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Analysis of Demand Uncertainty in Ground Delay Programs

Analysis of Demand Uncertainty in Ground Delay Programs. Mike Ball University of Maryland. Uncertainty in Arrival Stream. flights. slots w multiple flights. destination airport. airborne queue. open slots. Sources of uncertainty: cancellations, pop-ups, drift. Planned vs Actual

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Analysis of Demand Uncertainty in Ground Delay Programs

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  1. Analysis of Demand Uncertainty in Ground Delay Programs Mike Ball University of Maryland

  2. Uncertainty in Arrival Stream flights slots w multiple flights destination airport airborne queue open slots Sources of uncertainty: cancellations, pop-ups, drift

  3. Planned vs Actual Airport Acceptance Rate AAR: airport acceptance rate – rate at which flights land at airport flights Planned AAR (PAAR) queue destination airport In order to fully utilize arrival capacity, a queue must be maintained “to keep the pressure on the airport” This is done by regulating the value of the PAAR Actual AAR

  4. Models • Integer Program • considers only flight cancellations • variables: PAAR in each time period, Pr{k flights in airborne queue at end of period i} • obj fcn: min total exp airborne delay • inputs: flight cancellation prob, overall slot utilization, AAR • Simulation • Considers cancellations, pop-ups, drift

  5. Airborne Delay vsCancellation Prob (IP Model)

  6. “Marginal” Effects of Uncertainty in Presence of Cancellations, Pop-ups, Drift (Sim Model)

  7. “Marginal” Effects of Uncertainty in Presence of Cancellations, Pop-ups Drift (Sim Model)

  8. Prelude to Understanding Compression Impact: Delay vs Arrival Times scheduleddeparture times departure times arrival times airborne delay ground delay Delay(f) = arr_time(f) – sched_arr_time(f) Delay(f) = arr_time(f) – sched_dep_time(f) – dir_en_route_time(f) Tot_delay = arr_time(f) – sched_dep_time(f) – dir_en_route_time(f) • As long as the overall set of arrival times (arrival slots used) remains the same total delay remains the same

  9. Compression Benefits Compression has filled in holes in the PAAR which has reduced the amount of assigned ground delay Possible system effects: • Holes in AAR have been filled in and total delay has been reduced • There were no holes in AAR so reduced ground delay has been replaced by airborne delay • Uncertainty in the arrival stream has been reduced enabling a reduction in the PAAR required to achieve a given AAR and a reduction in overall airborne delay Of course, each individual airline generally derives substantial benefits based on internal cost reductions.

  10. Analysis • Looked at PAAR vs Actual AAR (arrivals) for 1st program hr, 2nd program hr, etc. for three airports; tried to group like GDPs, e.g. only morning GDPs at SFO • ATL and BOS: used command center logs for PAAR and AAR • SFO: used command center logs for PAAR, tower counts for AAR. • Important Questions: • Is PAAR or AAR increasing or decreasing (improvement = increase in AAR) • Is deviation of PAAR from AAR changing (improvement: PAAR and AAR closer together)

  11. SFO PAAR Actual AAR

  12. PAAR ATL Actual AAR

  13. PAAR BOS Actual AAR

  14. Typical PAARs Used Today PAAR 35 34 33 32 31 30 29 28 27 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Time Period (hour)

  15. Sample PAARs Recommended by IP Model PAAR 35 34 33 32 31 30 29 28 27 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Time Period (hour)

  16. Conclusions • Reducing uncertainty is extremely important • Pop-ups • Cancellations w/o notification • Wide departure windows (may be most important) • No evidence found (yet) that improved prediction accuracy of CDM has been transformed into actual uncertainty reduction benefits (more analysis necessary) • Command Center (and FSM) should consider change in manner of setting AARs

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