Research Thrust: Airport and Airline Systems Project: Implications of Congestion for the Configuration of Airport Networks and Airline Networks (AirNets) Duration: (November 2007 December 2010) Description: Most ongoing work on the implications of congestion for the configuration of airport networks and airline networks relies on qualitative approaches that posit sets of assumptions about future traffic conditions and then discuss the implications of these assumptions for the various air transportation stakeholders. By contrast, the objectives of the proposed project will be pursued through quantitative analyses that will utilize a new network model of two regional airport systems one E.U.-wide and the other U.S.-wide that combine mathematical optimization with a stochastic and dynamic queuing theory approach. Using these network models, the impacts of distribution of traffic among alternative types of airports and the incidence of delays on airlines and on passengers will be explored. Objectives/Deliverables: The project is organized around the following specific objectives: * Developing airport and airline network models incorporating access costs (landing fees, passenger taxes, and environmental charges) and delay costs, as well as constraints related to new air transportation policies and technologies * Developing a stochastic and dynamic queuing model to compute detailed delay profiles and delay costs * Applying the models to the E.U. and U.S. air transportation systems Industry Involvement: Collaboration with industry, particularly airlines, will be sought for the assembly of input data and formulation of scenarios. PT Faculty: Rosário Macário (IST), António Antunes (Coimbra) MIT Faculty: Cynthia Barnhart, Amedeo Odoni Financial Resources: TBD
May 2008 Status Report on AirNets Project Amedeo Odoni MIT Objectives New network models of two regional airport systems one E.U.-wide and the other U.S.- wide that employ mathematical optimization and/or a stochastic and dynamic queuing theory approach. Using these network models, explore the impacts on distribution of traffic among alternative types of airports and the incidence of delays on airlines and on passengers. Page 2 1
Operational Objectives Development of airport and airline network models incorporating access costs (landing fees, passenger taxes, and environmental charges) and delay costs, as well as constraints related to new air transportation policies and technologies. Development of a stochastic and dynamic queuing model to compute detailed delay profiles and delay costs. Application of the models to the E.U. and U.S. air transportation systems Page 3 Project Structure by Work Package WP Relation to Operational Objectives Description of the Work Duration (months) Start Month End Month 0 All Detailed specification of operational objectives and expected outcomes for the different work packages 2 Nov. 01, 2007 Dec. 31, 2007 1 Development of airport and airline network models Conceptual design of the airport and airline network models 6 Jan. 01, 2008 June 30, 2008 2 Development of stochastic and dynamic queuing model Formulation and testing of the stochastic and dynamic queuing model 18 Jan. 01, 2008 June 30, 2009 3 Application of the models to the E.U. and U.S. Assembly and analysis of relevant data for the E.U. and U.S. air transportation systems 30 July 01, 2008 June 30, 2010 4 Development of airport and airline network models Formulation and testing of the airport and airline network models 18 July 01, 2008 Dec. 31, 2009 5 Application of the models to the E.U. and U.S. Development of scenarios regarding the evolution of air transportation systems 18 Jan 01, 2009 June 30, 2010 6 Application of the models to the E.U. and U.S. Application of the models to the E.U. and U.S. air transportation systems 12 Jan. 01, 2010 Dec. 31, 2010 Page 4 2
