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Lessons* from Airport Gridlock: LaGuardia Airport

(*for Demand Management)

Amedeo R. Odoni and Terence P. C. Fan

Massachusetts Institute of Technology

March 19, 2002

NAS Resource Allocation Workshop

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Objective

• Provide background for Workshop discussions

• Recap LGA events between 4/2000 and 9/2001

• Emphasis on demand management aspects

• Implications and lessons regarding:

-- Sensitivity of airport delay to changes in demand

-- Magnitude of external delay costs relative to current levels of landing fees

-- Other complications

-- Environment in US vis-à-vis application of demand management

-- Nature of viable policies

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Premise

• Capacity expansion should be the fundamental means for accommodating growth of demand

• Demand management should be considered when capacity expansion is problematic, especially in the short run, due to– unreasonable cost; or– technical, sociopolitical or environmental problems with

long resolution times• In such cases, demand management should rely primarily

on those approaches that interfere the least with a deregulated and competitive market:– Congestion pricing– Auctions

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Case of LaGuardia

• Since 1969: “Slot”-based High Density Rule (HDR) – DCA, JFK, LGA, ORD; “buy-and-sell” since 1985

• Early 2000: About 1050 flights per weekday• April 2000 – Air-21 (Wendell-Ford Aviation Act for the Twenty-

first Century)– Immediate exemption from HDR for aircraft seating 70 or fewer on service

between small communities and LGA– Eventual elimination of HDR (by 2007)

• By November 2000 airlines had added over 300 flights per day; more planned– Virtual gridlock at LGA (25% of all OPSNET delays in Fall, 2000)

• December 2000: FAA and PANYNJ implemented slot lottery and announced intent to develop longer-term policy for access to LGA

• June 2001: Notice for Public Comment posted with regards to longer-term policy

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Outline

• Sensitivity to characteristics of demand and capacity

• External delay costs vs. the current cost of access

• Sample demand management systems

• Other complications

• Conclusions

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LGA demand before and after the lottery

November 2000 as a representative profile prior to slot lottery at LaGuardia; August 2001 as a representative after slot lottery.Source: Official Airline Guide

Scheduled operations per hour on weekdays

Time of day, e.g. 5 = 0500 - 0559

• Scheduled operations reduced by 10% (from 1,348 to 1,205/day)

0

10

20

30

40

50

60

70

80

90

100

5 7 9 11 13 15 17 19 21 23 1 3

Nov, 00

Aug, 01

75 flt/hour

Capacity of 75/hr does not include allocation of six slots for g.a. operations

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Small reduction in demand may lead to dramatic reduction in delays

Minutes of delay per operation

• Average delay reduced by >80% during evening hours

• Lottery was critical in improving operating conditions at LGA

Capacity = 75 operations/hr

Time of day

0

20

40

60

80

100

120

5 7 9 11 13 15 17 19 21 23 1 3

Nov, 00

Aug, 01

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A dynamic system

• A priori delay estimates may give only an upper bound on the true extent of delays

• Aircraft operators react dynamically on a day-to-day basis to operating conditions

• ASQP statistics (weekdays, Sept.- Dec. 2000): Average taxi-out time: 43 minutesAverage time from scheduled departure time to take-off: 80 minutes

On-time arrivals: 52%Cancelled flights: 9/00 => 6.7%; 10/00 =>5.1%

11/00 => 5.1%; 12/00 => 12.6%

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Comparing Queuing Model with ASQP Data

Average departure delay at LGA (minutes/flight) for Nov 13, 00 (VFR, light wind)

Time of day

Total flight operations per hour reduced by the observed cancellation rate from ASQP data from major carriers

0

20

40

60

80

100

120

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0

Actual departuredelay (majors)

Model - asscheduled

Model - adjustedfor cancellations

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Matching Total Demand with Capacity is Key

Total delay per weekday (aircraft-hour)

Demand reducedDemand leveled Demand leveled

• Impact from demand leveling is small compared to demand reduction

• Some demand peaks can be allowed under demand management

0

200

400

600

800

1000

1200

1400

Nov 00 actual Nov 00 level(0700-2159)

Aug 01 actual Aug 01 level

Total delay

4 pm - 7:59 pm

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Outline

• Sensitivity to characteristics of demand and capacity

• External delay costs vs. the current cost of access

• Sample demand management systems

• Other complications

• Conclusions

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Marginal delay cost due to an additional operation

Marginal delay caused by an additional aircraft (aircraft-hours)

