© 2010 The MITRE Corporation. All Rights Reserved. Sector Capacity Prediction for Traffic Flow...

© 2010 The MITRE Corporation. All Rights Reserved.

Sector Capacity Prediction for Traffic Flow Management

Lixia SongApril 13th, 2010

© 2010 The MITRE Corporation. All Rights Reserved.2

En Route Congestion

Uncertain weather forecasts indicate current and future loss of airspace capacity…

Uncertain traffic forecastsprovide airspace demand…

If demand exceeds capacity, delays will occur and safety may

be compromised.

Role of TFM: Balance demand vs. capacity. Key function: Predicting capacity/congestion.Required: A good metric of capacity/congestion.

Air traffic control sectorCongestion Alerts


What is Sector Capacity?

AircraftCount (N)

Workload

OverloadThreshold

Sector Capacity

IncreasingComplexity

Cited from EUROCONTROL Experimental Center Note No. 21/03EDYCOLO Wednesday 4 July 2001


A Good Sector Capacity Metric for En Route TFM Decision Support…

• Better represents sector workload, and workload threshold, considering traffic complexity

• Is intuitive and relevant to human decision-makers

• Provides insight into congestion resolution options

• Is predictable at useful look-ahead times (30 min – 2 hr)

• Captures impact of convective weather


Flow Structure to Represent Traffic Complexity for TFM

Traffic Complexity

Detailed factors(traffic characteristics)for Dynamic Density

Controller Workload

Air Traffic Control

Aggregated factors(flow characteristics)

for predicting sector capacity

Traffic Flow Management

– Aggregate flow variables are more easily predicted at TFM time frames than aircraft interaction variables

– Flow structure is tightly related to controllers control strategy– Flow structure provides insight into congestion resolution options


The Flow Pattern Based Approach

• Identify the primary set of traffic flow patterns for each sector

18 20 22

Best Match

Predicted flow pattern

• Assess the sector capacity for each pattern of the set• Predict the sector capacity through pattern recognition


Under Severe Weather Impact

6

23

2

2

Case A

6

23

2

2

Case B

2

412

Case C


Under Weather Impact--- Available Sector Capacity Ratio

Traffic flow pattern is predicted with flows defined by sector transit triplets

LL’s CWAM1 model is usedto estimate the WAAF

AvailableFlowCapacityRatio is determined by mincut theory (inspired by METRON)

m

jj yRatiolowCapacitAvailableFW

ityRatioectorCapacAvailableS

1

Wmincut

Wmincut

Sector BSector A Sector C

T

B

S D

WAAF

Flow A-B-C

Omincut

Wmincut

Wmincut

Sector BSector A Sector C

T

B

S D

WAAF

Flow A-B-C

Omincut


The Effect of Convective Weather on Sector Capacity

• Compared three sector weather impact indexes– 2-D weather coverage– 3-D WAAF coverage– Flow-based AvailableSectorCapacityRatio

• Answer the following questions with statistical analysis of historical data– Which sector weather impact index has the strongest

correlation with actual sector capacity– What’s the predictability of these sector weather impact

indexes


Results from Initial Correlation Analysis

10

• June and July 2007 traffic and weather data were collected• The 95th percentile of sector throughput is used as the

estimated actual sector capacity• Initial analysis on 48 high sectors shows

– Flow-based AvailableSectorCapacityRatio has the strongest linear correlation with sector capacity for sectors with dominant flows

The AvailableFlowCapacityRatio of the dominant flow has strong linear correlation with the flow and sector capacity

– Probabilistic CWAM1 with right threshold is needed to calculate WAAF

• Initial analysis on 43 low sectors shows– Both Deterministic and probabilistic CWAM1 do not work well

for most of the low sectors


Linear Correlation between 95th Percentile of Sector

Throughput and Sector Weather Impact Indexes

0 10 20 30 40 50 60 70 80 90 1008

10

12

14

16

18

20

22

24ZDC12 2007 Summer

Sector Weather Impact Index

95th

Per

cen

tile

of

Sec

tor

Th

rou

gh

pu

t

2D, R2 = 0.6620

3D-Equal, R2 = 0.7922

3D-Profile, R2 = 0.8658

Flow-Based, R2 = 0.9460


Linear Correlation between 95th Percentile of Flow

Throughput and AvailableFlowCapacityRatio

ZDC16-ZDC12-ZDC18

ZDC72-ZDC12-ZDC18

ZDC16-ZDC12-ZDC17

ZDC16

ZDC72

ZDC12

ZDC18

ZDC17


Correlation between 95th Percentile of Sector

Throughput and AvailableFlowCapacityRatio


Initial Results of Predictability Analysis

• Predictability of 2D weather coverage and 3D WAAF coverage are comparable

• The predictability of available flow capacity ratio is relatively lower than the other two

