Data Driven Methods for Airspace Performance Analysis

(1)

Center for Air Transportation Systems Research (CATSR) at George Mason University

Data Driven Methods for

Airspace Performance Analysis

Lance Sherry

John Shortle

Students

(2)

Research and Business Opportunities

Years Complexity of Interactions in Network of Distributed Agents Aircraft •Basic Aero •Propulsion Air Transportation • Air Transportation - Mail

Air Traffic Control

• Basic Airport Traffic Control

Air Transportation

• National Air Carriers • Point-to-Point Service • Inter-modal

• En-route Air Traffic Control

• Terminal Area Traffic Control

• National/International Network airlines •Civil Aviation Board

• Radar • Precision Approach • 1920 1940 1960 1980 Air Transportation • Deregulation • Hub monopolies •Schedule/Network optimization •Overscheduling •Yield Management • Fuel Management airlines

• Radar

• Precision Approach

• Flexible Airline Business Models • Low Cost Carriers/Regional Jet Airlines • Network configurations (Hub, point-to-point)

• Collaborative Decision Making • Revenue/Cost Synchronization • Aircraft Self-separation • Facility Resizing • Safety/Capacity Tradeoff

Networked

Scheduled

Operation

Point-to-Point

Scheduled

Operations

Optimized

Networked

Operations

Optimized

Stochastic,

Capacity-limited

Networked

Operations

2000

Barnstorming

Operations

2

(3)

Big Data Analytics in Air Transportation

Model-based

Engineering &

Big Data

Analysis

Latitude/Longitude Altitude and (Derived) True Airspeed

Insights & Understanding for Operators, Planners, &

Investors

Surveillance

Data

Weather Data

Aircraft Performance Models

Procedures

Flight Data

Aircraft Performance

Automation Behavior Models

Automation

Behavior

Models

(4)

Emission Inventory

(5)

Context

• Airport Management is required

to report Emissions Inventory for

aircraft Landing and Takeoff

Operations (LTO):

1. Sustainability planning

2. Climate Change Registries

3. Environmental Impact Studies

CnHm

+

S

+

N

+

O

→

CO + H O + N + O +

NOx

+

CO

+

SOx

+

Soot

+

UHC

Fuel

Air

Products of Ideal Combustion

Products of Non-Ideal Combustion

Accelerate to VR V2 Hold V2+ 10 knots at Constant Rate-of-Climb

1500 ft AGL Thrust Reduction Altitude

(Takeoff Thrust to Climb Thrust)

100 ft AGL Gear-Up

Maximum Emissions generated

during Takeoff Phase

(6)

Context

• Monitoring by sensors inaccurate due to

dispersion effects/prohibitively expensive

• Inventory Models

–

Mass of pollutants generated

–

ICAO Reference Model

Pollutant mass per flight =

Number of Engines

x

Time in Phase of Flight (T)

x

Fuel Flow Rate (FFR)

x

Emissions Index (EI)

• T, FFR, EI averages from ICAO data-base

(7)

Problem

• “Static” Inventory Models over-estimate

Emissions Inventory

• Two assumptions:

1. Average Time-in-Phase (assumed 2.9 mins

takeoff)

2. Thrust Setting for Takeoff (assumed 100%)

7

Pollutant mass per flight =

(8)

Solution

• Improve accuracy in Emissions Inventory

Estimate using

1. High-fidelity track surveillance data

2. Procedure data (i.e. navigation data-base)

3. Aircraft performance model

• Validated by Flight Data

4. Weather data

(9)

Center for Air Transportation Systems Research (CATSR) at George Mason University 9 Emissions Inventory

Emissions

Data Bank

Trajectory

Analysis

(AEDT/NIRS)

Emissions Index Fuel Burn Rate Time in Phase No. Engines Flight Schedule Assume 100% Takeoff Thrust

Estimate

Takeoff

Thrust

Setting

Takeoff Thrust Setting Aircraft Type •Aircraft Type •Aircraft Trajectory Avg. Fuel Burn Rate Time in Phase for each flight

Emissions Inventory (tons) =

# Engines ∙

Emissions Index ∙

Fuel Burn Rate ∙

Time in Phase

Aircraft Performance Model Weather Data Surveillance Track Data

Calculate

Emissions

Inventory

(Reference

Model)

Big Data Analytics

Standalone Takeoff Thrust Models (ACRP-04-02) Avg Time- in-Phase Fuel Burn Rate for each flight Estimated Takeoff Thrust Estimated Takeoff Thrust

(10)

