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Analytical Workload Model for Estimating En Route Sector Capacity - - PowerPoint PPT Presentation

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Analytical Workload Model for Estimating En Route Sector Capacity in Convective Weather* John Cho, Jerry Welch, and Ngaire Underhill 16 June 2011 *This work was sponsored by the Federal Aviation Administration under Air Force Contract No.

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SLIDE 1

Paper 33-1 JYNC 6/2/2011

Analytical Workload Model for Estimating En Route Sector Capacity in Convective Weather*

John Cho, Jerry Welch, and Ngaire Underhill

16 June 2011

*This work was sponsored by the Federal Aviation Administration under Air Force Contract No. FA8721-05-C-0002. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the United States Government.

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Paper 33-2 JYNC 6/2/2011

Issues with Existing Airspace Capacity Models

  • Weather-impact models yield flow reduction relative to

historical fair-weather traffic (fractional availability)

– Route blockage model – Sector min-cut max-flow approach – Directional ray scanning method

  • Controller workload, which determines sector capacity,

is not taken into account

  • Workload-based sector models give absolute capacity

values but weather effects not included

– Detailed simulation models – “Macroscopic” analytical models

⇒ Incorporate convective weather effects into analytical sector workload model

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SLIDE 3

Paper 33-3 JYNC 6/2/2011

Outline

  • Motivation
  • Sector capacity model without weather
  • Sector capacity model with weather
  • Results and issues
  • Summary
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SLIDE 4

Paper 33-4 JYNC 6/2/2011

Controller Workload Limits Traffic

  • Sector reaches capacity when the controller team is fully occupied
  • Queuing grows with three critical traffic-dependent event rates

Mh V21 ∆t

Aircraft randomly located with density κ

V21

Conflict rate

λc = (2 N2/Q) Mh Mv V21

Sector aircraft count N Sector airspace volume Q Miss distances Mh, Mv Mean closing speed V21

Transit (boundary crossing) rate

λt = N/T

Sector aircraft count N Mean sector transit time T

Recurring event (scanning/monitoring) rate

λr = N/P

Sector aircraft count N Recurrence period P Monitor Alert Parameter (MAP) basis

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Paper 33-5 JYNC 6/2/2011

Task-Based Analytical Sector Workload Model

G = Gb + Gc + Gr + Gt

Sector workload intensity Conflict Recurring Transition

Gc = τc [(2 N2/Q) Mh MvV21] Gr = τr [N/P] Gt = τt [N/T]

Service times (empirical ) Occurrence rates (calculated from

airspace parameters)

Fraction of controller time

Background

Welch et al., 2007: Macroscopic model for estimating en route sector capacity, 7th USA/Europe ATM R&D Seminar, Barcelona, Spain

  • Determining the unknown service times

– Live approach

Measure controller performance

– Regression approach

Observe peak daily counts Np for many sectors Calculate corresponding model capacities Nm Find service times that best fit Nm to Np bound

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Paper 33-6 JYNC 6/2/2011

500 1000 1500 2000 0.2 0.4 0.6 0.8 1

Vertical Miss Distance Mv , ft

Effect of Altitude Changes

  • Aircraft with vertical rates cause increased uncertainty
  • Adapt by increasing vertical miss distance Mv

―

Determine fraction Fca of aircraft with ≥ 2000 ft altitude change

―

As Fca grows, increase Mv linearly from 1000 ft to Mvmax Mvmax ≈ 1600 ft (for NAS)

∆a

Fraction Fca of Aircraft with ∆a > 2000 ft

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Paper 33-7 JYNC 6/2/2011

Fitted Capacities vs. Peak Counts

(790 NAS Sectors July–August 2007)

5 10 15 20 25 30 5 10 15 20 25 30

Observed Peak Count NAS Model Capacity

Simple analytical model can bound data well and is suitable for real-time application

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SLIDE 8

Paper 33-8 JYNC 6/2/2011

Outline

  • Motivation
  • Sector capacity model without weather
  • Sector capacity model with weather
  • Results and issues
  • Summary
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SLIDE 9