• 3D WAAF coverage and the reduced flow capacity ratio tend to be over-predicted– Both due to the over-prediction of CIWS echo top

14


Predictability Comparison

15

LAT=30 LAT=60 LAT=90 LAT=1200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

ZID66

2D3DA8ZID82-ZID66-ZID75ZID83-ZID66-ZID88

R^

2 b

etw

ee

n P

red

icte

d a

nd

Ac

tua

l


36-16-12

Sensitivity to Weather Location

Omincut

Cases with the prediction to be 0 but the actual to be 1


Future Research

• Develop probabilistic model to capture weather prediction uncertainty, especially weather shape and location

• Improve the flow-based model with more research on translating the flow blockage to sector capacity reduction– Consider the impact of weather on traffic complexity

• Enhancing available flow capacity ratio calculation to more accurately estimate impact of transition flows

• Flow capacity prediction and usage in both current and future operational environment– This analysis reveals the directional (flow) capacity usage in

the current operational environment• NAS-Wide analysis is necessary


Questions for FCA Capacity

• What is FCA capacity?

• What is a good metric of FCA capacity?

• How to predict the nominal FCA capacity?

• How to reduce the FCA capacity under weather impact?


Back Up


16-12-14

16-12-1799-12-11

16-12-18

Sector Transit Triplets to Represent Flows 1/6/04 14Z ZDC12 Major Triplets (actual traffic)

• Flows are the triplets that have at least two aircraft within the given time period.


Identify Primary Set of Traffic Flow Patterns with Self-Organizing Maps (SOMs)

P1 : Major Flow36-16-12


P3 : Major FlowOthers



P3 : Major FlowOthers

• Unified distance matrix visualizes distances between neighboring traffic flow patterns, and helps to see the cluster structure of the traffic flow patterns

• Each component plane shows the values of each feature in the patterns organized in U-Matrix


Assessing Sector Capacity Through Observing System Performance

0 2 4 6 8 10 12 14 16 1895

100

105

110

115

120

125

Aircraft Count

Ma

jor

Flo

w 3

6-1

6-1

2 A

ve

rag

e

Dis

tan

ce

Flo

wn

in Z

DC

36

(n

mi)

ZDC16 with Major Flow 36-16-12


Weather Avoidance Altitude Field (WAAF)

VIL3+ coverage in 60X60 region

90th pct. Echo top in 16X16 region

Sector floor

Sector top

90th pct.16km echo top

DeltaZWeather

AvoidanceAltitude

% VIL pixels in 60km region20 40 60

-10

0

10D

elta

Znon-deviation

deviation


-10

0

10D

elta

Znon-deviation

deviation

VIL3+ coverage in 60X60 regionVIL3+ coverage in 60X60 region

90th pct. Echo top in 16X16 region90th pct. Echo top in 16X16 region

Sector floor

Sector top


DeltaZWeather

AvoidanceAltitude

Sector floor

Sector top


DeltaZWeather

AvoidanceAltitude


-10

0

10D

elta

Znon-deviation

deviation


-10

0

10D

elta

Znon-deviation

deviation

LL’s CWAM1


Example Available Ratio of Flow Capacity


Flights Went Through Mincut to Avoid Weather



26


0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

ZDC16

2D3DA8ZDC36-ZDC16-ZDC10ZDC36-ZDC16-ZDC12

R^

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27


0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

ZNY42

2D3DA8ZBW20-ZNY42-ZNY09ZBW20-ZNY42-ZNY55

R^

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28


0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

ZOB77

2D3DA8ZOB27-ZOB77-ZNY75ZOB36-ZOB77-ZOB59

R^

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© 2010 The MITRE Corporation. All Rights Reserved. Sector Capacity Prediction for Traffic Flow...

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