Optimum Fleet Utilization under Congestion Management at ... · PDF fileOptimum Fleet...

CENTER FOR AIR TRANSPORTATION SYSTEMS RESEARCHCENTER FOR AIR TRANSPORTATION SYSTEMS RESEARCHCENTER FOR AIR TRANSPORTATION SYSTEMS RESEARCHCENTER FOR AIR TRANSPORTATION SYSTEMS RESEARCH

Optimum Fleet Utilization under Congestion Management at NY LGA

George L. Donohue, Ph.D.Professor Systems Engineering and Operations Research

Director of the Center for Air Transportation Systems ResearchVolgenau School of Information Technology and Engineering

NEXTOR Wye River Conference June 7, 2007

© George L. Donohue 2007

2

CATSRCATSRCATSRCATSRCreditsResearch team at GMU contributing to

these insights:• Dr. Loan Le, Ph.D. (2006)• Dr. Karla Hoffman, Prof. SEOR, CATSR• Danyi Wang, Ph.D. Candidate• Ning Xie, Ph.D. Candidate• Dr. C.H. Chen, Prof. SEOR, CATSR

RAND Corp.:• Dr. Russell Shaver, Senior Research Fellow

3

CATSRCATSRCATSRCATSROutline

• Motivation for the Study• NY LaGuardia Data• Approach• Results from Schedule Optimization and

Delay Simulation Study

4

CATSRCATSRCATSRCATSR

Annual Passenger Enplanements Predicted to be Lost: FAA Forecast to 2025

Annual Projected Enplanements Foregone Because of Airport Capacity Constraints

0

5

10

15

20

25

30

ATL

BO

S

BW

I

CLE CLT

CVG DC

A

DEN

DFW

DTW

EWR

FLL

HN

L

IAD

IAH

JFK

LAS

LAX

LGA

MC

O

MD

W

MEM M

IA

MSP

OR

D

PDX

PHL

PHX

PIT

SAN

SEA

SFO

SLC

STL

TPA

Airports

Ann

ual E

npla

nem

ents

Los

t (M

illio

ns)

202520152005

Optimistic: All Planned Airport Improvements

Occur

All available landing slots fully utilized regardless of congestion

EWR

ORDJFK

FAA 2006 TAF & 2004 Benchmark

LGA

5


Estimated Annual Cost to US (Lost Consumer Surplus, 2005$) due to Expected Airport Capacity Limitations

$0

$2

$4

$6

$8

$10

$12

$14

$16

100% 95% 90% 85% 80%Usable NAS Capacity (%)

Ann

ual C

ost t

o U

S Ec

onom

y fr

om

Fore

gone

Enp

lane

men

ts ($

B)

CY2015, All A/PImprovements

CY2015, No A/PImprovements

CY2025, All A/PImprovements

CY2025, No A/PImprovements

FAA Assumptions on Growth in Airport Operations and Passenger E l t

Shaver 2007

Assumes:$200/segment ticketPrice Elasticity = -1

6


Severe Congestion at HDR 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, DCA

4.2000

Exempt from HDR at LGA,

JFK, ORDcertain 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

Introduction of Hub-and-Spoke Network System

7


How is the Public Best Served?Top 20 Worst Airports in the US (45-PTD)

Year

Rank Airports

Prob. Of PaxDelay >45 min Airports



Prob. Of PaxDelay >45 min

1 ORD 14% EWR 18% ORD 17% EWR 16%2 EWR 14% LGA 17% EWR 16% LGA 15%3 LGA 13% ATL 14% LGA 15% ORD 15%4 PHL 12% PHL 13% PHL 15% PHL 13%5 ATL 11% BOS 13% JFK 14% ATL 12%6 MIA 9% ORD 12% IAD 12% JFK 11%7 FLL 9% FLL 12% MIA 12% BOS 11%8 MCO 9% JFK 12% ATL 12% MIA 11%9 DFW 9% MIA 11% MDW 12% FLL 10%

10 LAS 9% SFO 11% DTW 12% IAD 10%11 BOS 9% SEA 10% DFW 12% DFW 10%12 SFO 9% IAD 10% BOS 11% SFO 10%13 IAD 9% TPA 10% DEN 11% DTW 9%14 JFK 9% MCO 10% CLT 10% MCO 9%15 CLE 9% BWI 9% IAH 10% LAS 9%16 SEA 8% PIT 9% CLE 10% CLE 9%17 TPA 8% PDX 9% PIT 10% PIT 9%18 STL 8% DTW 9% DCA 10% SEA 9%19 PDX 8% LAS 9% MEM 10% MDW 9%20 BWI 8% DCA 9% SFO 10% DCA 9%

