Underlying Problems and Major Research Issues Facing the US Air Transportation System

CENTER FOR AIR TRANSPORTATION CENTER FOR AIR TRANSPORTATION SYSTEMS RESEARCHSYSTEMS RESEARCH

CENTER FOR AIR TRANSPORTATION CENTER FOR AIR TRANSPORTATION SYSTEMS RESEARCHSYSTEMS RESEARCH

Underlying Problems and Major Research Issues Facing the US Air Transportation System

George L. Donohue, Ph.D.Professor, Systems Engineering and Operations ResearchDirector, Center for Air Transportation Systems Research

2nd International Conference on Research in Air Transportation - ICRAT 2006

Belgrade, Serbia and MontenegroJune 24, 2006

CATSRCATSRCATSRCATSRCreditsResearch Team at GMU that have contributed to

these Insights:• 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

CATSRCATSRCATSRCATSROutline Worldwide Generic Problems in Air

TransportationEconomic System of SystemsStochastic Safety Process ControlAirspace Designs are not Optimum

US has some Unique Problems in Air TransportationLittle Concern for Passengers Quality of Service Airport Congestion Regulations Chaotic

Future Research should focus more on:Passenger Metrics and less on Aircraft Operations

MetricsStochastic Metrics and RegulationsEconomic System Control Mechanisms

CATSRCATSRCATSRCATSR

Economic System of Systems


Air Transportation is a Complex Adaptive System (CAS) Problem

• 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

Total Seats

Suppliers of Air Traffic

Infrastructure

Suppliers of Air Transportation

Services

Demand for Air Transportation

ServicesRegional Markets (Businesses, Citizens)

( = weeks, Variations: Daily, Weekly, Seasonal, Econ Cycles)

Enplanements

Seats, Parking, Rental Cars

Airports (Land-side)( = 2, 10, 30 years)

Airlines( = 3 Months)

Suppliers of Air Traffic

Infrastructure

Suppliers of Air Transportation

Services

Demand for Air Transportation

Services

Scheduled Flights

Taxiways Runways, Ap/Dp Cor., Airways

AARs, ADRs

Air Navigation Service Providers( = 7 years, Variations: Daily due to Weather)

Aircraft per Sector, Runway /Unit Time

Aircraft per Sector, Runway /Unit Time

Airports (Air-side)( = 2, 10, 30 years)

Airspace( = 1-2 years)

Capacity Offset

Suppliers of Air Traffic Flow

Services


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 InnovationStrong 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 Regional Markets (Businesses, Citizens)

( = weeks, Variations: Daily, Weekly, Seasonal, Econ Cycles)

Enplanements

Seats, Parking, Rental Cars

Airports (Land-side)( = 2, 10, 30 years)

Airlines( = 3 Months)

Scheduled Flights

Taxiways Rwys App.

Spac, Airways

Air Navigation Service Providers( = 7 years, Variations: Daily due to Weather)Aircraft per Sector,

Runway /Unit Time

Airports (Air-side)( = 2, 10, 30 years)

Airspace( = 1-2 years)

Aircraft per Sector,

Runway /Unit Time

ADS-B Initiatives

Delays, Flat Fees & Taxes

Airfares, + fees, taxes, delay costs

CAS Control Problem: Example Question

What is Impact of ADS-B ? Plausible Futures?

Dr. Lance Sherry and Benji Mezhepoglu


Stochastic Safety Process Control

- Solid Theoretical Foundation NOT BEING APPLIED TO ATM


Air Transportation Safety is a Stochastic Characterization and Control Problem

• 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 :• 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

Detroit Airport (DTW)

(Mr. Babak Jeddi, research in progress)


Sample Landings on 21L:GMU Processed Multilateration Data

Distorted Scale

Correct Scale


Data Analysis Process to Estimate:IAT, IAD and ROT pdf’s

Runway

Threshold

Airplane i+1

Airplane i

Aircraft Type Threshold Leave RunwayHeavy 10:23:14 10:24:04Large 10:24:28 10:25:13Large 10:26:16 10:27:12Small 10:28:32 10:29:28

. . .

. . .

. . .

Col. Clint Haynie, USA PhD., 2002

Yue Xie, PhD. 2005


• 669 samples for all aircraft types, peak IMC periods• Sample mean is 49.1 sec.

• Sample std. dev. is 8.1 sec.

Runway Occupancy Time (ROT) at AAR = 40 Arr/Rw/Hr

40 Ar/Rw/Hr

=90 seconds

49 seconds


• IMC• 3 nm pairs• 523 samples (during peak periods)• Fit: Erlang(40;11,6): mean 106 sec, std. dev. 27 sec.

Inter-Arrival Time (IAT)

SAFETY ?

LOST CAPACITY

40 Ar/Rw/Hr


• 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.

Inter-Arrival Distance (IAD)

SAFETY ?

LOST CAPACITY

ADS-B

RSA

Schedules, TFM, RTA


ROT vs. IAT to find Simultaneous Runway Occupancy (SRO) Probability: est to be ~1 x 10-3

Inter-Arrival Time (sec)

Runway Occupancy

Time(sec)

SRORegion

• Freq (IAT < ROT) ~= 0.0016 in peak periods and0.0007 overall (including non-peak

periods)• IMC: 1 / 669= 0.0015 in peak periods• Correlation coefficient = 0.15


ROT vs. IAT to find Simultaneous Runway Occupancy (SRO) Probability: est to be ~1 x 10-3

Inter-Arrival Time (sec)

Runway Occupancy

Time(sec)

SRORegion

•Question:•Should P(SRO)= 1 x 10-6 /Arrival?