Paper 33-9 JYNC 6/2/2011

Convective Weather Avoidance Model (CWAM)

Creating the model

Planned Path Actual Path

IDENTIFY WEATHER ENCOUNTERS

Planned Path End Encounter Begin Encounter

VIL

ENSEMBLE OF CIWS WEATHER & ETMS TRAJECTORIES

Planned Path Actual Path

VIL VIL

Planned Path Actual Path

DEVIATION DATABASE

2006-2008 Database Classified Weather Encounters Non-Deviation Deviation

Total Weather Encounters: Weather Encounters w/ Deviation: Weather Encounters w/o Deviation: Weather Encounters Edited: ~5000 ~3500 ~10000 ~1500

CLASSIFY TRAJECTORY

Actual Path Planned Path Mean Deviation Threshold

Deviation Non-deviation

Begin Deviation End Deviation Decision Point

Data Editing

Actual Path Planned Path

Edited Trajectories

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Paper 33-10 JYNC 6/2/2011

Weather Avoidance Field (WAF)

Applying the model

VIL EchoTop

CIWS WEATHER DATA

Spatial Filters

DEVIATION DATABASE

Non-Deviation Deviation

Echo Top 90th Percentile 60km VIL Area Coverage

Deviation Probability

Statistical Pattern Classifier Flight Altitude – 16km

WEATHER AVOIDANCE FIELD

WAF

60km VIL Area Coverage Echo Top 90th Percentile

Deviation Probability Lookup Table

Flight Altitude – 16km

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Paper 33-11 JYNC 6/2/2011

Weather Blockage Modification to Sector Workload Model

) 1 (

max

+ + + + = N Q BN N T N P G G

c t r b

τ τ τ

) 1 ( ) 1 ( ) (

max w c t w w r b

F Q N BN T N P N F G G − + + + + + = τ τ τ τ

Fw = fraction of airspace blocked by weather τw = time needed per reroute due to weather blockage

No Weather With Weather

  • Compute Fw from WAF data

― 80% WAF contours ― Integrate over WAF contours at 2000-ft altitude increments ― Fractional blockage of 3D sector volume

  • Fit to observed sector peak counts during weather to obtain τw

― Compare to τw = 45–60 s estimated by experienced air traffic controller

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Paper 33-12 JYNC 6/2/2011

Outline

  • Motivation
  • Sector capacity model without weather
  • Sector capacity model with weather
  • Results and issues
  • Summary
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Paper 33-13 JYNC 6/2/2011

Some Results Using Observed Weather

Fair-weather model capacity Model capacity with τw = 30 s Model capacity with τw = 90 s Actual sector peak count

Peak Count Peak Count

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Paper 33-14 JYNC 6/2/2011

Weather Effects on Sector Transit Time

  • “Cutting corners” to avoid

weather decrease mean sector transit time

  • Use fitted wx blockage-

transit time relationship to adjust mean transit time in capacity forecast

  • Fca does not show

dependence on weather blockage

Slope = -0.5

ZDC32

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Paper 33-15 JYNC 6/2/2011

Model vs. Observed Peak Sector Count

  • Capacity model should bound sector peak count data
  • Still do not have a lot of heavy weather impact cases
  • For now set τw = 45 s (consistent with subject matter expert estimate)

31 ARTCC-days worth of data used

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Paper 33-16 JYNC 6/2/2011

Some Results with Forecast Weather

  • Historical mean sector transit time and Fca per are used in forecast

― Transit time adjusted for weather blockage ― Better to use time-dependent forecast values of transit time and Fca if available

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Paper 33-17 JYNC 6/2/2011

Model Dependencies

  • Three workload components

affected by weather

― Conflict resolution task (via available airspace reduction) ― Weather rerouting task ― Sector hand-off task (via mean transit time reduction)

  • The rerouting and hand-off tasks

dominate the dependence of workload on weather except at very high weather blockages

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Paper 33-18 JYNC 6/2/2011

Capacity vs Weather Blockage Fraction

Capacity dependence on weather blockage is nonlinear

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Paper 33-19 JYNC 6/2/2011

Sector Weather Blockage Forecast Errors

  • Sector weather blockage is

scalar: Straightforward error analysis

  • Need to accumulate more data

for heavy weather cases

22 ARTCC-days worth of data used

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Paper 33-20 JYNC 6/2/2011

Sector Capacity Forecast Errors

  • No sector capacity truth

available

  • Comparison of model capacity

using forecast data vs.