2004 2005 2006 Average of 2004 to 2006

D. Wang, GMU PhD. In Progress

8


EWR a NYNJ Airport with No Slot Controls:Market Acceptable Transportation Predictability (2006)

Time of Day (hour)

Del

ay p

er F

light

(min

utes

)

EWR

0 5 10 15 20-40

-20

0

20

40

60

80

45 minute Passenger Trip Delay (45-PTD) Metric that Includes Flight Load Factors, Cancellations & Missed Connections

(D. Wang GMU 2007)

35 Airport BN Model

Summer 2006 Data

(N. Xie GMU 2007)

9


Marginal Rate IFR Rate

Calculated Capacity (Today) and Actual Throughput

Optimum Rate

0

20

40

60

80

0 20 40 60 80

Departures per Hour

Arr

ival

s pe

r Hou

r

Calculated Capacity - Today

Facility Reported Rate - EWR(arrivals, departures per hr)

Each symbol represents actual traffic during a single hour

Infrequent Most Frequent

42, 42

0

20

40

60

80

0 20 40 60 80

Departures per Hour

Arr

ival

s pe

r Hou

r

40, 40

0

20

40

60

80

0 20 40 60 80

Departures per Hour

Arr

ival

s pe

r Hou

r

33, 33

EWR :

DoT/FAA

2004 Capacity Benchmark Report

Operational Rates Inside this Envelope Satisfy FAA Safety Standards (i.e. establish Max. Number of Arrival and Departure Slots)

10


Are Airlines Making Optimal use of these Operations? EWR Fleet Mixture

Monthly Averaged T-100 Segment Data

11

CATSRCATSRCATSRCATSRNew York LaGuardia Airport: Case StudyData (2005):

• Throughput:404,853 flights/yr

• Average flight delay:38 min

• Revenue passengers:26,671,787

• Average aircraft size:96 passenger

• Average inter-city fare: $133

12


NYNJ Airport with Current Slot Controls:LGA 2004 - 2006



Optimum Rate

0

20

40

60

0 20 40 60

Departures per Hour

Arr

ival

s pe

r Hou

r


Facility Reported Rate - LGA(arrivals, departures per hr)

39,39



0

20

40

60

0 20 40 60

Departures per Hour

Arr

ival

s pe

r Hou

r

37,37

0

20

40

60

0 20 40 60

Departures per Hour

Arr

ival

s pe

r Hou

r

37,37

Time of Day (hour)

Del

ay p

er F

light

(min

utes

)

LGA

0 5 10 15 20-40

-20

0

20

40

60

80

13


Current Government Rules at LGA AlsoLead to Poor Use of Runway Resources

• Inefficient use of resources

Airports loseAirlines lose

(Low load factor/Small

Aircraft)

Airports winAirlines win

(High Load Factor/Large

Aircraft)

14


Why do the Airlines Schedule beyond the Maximum Safe RW Capacity with Flights that Loose Revenue?

• There is no government regulation to Limit schedules for Safety or Compensate passengers for Excessive Delays and Volume related Flight Cancellations• These were errors in the 1978 Deregulation Act• Congress creates New Slots

• Passenger surveys indicate that Frequency and Price are the most (Only?) desirable characteristics of a flight

• Passengers are not Told of Consequences of published schedule to travel Predictability

• If any One airline decided to offer Feasible Schedules, their competition might offer more frequency to capture market share (No Good Deed goes Unpunished!)• Thus, still producing delays and cancellations for All

• In Game Theory, this is called the Prisoner’s Dilemma

15


• What would happen if schedules at major airports were capped by predictable runway capacity and allocated by a market mechanism?• What markets would be served?• How would airline schedules change?

– Frequency– Equipment (#seats per aircraft)

• How would passenger demand change?– At airport– On routes

• How would airfares change?– What would happen to airline profit margins?

• How would airport and network delays be altered?

A Natural Question? Is There an Optimal Allocation of Scarce Runway Resources?

