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, 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.) 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