Air Transportation Network Load Balancing using Auction-Based Slot Allocation for Demand Management George L. Donohue Loan Le and C-H. Chen Air Transportation Systems Engineering Laboratory Dept. of Systems Engineering & Operations Research George Mason University Fairfax, VA Harvard University 18 March, 2004

Outline  Necessity of Demand Management  History of US Demand Management  Auction model for airport arrival slots  auctioneer optimization model  airline optimization model  Atlanta airport case study  simulated scenarios  results and interpretation  Observations

Why Demand Management? Atlanta Airport - FAA Airport Capacity Benchmark 2001  Over-scheduling causes delay and potentially compromises safety

Data Indicates Loss of Separation Increases at High Capacity Fraction Statistics at ATL, BWI, DCA and LGA airports (Haynie)  Over-scheduling causes accident pre-cursor events and potentially compromises safety

Observed WV Separation Violations vs. Capacity Ratio Haynie, GMU 2002

Atlanta Airport - FAA Airport Capacity Benchmark 2001 Flight Banking at Fortress Hubs Creates Inefficient Runway Utilization  Over-scheduling causes accident pre-cursor events and potentially compromises safety  Under-scheduling wastes runway capacity

Enplanement Capacity is More Important than Operational Capacity  Small aircraft make inefficient use of runway capacity seats/aircraft Cumulative Seat Share vs. Cumulative Flight Share and Aircraft Size ATL total operations (OAG Summer 2000) Cumulative flight share Cumulative seat share

Excess Market Concentration May Lead to Inefficient Use of Scare Resources HHI is a Metric used to Measure Market Concentration  Hirschman-Herfindahl Index (HHI) is standard measure of market concentration  Department of Justice uses to measure the competition within a market place  HHI=  (100*s i ) 2 with s i is market share of airline i  Ranging between 100 (perfect competitiveness) and (perfect monopoly)  In a market place with an index over 1800, the market begins to demonstrate a lack of competition

History of US Demand Management - Limited #IFR slots during specific time periods - Negotiation-based allocation 1968 High-Density- Rule Apr 2000 Exempted from HDR certain flights to address competition and small market access AIR-21 Jan 2001 Cap of the #exemption slots Lottery 1978 Deregulation Use-it-or- lose-it rule based on 80% usage 1985 Slot ownership 2007 End of HDR. What’s next? -Congestion pricing? -Auction? LGA Airport Slot Control

Demand Management Approaches  Administrative  negotiation-based IATA biannual conferences  Economic  weight-based landing fee: no incentive for large aircraft – inefficient Enplanement capacity  time-based congestion pricing: not reveal the true value of scarce resources Market-based Auctions  DoT supervised Market-based Auctions of Arrival Metering-Fix Time Slots  Hybrid

Auction Model Design Issues  Feasibility  package slot allocation for arrival and/or departure slots  politically acceptable prices  Optimality  efficiency: throughput (enplanement opportunity) and delay  regulatory standards: safety, flight priorities  equity:  stability in schedule  airlines’ need to leverage investments  airlines’ competitiveness : new-entrants vs. incumbents  Flexibility  primary market at strategic level  secondary market at tactical level

Design Approach  Objective: Network Load Balancing  Obtain Better Utilization of Nation’s Airport Network Infrastructure – Network Load Balancing Optimum FleetSafe Arrival Capacity  Provide an Optimum Fleet Mix at Safe Arrival Capacity Fair Market Access  Ensure Fair Market Access Opportunity Schedule Predictability  Increase Schedule Predictability - reduced queuing delays  Assumptions  Airlines will make optimum use of slots they license  Auction rules: Bidders are ranked using a linear combination of:  monetary offer (combination of A/C equipage credit and cash)  flight OD pair (e.g. international agreements, etc.)  throughput (aircraft size) ?  airline’s prior investment ?  on-time performance ?