16

CATSRCATSRCATSRCATSRModeling Approach and Assumptions

• A Benevolent Monopolistic Airline (e.g. Port Authority of NY&NJ) has the ability to Determine and Set an Optimum Schedule to:• Operate at Competitive Profit Margins• Maximize Passenger Throughput• Ensure an Airline Operating Profit (Max, 90%,80%)

• All Current Domestic Origin and Destination Markets are Considered• 67 Scheduled Daily Service Markets

• Current Market Price Elasticity Remains Constant• BTS T-100 segment data

17

CATSRCATSRCATSRCATSRNY LGA Has 67 Daily Markets

18


Network Flow Optimization Problem

(Le, 2006)

Demand-Price Elasticity$

#

S1S2

DASPM, BTS databases

Delay Network Simulation

Flight schedulesFleet mixAverage fareFlight delays

Airline Competitive Scheduling: Modeling Framework

Auction 64 Ops Slots/Hr

19


Research Results:Detailed Data at 90% of Profit Optimality

Estimated Effect of Slot Controls at LGA Using Market Mech

-120%

-100%

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

0 1 2 3 4 5 6 7 8 9 10

Arrival Rate per 15min Time Slot

Perc

ent C

hang

e fr

om C

urre

nt P

olic

y

Market Change# flts/dayMax Enpl/dayAvg AC Seat CapAvg FareAvg Flt Delay

BL U 10 9 8 7 6 5 4

LGA IMC Capacity Aircraft Size

Airfare

Markets servedFrequency of Service

Delays

Max enpl/day

20


90% Profit Max - 64 Ops/Hr compromise: Frequency and Delay profile by time of day

21


Optimized Schedule Frequency and Aircraft Gauge by Market (Opt/Current)

Optimized Frequency and Aircraft Gauge

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63

Frequency Rank

Opt

imum

to C

urre

nt R

atio

Flight FrequencyAircraft Gauge

Freq. A/C Gauge0.8 1.2 Mean0.9 1.1 Median1 1 Mode

0.23 0.50 SD

> 2/Hr

22

CATSRCATSRCATSRCATSRModel Estimate of Aircraft Gauge Change

Estimate of Aircraft Up-Gauging

0

50

100

150

200

250

300

19 to 37 44 to 50 69 to 77 88 to110

117 to133

138 to158

166 to181

194 to225

Aircraft Seating Capacity

Num

ber

of D

aily

Flig

hts

Current Fleet Allocation 90% Optimum Fleet Allocation

Current 90% Opt %Aircraft Size Range # Flights # Flights Change

19 to 37 64 66 3%44 to 50 259 54 -79%69 to 77 90 90 0%

88 to 110 192 118 -39%117 to 133 121 86 -29%138 to 158 235 252 7%166 to 181 49 72 47%194 to 225 0 68 -

Total 1010 806 -20%

23

CATSRCATSRCATSRCATSRUnprofitable daily markets at LGA

• Three markets (13 Flights) that are not profitable to operate on a daily basis are identified to be:• Lebanon-Hanover, NH (LEB),• Roanoke Municipal, VA (ROA),• Knoxville, TN (TYS).

Runway Cap. Market seats/AC Fare Passengers RPM Yield Flights/dayunconstrained LEB 19 $153 50 $0.72 6

10,9,8,7 ROA 37 $186 77 $0.46 5 6,5,4 TYS 50 $125 85 $0.19 2

24


•Airlines• Reduced frequency with

larger aircraft• Most Markets Retained• More Profitable (90% of

Optimum)•Passengers

• Markets served: Little change

• Airfares no change• Improved Predictability

• Airports• Increased passenger

throughput• Reduced delays (70%)

•Air Traffic Control• Reduced delays

– Demand within capacity– Reduced Prob. SRO

Airlines adapt with aircraft size and frequency to congestion constraint: Positive impacts on passengers, airports, airlines, and ATC

Research Results – Win Win

25

CATSRCATSRCATSRCATSRConclusions and Recommendations

• Airport Congestion Management will be Required to Accommodate Projected passenger growth rates

• Market Based Approaches May be able to Approximate Optimum Allocation of Scarce Runway Availability Resources

• Metropolitan “Metroplex Operation” should be Investigated to Better Understand Airport Synchronization Possibilities under Congestion Management Measures

26

CATSRCATSRCATSRCATSROpinion

• FAA Owns slots Because:• FAA computes Max Number of Safe Arrival and

Departure Combinations as a Function of:– Airport Runway Configuration, – Separation Technology – Designated Level of Safety

• Daily GDP control (and Acceptance by Airlines) is an implicit exercise of this ownership

• Slot Exemptions (or total lack of control) are an implicit reduction of the FAA’s stated safety standards• Standards should be either changed or enforced

27


Center for Air Transportation System Research Publications and Information• Loan Thanh Le, “Demand Management at Congested

Airports: How far are we from Utopia?”, Ph. D. dissertation August 2006.

