Lessons* from Airport Gridlock: LaGuardia Airport (*for Demand Management)

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

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

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

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

Outline • Sensitivity to characteristics of demand and capacity • External delay costs vs. the current cost of access • Sample demand management systems • Other complications • Conclusions

LGA demand before and after the lottery Scheduled operations per hour on weekdays • Scheduled operations reduced by 10% (from 1,348 to 1,205/day) Capacity of 75/hr does not include allocation of six slots for g.a. operations Time of day, e.g. 5 = 0500 - 0559 November 2000 as a representative profile prior to slot lottery at LaGuardia; August 2001 as a representative after slot lottery. Source: Official Airline Guide

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 Time of day Capacity = 75 operations/hr

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%

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

Matching Total Demand with Capacity is Key Total delay per weekday (aircraft-hour) • Impact from demand leveling is small compared to demand reduction • Some demand peaks can be allowed under demand management Demand leveled Demand reduced Demand leveled

Marginal delay cost due to an additional operation Marginal delay caused by an additional aircraft (aircraft-hours) • Runway at LGA virtually “saturated” prior to slot lottery • Delays propagate throughout the day Time of day Capacity = 75 per hour

Marginal delay cost dwarfs landing fee at LGA, even after lottery $ Time of day – e.g. 5 = 0500 – 0559

External delay cost caused by an additional operation Marginal delay caused by an additional flight operation at four airports (aircraft-hour) • LGA: Feb 01 ~$6,000 (most of the day) • BOS: ~$2,500 (16:00-21:00) • AUS: ~$0 Peak-hour external delay costs: Hour of the day (during which an extra operation is added)

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

Hub demand - Atlanta Total scheduled movements per 15-minute intervals (November, 2000) Time of day – e.g. 5 = 0500 – 0559 Source: FAA Airport Benchmark Report, 2001, Official Airline Guide 15

Non-hub demand- LaGuardia Total scheduled movements per 60-minute intervals (November, 2000) Time of day – e.g. 5 = 0500 – 0559 Source: Official Airline Guide Note: 75 flights/hr excludes allocation for general aviation 16

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

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 “de-peaking” of daily demand profiles are typically more modest • Adequate quantitative methods are available

Proposed Demand Management Alternatives • Three types of demand management strategies were put forward in June 2001: • Congestion pricing: PANYNJ (two options) • Auctions: PANYNJ (two options) • 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

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: • 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 ~ $700-2,000)

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: • 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

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

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

40 80 120 0 100 80 60 40 20 BOS: Annual Capacity Coverage Chart(assumes 50 % arrivals and 50 % departures) Movements per hour % of time

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….”

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”

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

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

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.)

Upon “leveling” temporal distribution of demand… Total scheduled movements per 60-minute interval (August, 2001, after slot lottery) Time of day – e.g. 5 = 0500 – 0559

…some further reductions in average delay may be obtained Average delay per operation in minutes/flight from August, 01 schedules (after slot lottery) Time of day – e.g. 5 = 0500 – 0559

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)

Lessons* from Airport Gridlock: LaGuardia Airport (*for Demand Management)