Time of day

0

2

4

6

8

10

12

14

16

18

5 7 9 11 13 15 17 19 21 23 1 3

Nov, 00

Aug, 01

• Runway at LGA virtually “saturated” prior to slot lottery

• Delays propagate throughout the day

Capacity = 75 per hour

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Marginal delay cost dwarfs landing fee at LGA, even after lottery

Time of day – e.g. 5 = 0500 – 0559

$

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

5 7 9 11 13 15 17 19 21 23 1 3

Marginaldelay cost

Actualcharge

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External delay cost caused by an additional operation

• LGA: Feb 01 ~$6,000 (most of the day)

• BOS: ~$2,500 (16:00-21:00)

• AUS: ~$0

Hour of the day (during which an extra operation is added)

0

2

4

6

8

10

12

14

16

5 7 9 11 13 15 17 19 21 23 1 3

LaGuardia, Nov 00,at 75 ops/hr

LaGuardia, Feb 01,at 75 ops/hr

Boston, summer 98,at 115 ops/hr

Austin, Nov 00, at54 ops/hr

Marginal delay caused by an additional flight operation at four airports (aircraft-hour)

Peak-hour external delay costs:

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Congestion pricing

• Estimating the marginal delay cost that each additional operation causes to all other movements at an airport is central to congestion pricing

• At non-hub airports with many operators holding a limited share of airport activity, marginal delay cost is not internalized

• Congestion pricing aims at increasing efficiency of resource utilization by forcing users to internalize external costs

• Current landing (and take-off) fees at US airports bear little relationship to true external costs

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Hub demand - Atlanta

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Time of day – e.g. 5 = 0500 – 0559

Total scheduled movements per 15-minute intervals(November, 2000)

Source: FAA Airport Benchmark Report, 2001, Official Airline Guide

0

10

20

30

40

50

60

70

5 7 9 11 13 15 17 19 21 23

Arrivals

Departures

Approxoptimumcapacity

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Non-hub demand- LaGuardia

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Time of day – e.g. 5 = 0500 – 0559

Total scheduled movements per 60-minute intervals(November, 2000)

Source: Official Airline Guide Note: 75 flights/hr excludes allocation for general aviation

0

20

40

60

80

100

5 7 9 11 13 15 17 19 21 23 1 3

Arrival

Departure

At 75movements/hrof capacity

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Important to note…

• The external costs computed, in the absence of congestion pricing, give only an upper bound on the magnitude of the congestion-based fees that might be charged

• These are not “equilibrium prices”• Equilibrium prices may turn out to be

considerably less than these upper bounds• Equilibrium prices are hard to estimate

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Lessons

-The delay reductions that can be obtained from relatively small reductions in total daily demand

and-the external delay costs incurred in accessing runway systemscan be very large at some of the busiest airports – probably

well in excess of what most would guess

-The delay reductions that can be obtained from some “de-peaking” of daily demand profiles are typically more modest

• Adequate quantitative methods are available

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Outline

• Sensitivity to characteristics of demand and capacity

• External delay costs vs. the current cost of access

• Sample demand management systems

• Other complications

• Conclusions

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Proposed Demand Management Alternatives

• Three types of demand management strategies were put forward in June 2001:

1. Congestion pricing: PANYNJ (two options)2. Auctions: PANYNJ (two options)3. Administrative: FAA (three options): e.g.,

“encourage use of larger aircraft”

• In fact, all options under 1 and 2 contained strong administrative components, as well

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Example: Congestion Pricing, Option B

• Assumes HDR slots and AIR-21 lottery slots will be abolished• Target: demand total of 78 ops per hour; possible future revisions• Toll: surcharge on top of existing landing fee; arrs and deps• 06:00-22:00 weekdays; 06:00-14:00 Sat; 09:00-22:00 Sun• Three classes of movements:1. Exempt from congestion fee: 80 movements per weekday that

formerly qualified under AIR-21 (allocated by lottery, 2 slots per airline per round of the lottery)

2. Subject to congestion fee A: all other movements formerly qualifying under AIR-21; general aviation. (A ~ $350-700)

3. Subject to congestion fee B: all other operations (B ~ $700-2,000)

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Example: Auctions, Option A

• Assumes HDR slots and AIR-21 lottery slots will be abolished• Target: total = 78 ops/hr; 6 g.a. slots/hr, non-g.a. 75 slots/hr • Distribution of non-g.a. slots:1. Baseline allocation: each airline will be permitted up to 20 slots per

weekday, up to a total of 300 for all airlines; obtained through deposit refundable at end of one year; each airline may use maximum of 2 such slots per hour