Summary of Ongoing Activities WP1: Development of airport and airline network models: conceptual design of the airport and airline network models. Leader: U. of Coimbra; support: MIT, FEUP, IST. WP2: Development of stochastic and dynamic queuing model: formulation and testing of the stochastic and dynamic queuing model. Leader: MIT. WP3: Application of the models to the E.U. and U.S.: assembly and analysis of relevant data for the E.U. and U.S. air transportation systems. Leader: FEUP; support: IST. WP5: Application of the models to the E.U. and U.S.: development of scenarios regarding the evolution of air transportation systems. Leader: IST; support: MIT, FEUP. Page 5 Steps Completed to Date on WP2 1. Re-programmed DELAYS model in Java; tested extensively the model and verified it is performing correctly. 2. Obtained data on demand (every arrival and departure during a 24-hour period) at 35 busiest airports in United States. 3. Obtained data on most common daily capacity profiles at several major airports in the United States, with associated probabilities. All 35 busiest airports are available. 4. Tested DELAYS model extensively at several airports. 5. Programmed the Approximate Network Delays (AND) model (delay propagation in a network of airports) in Java. 6. Verified availability of data on aircraft itineraries from NASA (necessary to operate AND model). 7. Currently de-bugging and testing AND in a 3-airport test case involving Chicago O Hare (ORD), New York LaGuardia (LGA) and Boston Logan (BOS) airports. Overall Assessment: Significantly ahead of schedule! Page 6 3
Major Future Challenges Availability of European data (individual airport schedules, aircraft itineraries). Operational Objective 1: Development of airport and airline network models incorporating access costs (landing fees, passenger taxes, and environmental charges) and delay costs, as well as constraints related to new air transportation policies and technologies. Operational Objective 2: Ensuring efficient performance of AND model as the number of airports included in the network increases. Page 7 A macroscopic, stochastic and dynamic single airport model: DELAYS 4
Modeling Dynamic Queuing Systems The behavior of a class of dynamic queuing systems over time can be computed through the numerical solution of a set of first-order ordinary differential equations, the Chapman-Kolmogorov equations A particularly powerful model is the one with: demands which are Poisson with time-varying rates service times which are k-th order Erlang with timevarying service rates This model (the M(t)/E k (t)/n model) is important because: its numerical solution can be obtained efficiently it approximates well most M(t)/G(t)/n systems The MIT DELAYS model does precisely that: it approximates M(t)/G(t)/n systems Page 9 Model of Each Airport Each airport is viewed as a queuing system with capacity equal to that of the runway system: modeled through DELAYS Aircraft requesting permission to land or take off are the demands The times of demands for arrivals and departures are modeled as time-varying Poisson processes Service times are modeled as k-th order Erlang; k is determined by ratio of σ S to E[S] Queuing discipline is FCFS Infinite waiting line capacity Page 10 5
The DELAYS Model Approximates, with high precision, the M(t)/E k (t)/n queue (and (by implication also approximates M(t)/G(t)/n systems) Inputs: Dynamic demand profile (typically specified via hourly demand rates); dynamic capacity profile (typically hourly capacity) Approach: Starting with initial conditions at time t=0, solves equations describing the evolution of queues by computing the probabilities, P n (t), of having n= 0, 1, 2, 3, aircraft in queue at times t = Δt, 2Δt, 3Δt,... up to end of the time period of interest (typically 24 hours) Outputs: Statistics about queues (average queue length, average waiting time, fraction of flights delayed more than X minutes, etc.) Page 11 The DELAYS model [2] Analytical; approximate; single airport Requires few data (demand profile, capacity profile, estimate of variance of service times) Time-horizon can be subdivided into intervals as small as 10 minutes Demand may exceed capacity during any number of intervals (no ρ < 1 restriction) Very fast and easy-to-use: updated to java in Fall 2007 and Winter 2008;, less than 1 sec for estimation of all P n (t) for a 24-hour period at a major airport Especially useful for parametric studies and sensitivity analyses Page 12 6