Surveillance Track Data

Latitude/Longitude

Altitude

and

(

Derived) True Airspeed

10

Alternate filtering

techniques developed

V

_TAS

= (𝑽𝑽

𝑮𝑮

∙ 𝑺𝑺𝑺𝑺𝑺𝑺 𝜽𝜽 ) − (𝑽𝑽

𝑾𝑾

∙ 𝑺𝑺𝑺𝑺𝑺𝑺 𝝓𝝓 )

(11)

Aircraft Performance Equations

Total-Energy model: rate of work done by forces acting on the

aircraft = rate of change of potential and kinetic energy

Rearranging for Thrust

11

(12)

Results

0%

10%

20%

30%

40%

50%

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Fr

eq

u

en

cy

% Max Takeoff Weight

Thrust Settings for 1200 departures at ORD

µ = 86% Max Takeoff Thrust, σ = 11%

Validation:

• Airline supplied takeoff thrust settings.

• Range in thrust reduction from 0% to 24 %

• An average thrust reduction of 13%, standard deviation of 8%.

Thrust

(13)

Sensitivity Analysis – TOW and Headwind

13

(Marginal)

Sensitivity to

Headwind

(~5%)

Sensitivity to

Takeoff

Weight (~30%)

MD-83 (Super MD-80)

(14)

Modernization Cost/Benefits

Analysis: Metroplex Flow

De-confliction Using

RNP Procedures at Midway Airport

Akshay Belle

(15)

Metroplex Examples

15

Top 10 Metroplexes in the US

Sl.no

Metroplex

Ops per day

_{(Year 2012)}

_{within 30NM}

# Airports

1 New york

3257

4

2 Chicago

3055

2

5 Los Angeles

2797

4

3 Atlanta

2542

1

4 District of

Columbia

2434

3

6 Dallas

2236

2

7 San Francisco

1903

3

8 Miami

1734

2

9 Denver

1697

1

10 Charlotte

1498

1 New York Metroplex

Chicago Metroplex

14NM

17NM 8.5NM 10 NM 9NM

Total of 21 Metroplexes in the U.S serving

metropolitan area that account for:

• 35% of the nation’s population (314 M)

(United States Census Bureau, 2012)

• 44% of the gross domestic product ($15.68

trillion) (U.S. Department of Commerce,

2012)

(16)

Metroplex De-confliction – Terminal Airspace

Redesign (Spatial Strategy)

16

Before

N

After

22L

Departures

ORD

MDW

13C RNP

N

Airspace

Conflict

13C ILS

22L

Departures

ORD

MDW

Wind Direction Wind vector

No Airspace

Conflict

(17)

Chicago Metroplex De-confliction

17

• Chicago Metroplex

De-confliction

–

RNP0.3 w/RF Leg approach on

to 13C at MDW

–

Safe Vertical Separation

MDW - RNP0.3 w/RF Leg approach on to 13C

(18)

MDW Flows from East and West

18

Flows From East

Flows From West

Sl.no Direction Runway Approach Count

1 E 13C ILS 798 2 RNP 87 3 Visual 1026 4 13L Visual 8 5 22L Visual 840 6 22R Visual 70 7 _31C ILS 1467 8 Visual 345 9 31R Visual 5 10 4L Visual 48 11 4R ILS 390 12 Visual 1181 13 W 13C ILS 568 14 RNP 151 15 Visual 857 16 13L Visual 9 17 22L Visual 650 18 22R Visual 56 19 _31C ILS 387 20 Visual 987 21 31R Visual 2 22 4L Visual 50 23 _4R ILS 729 24 Visual 564

Flows and their respective Track count

Abeam

(19)

TRACON Flow Track Distance & Time

Performance

19

Flows From East

Flows From West

Ranking of flow from the East

Ranking of flow from the West

Abeam -

Final

waypoint on

STAR

(20)

Benefits RNP Approaches for MDW

(all Runways)

RNP 31 C from West

RNP 4R from the East

20

ILS available

Chicago Heights (CGT) Joliet (JOL)

(21)

Source of variance in RNP flows

21

“Vectors" up to the Start of the RNP

approach (base leg) introduce as much

variation in track distance/time as the ILS

approaches.

