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
0
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
1
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
2
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 Twentyfirst 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
3
Outline
• Sensitivity to characteristics of demand and capacity
• External delay costs vs. the current cost of access
• Sample demand management systems
• Other complications
• Conclusions
4
LGA demand before and after the lottery
Scheduled operations per
hour on weekdays
100
Nov, 00
90
Aug, 01
80
75 flt/hour
70
60
50
40
30
• Scheduled
operations
reduced by
10% (from
1,348 to
1,205/day)
20
10
0
5
7
9
11
13
15
17
19
21
Time of day, e.g. 5 = 0500 - 0559
23
1
3
Capacity of 75/hr does not
include allocation of six slots
for g.a. operations
November 2000 as a representative profile prior to slot lottery at LaGuardia; August 2001 as a representative after slot lottery.
Source: Official Airline Guide
5
Small reduction in demand may lead to
dramatic reduction in delays
Minutes of delay per operation
120
Nov, 00
Aug, 01
100
80
60
• Average delay
reduced by
>80% during
evening hours
• Lottery was
critical in
improving
operating
conditions at
LGA
40
20
0
5
7
9
11
13
15
17
19
21
23
1
3
Time of day
Capacity = 75 operations/hr
6
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 minutes
Average 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%
7
Comparing Queuing Model with ASQP Data
Average departure delay at LGA (minutes/flight)
for Nov 13, 00 (VFR, light wind)
120
Actual departure
delay (majors)
100
80
Model - as
scheduled
60
Model - adjusted
for cancellations
40
20
0
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0
Time of day
Total flight operations per hour reduced by the observed cancellation rate from ASQP data
from major carriers
8
Matching Total Demand with Capacity is Key
Total delay per weekday (aircraft-hour)
1400
1200
Total delay
1000
4 pm - 7:59 pm
800
600
• Impact from
demand
leveling is
small
compared to
demand
reduction
400
200
0
Nov 00 actual
Demand leveled
Nov 00 level
(0700-2159)
Aug 01 actual
Demand reduced
Aug 01 level
• Some demand
peaks can be
allowed under
demand
management
Demand leveled
9
Outline
• Sensitivity to characteristics of demand and capacity
• External delay costs vs. the current cost of access
• Sample demand management systems
• Other complications
• Conclusions
10
Marginal delay cost due to an additional
operation
Marginal delay caused by an additional aircraft (aircrafthours)
18
Nov, 00
16
Aug, 01
• Runway at
LGA
virtually
“saturated”
prior to slot
lottery
• Delays
propagate
throughout
the day
14
12
10
8
6
4
2
0
5
7
9
11
13
15
17
19
21
23
1
3
Time of day
Capacity = 75 per hour
11
Marginal delay cost dwarfs landing fee at LGA,
even after lottery
$
9000
Marginal
delay cost
Actual
charge
8000
7000
6000
5000
4000
3000
2000
1000
0
5
7
9
11
13
15
17
19
21
23
1
3
Time of day – e.g. 5 = 0500 – 0559
12
External delay cost caused by an additional operation
Marginal delay caused by an additional flight
operation at four airports (aircraft-hour)
16
14
LaGuardia, Nov 00,
at 75 ops/hr
12
LaGuardia, Feb 01,
at 75 ops/hr
10
Boston, summer 98,
at 115 ops/hr
8
Austin, Nov 00, at
54 ops/hr
6
Peak-hour external
delay costs:
•
LGA: Feb 01
~$6,000 (most
of the day)
•
BOS: ~$2,500
(16:00-21:00)
•
AUS: ~$0
4
2
0
5
7
9
11
13
15
17
19
21
23
1
3
Hour of the day (during which an extra operation is added)
13
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
14
Hub demand - Atlanta
Total scheduled 70
movements per
60
15-minute
intervals
(November, 2000) 50
Arrivals
Departures
40
Approx
optimum
capacity
30
20
10
0
5
7
9
11
13
15
17
19
21
23
Time of day – e.g. 5 = 0500 – 0559
Source: FAA Airport Benchmark Report, 2001, Official Airline Guide
1515
Non-hub demand- LaGuardia
Total scheduled
movements per
60-minute
intervals
(November, 2000)
100
Arrival
80
Departure
60
At 75
movements/hr
of capacity
40
20
0
5
7
9
11 13 15 17 19 21 23
1
3
Time of day – e.g. 5 = 0500 – 0559
Source: Official Airline Guide
Note: 75 flights/hr excludes allocation for general aviation
1616
Important to note…
• The external costs computed, in the absence
of congestion pricing, give only an upper
bound on the magnitude of the congestionbased 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
17
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
systems
can 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 “depeaking” of daily demand profiles are typically more
modest
• Adequate quantitative methods are available
18
Outline
• Sensitivity to characteristics of demand and capacity
• External delay costs vs. the current cost of access
• Sample demand management systems
• Other complications
• Conclusions
19
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
20
Example: Congestion Pricing, Option B
•
•
•
•
•
1.
2.
3.
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:
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)
Subject to congestion fee A: all other movements formerly
qualifying under AIR-21; general aviation. (A ~ $350-700)
Subject to congestion fee B: all other operations (B ~ $7002,000)
21
Example: Auctions, Option A
•
•
•
1.
2.
3.
4.
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:
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
Small hub and non-hub slots: 5 movements per hour; assigned by
lottery (or possibly through auction or administrative procedure)
“Performance based” slots: 70 percent of remaining slots;
allocated among airlines based on their market share of total
revenue pax at LGA
Auctioned slots: remaining slots are auctioned
22
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
23
Outline
• Sensitivity to characteristics of demand and capacity
• External delay costs vs. the current cost of access
• Sample demand management systems
• Other complications
• Conclusions
24
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 tradeoffs 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
25
BOS: Annual Capacity Coverage Chart
(assumes 50 % arrivals and 50 % departures)
Movements per
hour
120
80
40
0
% of time
20
40
60
80
100
26
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….”
27
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”
28
Outline
• Sensitivity to characteristics of demand and capacity
• External delay costs vs. the current cost of access
• Sample demand management systems
• Other complications
• Conclusions
29
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
30
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 marketbased measures
31
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 distributions
Inputs:
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, 2t, 3t, … 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.)
32
Upon “leveling” temporal distribution of
demand…
Total scheduled
movements per
60-minute interval
(August, 2001,
after slot lottery)
90
Actual
Leveled
75 flts/hr
80
70
60
50
40
30
20
10
0
5
7
9
11
13
15
17
19
21
23
1
3
Time of day – e.g. 5 = 0500 – 0559
33
…some further reductions in average delay may
be obtained
Average delay
per operation in
minutes/flight
from August, 01
schedules (after
slot lottery)
25
Actual
Leveled
20
15
10
5
0
5
7
9
11
13
15
17
19
21
23
1
3
Time of day – e.g. 5 = 0500 – 0559
34
Distribution of Aircraft Size at LaGuardia
Frequency of operations
140
•
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
120
100
80
60
40
20
0
20
40
60
70
80 100 120 140 160 180 200 220 240 260
Aircraft seating capacity (e.g. 40 = 21 - 40 seats)
35