• http://catsr.ite.gmu.edu

– Other Useful Web Sites

• http://mytravelrights.com• http://gao.gov• http://www.airconsumer.ost.dot.gov

http://catsr.ite.gmu.edu/

http://mytravelrights.com/

28


BACKUP Material

29


DCA a Conservatively Scheduled High Demand Slot Controlled Airport



Optimum Rate

0

20

40

60

0 20 40 60

Departures per Hour

Arr

ival

s pe

r Hou

r


Facility Reported Rate - DCA(arrivals, departures per hr)


36, 36


0

20

40

60

0 20 40 60

Departures per Hour

Arr

ival

s pe

r Hou

r30, 30

0

20

40

60

0 20 40 60

Departures per Hour

Arr

ival

s pe

r Hou

r

24, 24

Time of Day (hour)

Del

ay p

er F

light

(min

utes

)

DCA

0 5 10 15 20-40

-20

0

20

40

60

80

30


Congestion Management could Shift Hubbing Passengers to other Large Airports

ConnectingAirport Passengers

%Chicago O'Hare 59

Newark NJ 32NY LaGuardia 8

NY JFK 40Philadelphia 38

Atlanta 66Boston 15Miami 55

Washington Dulles 53Dallas/Fort Worth 60

FAA 2006 NPIAS

31


LGA High Frequency Flights:Current and 90% of Optimum

Market Daily A/C Model Model Normalized RankFreq seats Freq seats Freq Seats

Boston Logan BOS 73 106 60 208 0.8 2.0 1Washington DC Reagen Nat DCA 69 108 68 131 1.0 1.2 2Chicago O'Hare ORD 62 138 56 139 0.9 1.0 3Atlanta Hartsfield ATL 48 156 32 145 0.7 0.9 4Fort Lauderdale Fl FLL 43 157 26 181 0.6 1.2 5Raueigh/Durham NC RDU 37 46 22 95 0.6 2.1 6Detroit Mi DTW 32 122 22 175 0.7 1.4 7Charlotte NC CLT 32 102 30 97 0.9 1.0 8Columbus OH CMH 26 46 22 102 0.8 2.2 9Dallas Ft Worth DFW 26 148 26 146 1.0 1.0 10

32


Model preserves Heterogeneous Aircraft Mix:But Reduces Frequency and Up-Gauges some Markets

Model Schedule at 90% Optimum

0

50

100

150

200

250

0 10 20 30 40 50 60 70 80

Daily Flight Frequency

Ave

rage

Sea

ts p

er F

light

2005 ScheduleModel Sched @ 90% Opt

33


Runway Capacity is Set by Aircraft Safety Separation Standards

Airline Demand Driven by Market Access/Competition/Profitability Concerns

•High utilization rates (>80%)increase delays exponentially•Delays at major airports Impact the Entire Air Transportation Network

Network Delays Driven by Uncoordinated and Over-Scheduled Flights: e.g. LGA, EWR, JFK

1 Hr

34

CATSRCATSRCATSRCATSRJFK Fleet Mixture

35

CATSRCATSRCATSRCATSRSub-problem: Modeling a single market

Build timeline network: complete schedule of all possible flights, fleetsEstimate arc costsEstimate node revenues: for each 15-min arrival time window

ORD-LGA segment, 10%

Available data: 10% ticket price sample by quarter

#passengers

5000 10000 15000 20000

1000900800700600500400300200100

fare

($)

#passengers

200K 400K 600K

ORD LGA segment, 100%1000900800700600500400300200100

fare

($)

36

CATSRCATSRCATSRCATSRSub-problem: Modeling a single market

Build timeline network: complete schedule of all possible flights, fleetsEstimate arc costsEstimate node revenues: for each 15-min arrival time window

From demand curves to revenue curves

40K

35K

30K

25K

20K

15K

10K

5K

Rev

enue

s ($)

peak to off-peak

37

CATSRCATSRCATSRCATSRGeneral solution approach

Airport’s enplanement opportunitiesmaximizing

Set packingmaster problem

Airline Profit maximizing

Multi-commodity network

subproblems

ATL

TPA

BOS

67 nonstop

dailydomesticmarkets

ORD

…

…

LGA

schedulesfleets

marginal value of allowing

an extra flight

(dual prices)