ILS Approach

RNP Approach

(22)

Airport Surface Operations

Analysis

Anvardh Nanduri, Kevin Lai

(23)

-500

0

500 1000

1500

2000

2500

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

Ex

ce

ss

S

ur

fa

ce

C

oun

t (

10 M

in P

er

io

ds

)

Day of Month

Arr Dep Avg 1 Sigma 2 Sigma

ATL 2012 Jan -

(24)

Annual Surface Ops

-500 0 500 1000 1500 2000 2500 3000 E x c e s s n u mb e r o f f li g h t s Dates

Excess Cum Arrivals Excess Cum Departures Avg 1 Sigma 2 Sigma 3 Sigma

3 Sigma 2 Sigma 1 Sigma Average Day of Year 2012 Daily C umu la tiv e 1 0 M in ut e Su rf ac e Co un ts Arrivals Departures 24

(25)

Daily Cumulative Surface Count

0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% 0 10 20 30 40 50 60 0 20 0 40 0 60 0 80 0 10 00 12 00 14 00 16 00 18 00 20 00 22 00 24 00 26 00 28 00 30 00 Fre qu en cy

Daily Cumulatve Surface Count

ATL 2012

Frequency Cumulative % Normal Distribution

(26)

Causal Analysis- Reduced Departure Rate -

Mar 13, 2012 (ATL)

0 5 10 15 20 25 30 35 00 :00-00: 59 00 :00-00: 59 01 :00-01: 59 01 :00-01: 59 02 :00-02: 59 02 :00-02: 59 03 :00-03: 59 03 :00-03: 59 04 :00-04: 59 04 :00-04: 59 05 :00-05: 59 05 :00-05: 59 06 :00-06: 59 06 :00-06: 59 07 :00-07: 59 07 :00-07: 59 08 :00-08: 59 08 :00-08: 59 09 :00-09: 59 09 :00-09: 59 10 :00-10: 59 10 :00-10: 59 11 :00-11: 59 11 :00-11: 59 12 :00-12: 59 12 :00-12: 59 13 :00-13: 59 13 :00-13: 59 14 :00-14: 59 14 :00-14: 59 15 :00-15: 59 15 :00-15: 59 16 :00-16: 59 16 :00-16: 59 17 :00-17: 59 17 :00-17: 59 18 :00-18: 59 18 :00-18: 59 19 :00-19: 59 19 :00-19: 59 20 :00-20: 59 20 :00-20: 59 21 :00-21: 59 21 :00-21: 59 22 :00-22: 59 22 :00-22: 59 23 :00-23: 59 23 :00-23: 59

Airport Departure Rate Airport Acceptance Rate Visibility Ceiling/1000 Wind Direction/10 Wind Speed

Runway Config Change 8L, 9R, 10 | 8R, 9L (35 ops/10 mins )

Runway Config

26R, 27L, 28 | 26L, 27R (34 ops/10 mins)

(27)

Cumulative Surface Counts - Reduced Departure Rate -

Mar

13, 2012 (ATL)

0 500 1000 1500 2000 2500 3000 3500 4000 TW 1 TW 4 TW 7 TW 10 TW 13 TW 16 TW 19 TW 22 TW 25 TW 28 TW 31 TW 34 TW 37 TW 40 TW 43 TW 46 TW 49 TW 52 TW 55 TW 58 TW 61 TW 64 TW 67 TW 70 TW 73 TW 76 TW 79 TW 82 TW 85 TW 88 TW 91 TW 94 TW 97 TW 100 TW 103 TW 106 TW 109 TW 112 TW 115 TW 118 TW 121 TW 124 TW 127 TW 130 TW 133 TW 136 TW 139 TW 142 TW 145 N umb er o f f lig ht s TW

Cumulative Actual Arr Surface Count Cumulative Actual Dep Surface Count Cumulative Sched Arr Surface Count Cumulative Sched Dep Surface Count

Runway Config Change

8L, 9R, 10 | 8R, 9L (35

ops/10 mins )

Runway Config

26R, 27L, 28 | 26L, 27R (34 ops/10 mins)

(28)

Causal Analysis- “Blue Sky” - May 18, 2012 (ATL)

0 5 10 15 20 25 30 35 00 :00-00: 59 00 :00-00: 59 01 :00-01: 59 01 :00-01: 59 02 :00-02: 59 02 :00-02: 59 03 :00-03: 59 03 :00-03: 59 04 :00-04: 59 04 :00-04: 59 05 :00-05: 59 05 :00-05: 59 06 :00-06: 59 06 :00-06: 59 07 :00-07: 59 07 :00-07: 59 08 :00-08: 59 08 :00-08: 59 09 :00-09: 59 09 :00-09: 59 10 :00-10: 59 10 :00-10: 59 11 :00-11: 59 11 :00-11: 59 12 :00-12: 59 12 :00-12: 59 13 :00-13: 59 13 :00-13: 59 14 :00-14: 59 14 :00-14: 59 15 :00-15: 59 15 :00-15: 59 16 :00-16: 59 16 :00-16: 59 17 :00-17: 59 17 :00-17: 59 18 :00-18: 59 18 :00-18: 59 19 :00-19: 59 19 :00-19: 59 20 :00-20: 59 20 :00-20: 59 21 :00-21: 59 21 :00-21: 59 22 :00-22: 59 22 :00-22: 59 23 :00-23: 59 23 :00-23: 59