  • bserved data
  • Accurate forecast of sector

transit time as important as weather forecast

  • Obs. T, Fca; Forecast Fw
  • Obs. Fca; Forecast Fw, T

Forecast Fw, T, Fca

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Paper 33-21 JYNC 6/2/2011

Directional Capacity Issue

  • Sector capacity (peak traffic count) is scalar—no

differentiation based on flow direction

  • But flow capacity is directional

– Sector transit time depends greatly on sector shape and travel direction – Weather blockage can be highly directional

  • Formulate workload model for directional capacity

– Replace scalar Fw with directional weather blockage in reroute term – Utilize existing directional blockage model

  • Scalar capacity depends on directional capacity and 4D flight

trajectories—a difficult forecast problem

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Paper 33-22 JYNC 6/2/2011

Summary

  • Sector capacity model based on analytical workload

model was modified to include weather effects

  • Difficult to validate because “truth” is not available

– Model as upper bound—use statistics – Initial results are promising—need to analyze more data

  • Sector capacity forecast uncertainties arise from

– Sector transit times – Weather

  • Weather forecast uncertainties are large at several

hours in advance

– Huge effort in developing complicated and ultradetailed capacity model may not be justified

  • Need to tackle directional capacity issue
  • Collaboration with MIT ORC and Metron to provide

sector capacity input to air traffic flow optimization models

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Paper 33-23 JYNC 6/2/2011

Back-up Slides

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Paper 33-24 JYNC 6/2/2011

Peak Traffic, Operational MAP, Model Capacity for NAS 5 10 15 20 25 30 10000 20000 30000 40000 50000 60000 70000 80000 Sector Volume (nm3) Aircraft Count LL Model Capacity Operational MAP Values Observed Peak Count, Np Peak Daily Traffic of NAS Sectors vs Transit Time 5 10 15 20 25 30 300 600 900 1200 1500 1800 Transit Time, T (sec) Aircraft Count MAP Capacity NMAP Observed Traffic Counts Peak Daily Throughput of NAS Sectors vs Transit time 20 40 60 80 100 120 140 300 600 900 1200 1500 1800 Transit Time, T (sec) Throughput, aircraft/hour

MAP Throughput FMAP Observed Throughput

Monitor Alert Parameter (MAP) Model

MAP capacity is based on handoff workload, assuming 36-second handoff time per flight

Peak throughput, FMAP = NMAP/T FMAP = 100 aircraft/hour Peak aircraft count, NMAP = T/36 (18 aircraft limit) [T is mean transit time, in seconds]

Operational MAP settings:

  • over-estimate capacity of small sectors by ignoring conflict workload
  • show that workload, not MAP rule, limits small-sector capacity

Lincoln Laboratory model

  • accounts for additional workload effects
  • extrapolates small sector workload capacity to large sectors
  • shows that18-aircraft limit under-estimates capacity in large sectors

Advantages of fitting models to peak count and transit time data:

  • simple and inexpensive
  • can determine system workload parameters for
  • entire NAS
  • individual centers
  • could support automated performance and parameter updates

Slope of peak count data shows that hand-off time is less than 36s FMAP is greater than 100/hr MAP over-estimates capacity when traffic density increases conflict workload FMAP determined by 18-aircraft limit, not workload

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Paper 33-25 JYNC 6/2/2011

Convective Weather Forecast Issues

Actual 1-hr fcst 2-hr fcst 3-hr fcst

19:30 20:00 20:30 21:00 21:30 22:00 UT

ZME26 2010-6-17 25-kft WAF