LMINET2: An Enhanced LMINET

1. LMINET2: An Enhanced LMINET Dou Long, Shahab Hasan dlong@lmi.org December 10, 2008

2. Old LMINET • Refers to a suite of models • Separate commercial and GA traffic schedule forecast models • NAS-wide operations/delay model • NAS-wide projected throughput schedule construction model • Utilities to process OAG, ETMS, ASQP data sources • Flight delay estimation, at 110 large airports • Based on the solution of dynamic queuing equations at 110 airports from demands and capacities • Outputs queuing delays (akin to measuring against unimpeded) • Constrained flight schedule construction, all flight • Can use delay tolerance-based rules or demand/capacity ratio-based rules for airport capacity constraints • Uses sector loading for en route constraints • Runs assuming universally good weather conditions across NAS to reflect general airline scheduling practice

3. Motivation for LMINET2 • Although LMINET is capable of estimating flight delays in any weather conditions, its modeling of NAS operations in bad weather can be improved. We need to have new capabilities to reflect the disruption of flight schedules: • Delay propagation • Flight cancellation (not the same as flight trimming) • Active air traffic management (ATM) measures such as the Ground Delay Program (GDP) • Some ATM technologies/strategies are designed to help the traffic operations in both good and bad weather, or in bad weather only

4. Triad of Models and Entities in LMINET2 • The airport queuing delay model is replaced by a triad of models: demand, flight, aircraft Flight Arrival Delay Queuing Delay Aircraft Connection and Turnaround Model Flight Schedule Delay & Cancellation Model Airport Queuing Delay Model Airport Demand Flight Departure Delay Demand Aggregate number of flights For queuing delay calculations Flight Scheduled service (O&D, time, equipment) Schedule delays depends on queuing delay, schedule pad, and schedule delay Aircraft For flight connection and delay propagation

5. Modeling Flight Delays at Arrival Gateand Schedule Pad Estimation • Flight schedule delay (gate arrival delay) • Block Delay = Taxi-out Delay + En Route Delay + Taxi-in Delay – Schedule pad, or • Arrival Gate Delay = Departure gate delay + Taxi-out Delay + En Route Delay + Taxi-in Delay – Schedule pad • Schedule pad estimation En Route (1.27) Block (-2.19) Taxi-in (1.71) Arrival Gate (7.55) Dept Gate (10.98) Taxi-out (4.68) • Pad is 9.85 min (1st formula), or 11.09 min (2nd formula) • Their difference is caused by the rounding error in the reported data base • Data source: 2005 ASPM, all flights including negative delays

6. Modeling Flight Connection • Flight connection model is needed for delay propagation and flight cancellation modules • Only the flights with the same seat size categories can be connected • The carrier flag is ignored because the model is envisioned to be used mostly for studies of future traffic when the carrier is the hardest to predict in a flight schedule • Window of flight connection construction • Estimated by the scheduled arrival and departure times (not the real operation times) • Data sources: ASQP (tail #), and OAG (seat size) of June 2005

7. Delay Propagation Model andIts Validation • Model for the departure gate delay (against schedule) • = max(arrival delay + minimum ground turn time – scheduled ground turn time, 0) + adjustment factor caused by other reasons • Y = α + βx • α =7.64 , β =0.966, R2 = 0.9368 • α =0, β=1.05, R2 = 0.9748

8. Operational Flight Cancellation Module • The following flights are cancelled • Due to congestion based on the queuing delay • Due to schedule delay • Due to connectivity • The next leg, if it exists, of a cancelled flight is also cancelled • Flights can also be cancelled by the Ground Delay Program logic (discussed on next slides)

9. GDP Logic • A proactive ATM program to reduce flight delay and congestion when the capacity of the destination airport is reduced due to weather by holding flights at their departure airports • While running the normal delay model, concurrently check the future capacity/demand imbalance at each of the 310 airports starting 2 hours ahead till the end of day • The acceptance rate at the destination airport can be taken from an input file, or can be generated based on the weather condition • If not departed, the departure times of the arriving flights are delayed to the next epoch. FIFO scheme used for multi-epoch delay. • Cancel flights if they are expected to experience extreme delays

10. Expanded Airport Coverage in LMINET2 • Airports with queuing delays: 310 • 110 with FAA capacity models • 200 LMI-developed models • Schedule delays: all commercial airports, ~ 450 • All airports: contribute demand to the 310 airports and to their delays Air Taxi Air Carrier

11. Model Parameter Calibration • Default setting based on the system averages • Airport specific tuning at a small set of airports • Schedule delay pad at the arrival airport • Non-congestion related departure delay adjustment • They are all interconnected; there is no single parameter responsible for one statistic • The model output is most sensitive to airport capacity and weather inputs

12. Summary of Model Validation • We are satisfied overall for a national model • By the delay/cancellation statistics comparison • Because of our queuing theoretic and modular approach • The errors are contributed mostly by the capacity models at a few airports • The model assumes the theoretical capacity while some airports are specified by operational capacities. • The capacity models assume one set of curves for each meteorological condition. Airports may have multiple curves under weather due to different runway configurations used, which can also cause the inconsistency of in the GDP program. • Some errors are expected • Used published commercial schedule and generated GA schedule instead of real schedule • It does not considered the carrier flag in flight connection for delay propagation and flight cancellation • Kept it this way because it is impossible to specify it in the studies of future traffic scenarios

13. Benefits of LMINET2:More Realistic Setting & Richer Statistics • It captures the delay absorption, propagation, cancellation, and ground control • Instead of a giant airport delay calculator, it now tracks the delays of each individual flight • It is especially needed in modeling NAS in bad weather • It yields better delay estimates, even for the queuing delays offered by the old LMINET, because of more proper accounting of schedule disruption • It generates a richer set of statistics in addition to queuing delays: • Arrival/departure gate delay • Arrival/departure gate on-time percentage (if arrival delay > 15 min) • Taxi-out delay • Arrival/departure flight cancellation statistics • These metrics provide a better representation of the current NAS operations, and for calculating stakeholder metrics

14. Running LMINET2 • Fast turn around • Unlike the old LMINET for delay estimation, the computer work load is a function of congestion • It takes a few minutes for one day of traffic • Inputs • Flight schedule • Airport capacity • Airport weather

15. The Future of LMINET2 • The model is ready to be used • Better information on some parameters would be helpful • Fine-tuning the parameters • Will not yield a significantly better model • But will improve the modeling at isolated areas or metrics • It still lacks an airspace delay module to claim to be a complete NAS operations model • The projected throughput schedule construction is unchanged • It is run under universally good weather