38


Schedules exceeding airport optimum rates…

Congestion management by better scheduling

39


…lead to excessive queuing delay

Congestion management by better scheduling

40


ADR: Airport Departure RateAAR: Airport Arrival Rate

Facility-reported capacity: average of (ADR+AAR) over 2000-2005 period

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

HDR1969

1985Grandfather rights

4.2000

- LGA: 75 operations/h- AIR21 added additional exemption slots for 70-seat or less aircraft to small airports

600 requests = 50% more flights

41


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

HDR1969

1985Grandfather rights

AIR214.2000

1.2001Lottery

Removal of HDR at ORD6.2002

Removal of HDR at LGA and JFKWhat’s next?

1.2007

42


Summary of European Passenger Bill of Rights -http://news.bbc.co.uk/1/hi/business/4267095.stm

• Overbooked Flights• Passengers can now get roughly double the existing compensation if they are bumped off a

flight. – Compensation must be paid immediately. – These passengers must also be offered the choice of a refund, a flight back to their

original point of departure, or an alternative flight to continue their journey. • May also have rights to meals, refreshments, hotel accommodation if necessary even free e-

mails, faxes or telephone calls. • Cancelled Flights

• Offered a refund of your ticket, along with a free flight back to your initial point of departure, when relevant. Or, alternative transport to your final destination.

• Rights to meals, refreshments, hotel accommodation if necessary, even free e-mails or telephone calls.

– Airlines can only offer you a refund in the form of travel vouchers if you agree in writing

• Refunds may also be paid in cash, by bank transfer or cheque• If the reason for your flight's cancellation is "within the airline's control", it must pay

compensation. • Compensation for cancellations must be paid within seven days.

• Delayed Flights • Airline may be obliged to supply meals and refreshments, along with accommodation if an

overnight stay is required. • If the delay is for five hours or more, passengers are also entitled to a refund of their ticket

with a free flight back to your initial point of departure if this is relevant.

43


Air Transportation System (ATS) is a Network with 6 Interacting Layers

Government Regulatory Control LayerPhysical Layer (i.e. Cities, Airports, Demographics)

Weather Layer (Thunderstorms, Ice Storms)

Airline Layer (Routes, Schedules, A/C size)TSA/FAA Layer (ATC Radar, Radios, Ctr’s, Unions)

Passenger/Cargo Layer (Delays, Cancellations)

•The ATS is a Public - Private Partnership with conflicting objective functions:

•Public – Commerce and safety; interest groups

•Private – Profit maximization

44


Air Transportation is Characterized as aComplex Adaptive System (CAS)

Market Clearing

Baseline Demand

Effective Price

Effect on GDP

Fleet Revenue

Trips flown by fleet

Reference demand

Ticket price

Effective price by length of trip

Aircraft Fleets

Fleet Costs

Airline Profits

Schedule Active fleet

Offered Flights by Fleet

Flight Delays &

Cancellations

Passenger Delays

Delays

Inconvenience

Airport Capacity

Enroute Capacity

Fleet Attributes

Market Clearing

Baseline Demand

Effective Price

Effect on GDP

Fleet Revenue

Trips flown by fleet

Reference demand

Ticket price

Effective price by length of trip

Aircraft Fleets

Fleet Costs

Airline Profits

Schedule Active fleet

Offered Flights by Fleet

Flight Delays &

Cancellations

Passenger Delays

Delays

Inconvenience

Airport Capacity

Enroute Capacity

Fleet Attributes

Bengi Mezhepoglu, PhD in progress

45

CATSRCATSRCATSRCATSRResearch problems and findingsResearch problem 1:

Are current rules of slot allocation the main causes of the congestion problem?

Answer: Yes, grand-father rights, weight-based landing fees, slot exemptions

Research problem 2:Impacts on congestion, enplanement opportunities, markets served, aircraft size, flight demands ?

46

CATSRCATSRCATSRCATSRAirline response model

Model a single benevolent airlineModel the interaction of demand and supply through price

– Price elasticity of demand determine demand at each price point– Each supply curve corresponds to a fleet mix profile– Different supply levels result in different equilibriums

quantity (#seats)

fare

($)

quantity (#seats)

reve

nue

($)

E1

E2

E3

E1

E2 E3

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