2. Small hub and non-hub slots: 5 movements per hour; assigned by lottery (or possibly through auction or administrative procedure)

3. “Performance based” slots: 70 percent of remaining slots; allocated among airlines based on their market share of total revenue pax at LGA

4. Auctioned slots: remaining slots are auctioned

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Lessons (2)

• Public policy objectives (“fairness”, continuity, opportunity for new entrants, access for all operators, access for small communities) dictate use of hybrid demand management systems that combine administrative measures and market-based approaches

• The demand management systems that may eventually be implemented will have complex rules

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Outline

• Sensitivity to characteristics of demand and capacity

• External delay costs vs. the current cost of access

• Sample demand management systems

• Other complications

• Conclusions

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Target levels of demand

• Demand management measures have to aim, explicitly or implicitly, for a “target number” of daily and hourly movements at which an airport is expected to operate at an acceptable level of delay

• Airport capacity is dynamic and stochastic• Determining the target demand requires difficult trade-offs

between overall utilization of available capacity and performance when capacity is reduced

• Must look at performance over entire range of airport capacities and consider frequency with which associated weather conditions occur

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BOS: Annual Capacity Coverage Chart(assumes 50 % arrivals and 50 % departures)

10080

80

120

40

0

604020

Movements per hour

% of time

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What is legit?

• Fundamental statutory issues concerning demand management are unresolved, e.g.,

-- Are time-varying landing fees legitimate?-- Must all landing fees and aeronautical charges be

cost-related?-- Can airports re-distribute among users the proceeds

from access fees?• “Federal laws, regulations and US international obligations

may prevent PANYNJ from imposing these proposals. We will consider pertinent legal issues….”

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Real-time, CDM-enabled possibilities

• CDM has opened the possibility of implementing market-based demand management mechanisms on an as-needed basis in real time

• A “Slot Exchange”

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Outline

• Sensitivity to characteristics of demand and capacity

• External delay costs vs. the current cost of access

• Sample demand management systems

• Other complications

• Conclusions

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General observations on demand management

• Responsiveness to local characteristics is essential• Most appropriate environment for application of

market-based demand management approaches:– Non-homogeneous traffic

– Many airlines; no dominant ones

– Mostly non-connecting traffic

– Significant peaking of demand profile

• Very few (but important) US airports are good candidates

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Conclusion

• Airport demand management is a very complex systems problem• Technical issues:

– Estimating magnitude of externalities– Setting target level of demand in view of dynamic and stochastic

capacity– Prediction of user response to market-based measures– Proper balance between strategic and tactical interventions

• Murky statutory framework• Conflicting stakeholder objectives• Policies must balance objectives of efficiency, reliability and equity• Any viable policy will be a hybrid of administrative and market-

based measures

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The Queuing Model

Assume: Time-varying demand, approximated as non-homogeneous Poisson process; Time-varying capacity (“general” service times, with given expected value and

variance); approximated through Erlang family of probability distributionsInputs:

Dynamic demand profile (typically via hourly demand rates over 24 hours) Dynamic capacity profile (typically via hourly capacity rates over 24 hours)

Approach: Starting with initial conditions at time t=0, solve equations describing

evolution of queues, computing probabilities of having 0, 1, 2, 3, … aircraft in queue at times t = t, 2t, 3t, … up to end of time period of interest

Outputs:Statistics about queues (average queue length, average waiting time, total

delay, fraction of flights delayed more than X minutes, etc.)

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Upon “leveling” temporal distribution of demand…

Time of day – e.g. 5 = 0500 – 0559

Total scheduled movements per 60-minute interval(August, 2001, after slot lottery)

0

10

20

30

40

50

60

70

80

90

5 7 9 11 13 15 17 19 21 23 1 3

Actual

Leveled

75 flts/hr

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…some further reductions in average delay may be obtained

Time of day – e.g. 5 = 0500 – 0559

Average delayper operation in minutes/flightfrom August, 01 schedules (after slot lottery)

0

5

10

15

20

25

5 7 9 11 13 15 17 19 21 23 1 3

Actual

Leveled

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Distribution of Aircraft Size at LaGuardia

Frequency of operations• Average aircraft size

at LGA is 102 seats, or 52,000 kg MTOW, corresponding to about USD $1,600/hr in direct operating costs

• 4 aircraft-hours of delay translate to about $6,400 congestion cost per marginal operation

Aircraft seating capacity (e.g. 40 = 21 - 40 seats)

0

20

40

60

80

100

120

140

20 40 60 70 80 100 120 140 160 180 200 220 240 260