LMINET2: An Enhanced LMINET

1
LMINET2 An Enhanced LMINET

• Dou Long, Shahab Hasan
• dlong_at_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
Flight Schedule Delay Cancellation Model
Aircraft Connection and Turnaround Model
Airport Queuing Delay Model
Airport Demand
Flight Departure Delay
Demand Aggregate number of flights For queuing
delay calculations
Flight Scheduled service (OD, time,
equipment) Schedule delays depends on queuing
delay, schedule pad, and schedule delay
Aircraft For flight connection and delay
propagation
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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)
Taxi-in (1.71)
Arrival Gate (7.55)
Block (-2.19)
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

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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 a ßx
• a 7.64 , ß 0.966, R2 0.9368
• a 0, ß1.05, R2 0.9748

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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

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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
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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 LMINET2More 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 gt 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

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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

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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