Strategic Auction Analytical Approach NAS Auction Model Schedules Analysis & Feedback Bids Slots Airlines Auctioneers Network Model -Auctions only at Capacitated Airports -Auction Licenses good for 5 to 10 years

Auction Model Process More bids than capacity Call for bids End auction process Submit information and bids Yes No Auctioneer’s action Airline’s action Simultaneous bidding of 15-min intervals Determine factor weights, initial bids and increments Sort the bids in decreasing ranks Sequence flights for each intervals Local optimum fleet mix order: small  large  757  heavy

ARR DEP X= (x 1 … x j … x n ) T xj=xj= 1 if P j wins a round 0 otherwise money #seats … Bid vector P j = Weight vector W = (w 1 w 2 ) T Rank of a bid vector : W·P j C = (W T ·P 1 … W T ·P j … W T ·P n ) T LP : max z = C T X s.t. (ARR·X) i, (DEP·X) i lies within the Pareto frontier i airlines’ combinatorial constraints Auctioneer Model

Capacity constraints for 15-min bins: (ARR·X) i  25 (DEP·X) i  25 max z = C T X s.t. A  X  b airlines combinatorial constraints A = ARR DEP, b = 25 Let: Atlanta’s VMC Auction Model departure per hour arrival per hour 100,100 ATL’s VMC capacity (April 2000)

Airline Bidding Model  Determine markets, legs, frequencies and departure times  Fleet assignment :  (aircraft type,leg)  line-of-flying (LOF): sequence of legs to be flown by an aircraft in the course of its day Bidding is all about scheduling 1,5 2,6 3,7 4,8 1,3 2,4 B C D A E F

 Determine markets, legs, frequencies and departure times  Fleet assignment :  (aircraft type,leg)  line-of-flying (LOF): sequence of legs to be flown by an aircraft in the course of its day time ARR DEP simple package bidding Bidding is all about scheduling Simple Flight Schedule Example 1,5 2,6 3,7 4,8 1,3 2,4 B C D A E F Daily arrivals and departures at A of one LOF:

 Determine markets, legs, frequencies and departure times  Fleet assignment :  (aircraft type,leg)  line-of-flying (LOF): sequence of legs to be flown by an aircraft in the course of its day time ARR DEP complex package bidding Bidding is all about scheduling Schedule Banking Constraints 1,5 2,6 3,7 4,8 1,3 2,4 B C D A E F Daily arrivals and departures at A of one LOF:

Assume the Airlines have a Near Optimal Schedule and Try to Maintain in Auction  Airlines’ elasticity for changing schedule original scheduled 15-min interval 15min bids withdrawn  Airlines bid reasonably and homogeneously by setting an upper bid threshold proportional to #seats (revenue)  No fleet mix change

Airline Agent Tries to Maximize Profit Objective function: Maximize revenue and ultimately maximize profit Maximise Subject to: Airlines’ package bidding constraints To bid or not to bid Upper bound for bids Lower bound for bids M big positive value y s binary value B o T airport threshold vector  airline threshold fraction B s ’ old bid for slot s in previous round if airline bids for slot s otherwise {B s } set of monetary bids {P s } airline expected profit by using a slot Variables:

Network Model used to Evaluate Auction Effectiveness LGA ORD MSP DTW ATL DFW LAX IAD BWI DEN PHX SFO 11-node network departure separation arrival separation Runway capacity determined by  Wake Vortex Separation Standards (nmiles/seconds) (M. Hanson)  and a scale factor to account for runway dependency

Simulation scenarios  Assumptions:  Aircraft can arrive within allocated slots with Required Time-of- Arrival errors of 20 seconds (using Aircraft RTA Capabilities)  Auction items: Metering Fix Arrival Slots  No combinatorial package bidding  Bid values and minimum increments are relative to the value of initial bid  Input:  Summer 2000 OAG schedule of arrivals to ATL (1160 flights)  Scenario 1 (Baseline):  OAG schedule  Scenario 2 (Simple auction):  Monetary Offer is the only determining factor  Auction-produced schedule