A macroscopic, stochastic and dynamic model of a network of airports: The Approximate Network Delays Model (AND) Outline of AND Models US (national scale) or EU (continent scale) airport system as a dynamic and stochastic queuing network Each individual airport is viewed as an individual queuing system; uses a decomposition approach to analyze delays at each airport separately Uses a delay-propagation and demand updating algorithm to capture the interactions between each individual airport and all other airports in network Initial conceptual design due to Malone and Odoni (1996) Model is being developed ab initio in java, incorporates major improvements over initial concept and is designed to accommodate data structure and massive database associated with aircraft itineraries Page 14 7
A Three-Airport Network BOS Airport X LGA ORD Airport X represents all the external airports ; it acts as an un-capacitated source and sink of traffic Page 15 The Iterative Logic of AND Calculates expected delay on landing and takeoff Expected delay by time of day Analytical queuing engine (DELAYS) Determines if significant delay occurs Processes flights Adjusts arrival and departure times Updates hourly demand rates Updated hourly airport demand rates Delay Propagation Algorithm Page 16 8
AND Data Requirements Detailed demand data (schedule of arrivals and of departures) for entire day for every airport Detailed capacity data (number of arrivals and of departures that can be accommodated per hour or other unit of time) for every airport Preferably capacity data will be provided for good and bad weather conditions, with associated probabilities Detailed aircraft itineraries: routing and schedule of every aircraft flying through the system Page 17 Examples of Ongoing Work on DELAYS and AND 9
Data on Individual Flight Legs 801075367, DAL, DAL1847, B738, KBOS, 2002-05-17 00:04:00 UTC, 2002-05-17 00:16:42 UTC, 2002-05-17 00:56:31 UTC, 2002-05-17 01:02:13 UTC, KLGA Field 1: Tail-number Field 2: Airline/carrier code Field 3: Flight number Field 4: Aircraft type abbreviation Field 5: Departure airport code Field 6: Gate departure day and time in UTC time zone Field 7: Takeoff/wheels-off day and time in UTC time zone Field 8: Landing/wheels-down day and time in UTC time zone Field 9: Arrival day and time in UTC time zone Field 10: Arrival airport code Page 19 ATL Capacity Scenarios Page 20 10
ATL: Summary of DELAYS results Scenario Total demand (aircraft) Total capacity Ave. delay (mins) Ave. delay in peak hour (mins) Total delay (mins) ATL1 1129 1565 4.00 17.34 4514 ATL2 1129 1659 2.98 15.16 3361 ATL3 1129 1470 6.39 27.19 7218 ATL4 1129 1437 6.88 21.23 7775 ATL5 1129 1284 16.70 40.99 18861 Page 21 ATL1: Prob y 0.263 (96 days) 45 40 35 30 25 20 15 10 5 0 7:00 7:30 8:00 8:30 9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 19:30 20:00 20:30 21:00 21:30 22:00 22:30 23:00 23:30 Series1 Series2 Series3 Expected number of aircraft with delays of more than... 5 minutes : 520.14 10 minutes : 284.35 15 minutes : 158.54 20 minutes : 87.45 Page 22 11
ATL2: Prob y 0.255 (93 days) 45 40 35 30 25 20 15 10 5 0 7:00 7:30 8:00 8:30 9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 19:30 20:00 20:30 21:00 21:30 22:00 22:30 23:00 23:30 Series1 Series2 Series3 Expected number of aircraft with delays of more than... 5 minutes : 373.38 10 minutes : 182.59 15 minutes : 93.03 20 minutes : 40.03 Page 23 ATL3: Prob y 0.208 (76 days) 45 40 35 30 25 20 15 10 5 0 Series1 Series2 Series3 7:00 7:30 8:00 8:30 9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 19:30 20:00 20:30 21:00 21:30 22:00 22:30 23:00 23:30 Expected number of aircraft with delays of more than... 5 minutes : 676.31 10 minutes : 413.25 15 minutes : 243.25 20 minutes : 141.95 Page 24 12
ATL4: Prob y 0.175 (64 days) 45 40 35 30 25 20 15 10 5 0 7:00 7:30 8:00 8:30 9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 19:30 20:00 20:30 21:00 21:30 22:00 22:30 23:00 23:30 Series1 Series2 Series3 Expected number of aircraft with delays of more than... 5 minutes : 705.32 10 minutes : 484.88 15 minutes : 319.22 20 minutes : 221.88 Page 25 ATL5: Prob y 0.099 (36 days) 45 40 35 30 25 20 15 10 5 0 7:00 7:30 8:00 8:30 9:00 9:30 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 19:30 20:00 20:30 21:00 21:30 22:00 22:30 23:00 23:30 Series1 Series2 Series3 Expected number of aircraft with delays of more than... 5 minutes : 1023.24 10 minutes : 845.42 15 minutes : 683.07 20 minutes : 563.88 Page 26 13
Aircraft Itineraries Page 27 14