airport Slot Utilization
Economic System Control Mechanisms
Dynamic Super-Sector Designs with Optimum
Convective Weather Avoidance Capability
CATSR
References
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CATSR
Haynie, R.C. (2002), “An Investigation of Capacity and Safety in NearTerminal Airspace for Guiding Information Technology Adoption” GMU PhD
dissertation
Yousefi, A. (2005), “Optimum Airspace Design with Air Traffic Controller
Workload-Based Partitioning” GMU PhD disertation
Xie, Y. (2005), “Quantitative Analysis of Airport Arrival Capacity and Arrival
Safety Using Stochastic Methods” GMU PhD dissertation
Le, L. (2006 expected), “Demand Management at Congested Airports: How
Far are we from Utopia?” GMU PhD dissertation
Wang, D., Sherry, L. and Donohue, G. (2006) “Passenger Trip Time Metric
for Air Transportation”, The 2nd International Conference on Research in Air
Transportation (ICRAT), June 2006
Jeddi, B., Shortle J. and L. Sherry, “Statistics of the Approach Process at
Detroit Metropolitan Wayne County Airport”, The 2nd International
Conference on Research in Air Transportation (ICRAT), June 2006
http://catsr.ite.gmu.edu/home.html