Traffic levels and estimated queuing delays during VMC 45 min maximum schedule deviation allowed, no flights are rerouted Scheduled arrivals (#operations/quarter hour) Estimated Average Runway Queuing Delay (min) ATL reported optimum rate

Results : Flight Deviations min 15-min max allowed 30-min max allowed45-min max allowed  Bell-shaped curves are consistent to the model assumption about airline bidding behavior  Curves are skewed to the right due to optimum sequencing that shifts aircraft toward the end of 15-min intervals ~70%

Results : Auction metrics #Flights to be rerouted #Seats to be rerouted #Rounds Average Auction Revenue Per Flight (x $Initial Bid) Maximum schedule deviation allowed (min) 23 Average cancelled arrivals in summer 2000: 23 #seats of rerouted flights

Observations on Research to Date  Simple Auctions could Exclude small airlines and/or small markets from Hub Airports  Simple Bidding Rules can Prevent this Problem  Number of flights to be rerouted is comparable to the number of cancelled flights  Combinatorial Clock Auctions Offer a Promising Market-Based approach to Demand Management  Auction Proceeds could be used as Incentives to the Airports for Infrastructure Investments and to the Airlines for Avionics Investments

Airlines Could bid with Avionics Investment Promissory Notes  Increased Hub airport capacity is Dependent on Aircraft being able to maintain Accurate Time- Based Separation (ROT and WV safety constraints)  Data Links, ADS-B, FMS-RTA and New Operational Procedures will be required  Airlines could Bid with Script that constituted a contract to equip their Aircraft with-in X years (i.e. ½ bid price)  Cash Bids could be used to replace PFC’s and go directly to the Capacitated Airport’s Infrastructure Investment Accounts

Future work  More airline and airport inputs  Experimental auction Participation  Include Efficiency Rules  Include combinatorial bidding  Include pricing  Conduct experimental auctions

 Backup

Observed Runway Incursions One formal simultaneous runway occupancy Several “near” simultaneous runway occupancies Out of 364 valid data points -14 sec

ATL and LGA Aircraft Inter-arrival Times

LGA Arrival Histograms Normalized by Arrival Rate Displaying Positive or Negative Deviation from WVSS Adherence Perfect WVSS Adherence = 0

ATL Arrival Histograms RW 27 Normalized by Arrival Rate Displaying Positive or Negative Deviation from WVSS Adherence Perfect WVSS Adherence Value = 0

ATL Arrival Histograms RW 26 Normalized by Arrival Rate Displaying Positive or Negative Deviation from WVSS Adherence Perfect WVSS Adherence Value = 0

Aircraft Wake Vortex Separation Violations : LGA & BWI Perfect WVSS Adherence Value = 0

FAA Barriers to Change  FAA has an Operational and Regulatory Culture  Inclination to follow training that has seemed to be Safe in the Past  FAR has NOT Changed to Provide Operational Benefits from Introduction of New Technology  Assumption that Aircraft Equipage would be Benefits Driven did not account for Lack of an ECONOMIC and/or SAFETY Bootstrapping Requirement

FAA Investment Analysis Primarily focus on Capacity and Delay  OMB requirement to have a B/C ratio > 1 leads to a modernization emphasis on Decreasing Delay  In an Asynchronous Transportation Network operating near it’s capacity margin, Delay is Inevitable  Delay Costs Airlines Money and is an Annoyance to Passengers BUT  is Usually Politically and Socially Acceptable

Safety Concerns Hypothesis: Most Major Changes to the NAS have been due to Safety Concerns  1960’s Mandated Introduction of Radar Separation  1970’s Decrease in Oceanic Separation Standards Required a Landmark Safety Analysis  1970’s Required A/C Transponder Equipage  1970’s Required A/C Ground Proximity Equipage  1990’s Required A/C TCAS Equipage  1990’s Required A/C Enhanced Ground Prox. Equipage  1990’s TDWR & ITWS Introduction  1990’s Mandated Development of GPS/WAAS