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Using Microsimulation to Recover Heterogeneity from Aggregated Data

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2015 D-STOP Symposium session by UT Austin's Doug Fearing. Watch the presentation at http://youtu.be/y2kYLM8GdbI?t=44m44s

Get symposium details: http://ctr.utexas.edu/research/d-stop/education/annual-symposium/

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Using Microsimulation to Recover Heterogeneity from Aggregated Data

  1. 1. D-STOP Symposium March 2, 2015 Douglas Fearing, UT Austin McCombs (doug.fearing@mccombs.utexas.edu) Joint work with Cynthia Barnhart, MIT, and Vikrant Vaze, Dartmouth Using Microsimulation to Recover Heterogeneity from Aggregated Data
  2. 2. 22 Passenger Delays • Depend on flight delays, flight cancelations, missed connections, and re-accommodation – Flight delays alone are not enough (Bratu & Barnhart, 2005) • Cost U.S. passengers billions of dollars per year – Exact amount unknown because data is proprietary • Multiple methodologies, cost estimates for 2007: – Air Transport Association ($5 billion), U.S. Senate Joint Economic Committee ($7.4 billion) (ignoring flight cancelations & passenger connections) – Wang, Sherry, and Donahue ($8.5 billion) (simulating passenger connections)
  3. 3. 33 Data Sources • Planned flight schedules – Flight on-time performance data • Flight seating capacities – Airline inventories, aircraft codes, monthly seat counts • Aggregate passenger demand data – Monthly segment demands, quarterly 10% coupon samples (one-way itineraries) • Heterogeneous booking data – One quarter for a major U.S. carrier
  4. 4. 44 Passenger Travel Estimation • Developed multinomial logit (discrete choice) model of itinerary shares – Regression function includes time-of-day, day-of-week, connection time, cancelations, and seats – Trained on one quarter of booking data from a large carrier • Generate potential non-stop and one-stop itineraries from flight schedule data • Use microsimulation to allocate passengers to itineraries based on estimated logit proportions – Using aggregated passenger demand data to determine total number of passengers and one-stop route proportions
  5. 5. 55 Passenger Delay Calculation • Extension of Passenger Delay Calculator developed by Bratu & Barnhart (2005) • Disrupted passengers are determined by analyzing historical flight schedule data • Passengers are re-accommodated on alternative itineraries in the order they are disrupted – Attempt re-accommodation on ticketed carrier and partner carriers first, and then consider all carriers • Maximum delay of 8 hours for daytime disruptions (5:00am - 5:00pm) / 16 hours for evening disruptions
  6. 6. 66 Annualized Costs of Delays • Estimated 245 million hours of U.S. domestic passenger delays in 2007 • Total cost of $9.2 billion – Assuming $37.60 per hour value of passenger time (same value as used in other reports) • Out of all passenger delay, – (only) 52% due to flight delays – 30% due to canceled flights – 18% due to missed connections • Average passenger delay of 30.2 minutes – Compared to average flight delay of 15.3 minutes
  7. 7. 77 Crew-related Flight Delays • FAA Office of Performance Analysis responsible for forecasting delays six months in advance – Existing simulations model delay propagation through aircraft, not passengers or crew • Crew schedules create another source of flight delays and cancelations – For example, pilots hitting work limits due to earlier delays • Data on crew schedules and constraints not publicly available
  8. 8. 88 Proposed Estimation Approach • Collect proprietary crew scheduling and recovery data to match with flight delay information – Across major carrier types (network, regional, point-to-point) • Build parameterized crew scheduling and recovery optimization models that incorporate insights from interviews and industry surveys – Allow for trade-offs between operating costs and delays • Fit optimization models against proprietary data • Apply optimization models against full data set – To understand the role that crew heterogeneity plays in overall flight delays, inform FAA simulation models

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