1 x 10-5 /Arrival? 1 x 10-4 /Arrival?


• 669 samples for all aircraft types, peak IMC periods• Sample mean is 49.1 sec.

• Sample std. dev. is 8.1 sec.

Runway Occupancy Time (ROT) and Increased AAR to 45 Arr/Rw/HR

45 Ar/Rw/Hr


• IMC• 3 nm pairs• 523 samples (during peak periods)• Fit: Erlang(40;11,6): mean 106 sec, std. dev. 27 sec.

Inter-Arrival Time (IAT)

SAFETY ?

LOST CAPACITY

45 Ar/Rw/Hr


New Airspace Design Paradigms


ATC Workload is not Uniform and Airspace Designs are Not Optimum

• Current Airspace Designs in most countries pre-date 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)


t WL = f( , )

where :

f is a generic function

t denotes the time interval

WL as a continuous function of Lat, Lon, and Time (Arash Yousefi, Ph.D. 2005)

CATSRCATSRCATSRCATSRPlanar Projection of Workload Function ( WLt )


Results of Center Boundary Re-design:An Example


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

• 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


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)

• 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”)

CATSRCATSRCATSRCATSRStrong Non-Linear Relationship Exists between Flight Disruptions, Load Factors, Time and Total Passenger Delay

• 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)


Airports Need Some Schedule Regulation for Safe, Efficient

and Predictable Transportation


US Does Little to RegulateAirport 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)


Excess of demand and severe congestion at NY area airports: a 40-year old reality

- Limited #IFR slots during specific time periods

- Negotiation-based allocation

1969

HDR at EWR, LGA, JFK, DCA, ORD

Perimeter rule at LGA, DCA4.2000

Exempted from HDR at LGA certain flights

to address competition

and small market access

AIR-211978

Deregulation

Use-it-or-lose-it rule based on 80% usage

1985

Slot ownership

Timeline recap of congestion management measures

early 1970s

Removal of HDR at EWR




Apr-00

AIR-21

Jan-01

Lottery at LGA

Jan-07

End of HDR.

What’s next?

Jul-02

Removal of HDR at ORD



Apr-00

AIR-21

Jan-01

Lottery at LGA

Jan-07

End of HDR.

What’s next?

Jul-02

Removal of HDR at ORD



Declining Trend of aircraft size: Fewer Passengers at Constant Congestion Delay


Small Aircraft & Low load-factor Flights: High Delay & Lost Airline Revenue

?

CATSRCATSRCATSRCATSRCongestion management options

Laissez-faire: AIR-21 HDR Airport expansion

Building new runway, new airport? Develop reliever airports?

Administrative options: Collaborative schedulingBilateral? Multilateral?

Market-basedCongestion pricingAuction

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?


Modeling airline flight scheduling: Approaches

Model individual airlines– Infinite number of competition behaviors– New entrants?– Limited data and inherent data 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?

Model a Benevolent Single Airline– Incorporates some competition requirement– Best schedule that could be achieved

benchmark for congestion management incentives– Aggregate data reduce noise

CATSRCATSRCATSRCATSRNew York LGA case study

A few statistics: Operations Throughput:

93,129 flights Average Flight Delay: 38 min

Seat throughput: 8,940,384 seats Average aircraft size 96 seats Number of regular markets* 66 (277) Average segment fare: $133

Revenue Passengers: 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


(unconstrained scenario)

Results: Profit maximizing service levelsfor unconstrained capacity scenario

Markets decreasing:

BOS 7446DCA 6842FLL 4224RDU 3622ORD 6248ATL 4834PHL 2010DFW 2618CLT 3224…


Results: Maximizing service levels at 10 ops/runway/15min

Throughput maximizing:

BOS 74 58DCA 68 60FLL 4444RDU 3636ORD 62 50ATL 48 32PHL 20 12DFW 26 22CLT 32 20…

Profit maximizing:

BOS 7446DCA 6842FLL 4424RDU 3622ORD 6248ATL 4834PHL 2010DFW 2618CLT 3224 …


Throughput Maximizing service level at 9 ops/runway/15min


BOS 74 58DCA 68 60FLL 4444RDU 363620ORD 62 5044ATL 48 3230PHL 20 12DFW 26 2218CLT 32 20CHM 26 20GSO 18 12IND 18 12BUF 22 16


Throughput Maximizing service level at 8 ops/runway/15min


BOS 74 58DCA 68 60FLL 4444 30RDU 363620ORD 62 5044 34ATL 48 3230PHL 20 12 10DFW 26 2218CLT 32 20CHM 26 20GSO 18 12IND 18 12BUF 22 16DTW 32 20

CATSRCATSRCATSRCATSRSummary of results for LGA

Non-monotonic behavior for profit maximizing schedulesMonotonic behavior for seat throughput maximizing schedules

CATSRCATSRCATSRCATSRDirections for Future Research

Future Research should focus more on:Passenger Metrics and less on Aircraft

Operations MetricsStochastic Metrics and RegulationsOptimum Airport Slot UtilizationEconomic System Control MechanismsDynamic Super-Sector Designs with Optimum

Convective Weather Avoidance Capability

CATSRCATSRCATSRCATSRReferences• Haynie, R.C. (2002), “An Investigation of Capacity and Safety in Near-

Terminal 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

Documents

Underlying Problems and Major Research Issues Facing the US Air Transportation System