Airport Departure Rate Airport Acceptance Rate Wind Speed Visibility Ceiling/1000 Wind Direction/10

No Runway Configuration Change

8L, 9R, 10 | 8R, 9L (36 ops /10 mins)

(29)

Cumulative Surface Counts – Blue Sky - May 18, 2012

(ATL)

0 500 1000 1500 2000 2500 3000 TW 1 TW 4 TW 7 TW 10 TW 13 TW 16 TW 19 TW 22 TW 25 TW 28 TW 31 TW 34 TW 37 TW 40 TW 43 TW 46 TW 49 TW 52 TW 55 TW 58 TW 61 TW 64 TW 67 TW 70 TW 73 TW 76 TW 79 TW 82 TW 85 TW 88 TW 91 TW 94 TW 97 TW 100 TW 103 TW 106 TW 109 TW 112 TW 115 TW 118 TW 121 TW 124 TW 127 TW 130 TW 133 TW 136 TW 139 TW 142 TW 145 N umb er o f f lig ht s TW

Cumulative Actual Arr Surface Count Cumulative Actual Dep Surface Count Cumulative Sched Arr Surface Count Cumulative Sched Dep Surface Count

Runway Configuration did not change

8L, 9R, 10 | 8R, 9L (36 ops /10 mins)

(30)

Airspace Risk Management

- Go Around

Stabilized Approaches

Zhenming Wang, Houda Kerkoub

(31)

Raw Surveillance Track Data

Navigation Data-base

(Procedures) Weather Data

Process Observed Data Calculate Derived data Calculate Risk Events and Factors

Risk Management Metrics

Derived data

Processed Observed Data Risk Events and Factors

Risk Management Metrics

Risk Events Definitions Risk Events Metric Definitions

(32)

Go Arounds

local time 18:00-18:59 18:00-18:59 19:00-19:59

quarter hour 3 4 1

arrival runway configuration 9R, 22L, 28 9R, 22L, 28 9R, 22L, 28 airport acceptance rate 15 15 15

arrival demand 72 72 73 ceiling (ft) 1200 1200 1000 wind direction 180° - S 180° - S 190° - S wind speed (kts) 14 14 10 visability (miles) 3 3 3 temperature (°F) 32 32 32 on-time arrival % 16.67% 26.32% 16.67% avg. arrival delay (min) 134 77 122 ₃₂

• Go Arounds are not

measured/reported.

• Track data used to

count and analyze

Missed Approach – 20%

Aborted Approach – 80%

Fit

Normal (0.007, 0.005)

Likelihood of a Go Around

Mean = 7/1000

Std Dev = 5/1000

Range = [0 22/1000]

(33)

Go-arounds

• 19 out of 3548

• About 5.4 per 1000 flights

(34)

Go Arounds - ASRS Taxonomy

Factor

% ASRS Reports

Airplane Issues

54%

Separation Violation

21%

Weather

17%

Flight/TAC Interaction

8%

Runway Issues

5%

No Reason provided

6%

34

(35)

Go Arounds - ASRS Taxonomy

1. Airplane Issue

53.74%

1.1 Unstable Approach

9.52%

1.1.1.High and Fast

6.12%

1.1.2 Other Approach

Issue

3.40%

1.2 Alerts

4.08%

1.3 Onboard failures

40.14%

(36)

Go Arounds – ATSAP Taxonomy

ATSAP Data, Bayesian Network Model

Firdu Bati (Dissertation)

(37)

Stabilized Approach

• 1000’ AGL

–

On Runway Center-line

–

On Glidepath

–

At Landing Speed (V

_Ref

)

–

At Rate of Descent for Glide-path (< 1000fpm)

(38)

Stabilized Approaches

Frequency of risk events from

1000 ft.

AGL to runway threshold

Fast

(39)

Frequency of risk events from

750 ft.

AGL to runway threshold

Stabilized Approaches

(40)

Summary table 500 ft. AGL

Frequency of risk events from

500 ft

. AGL to runway threshold

Stabilized Approaches

(41)

1000’ AGL

Groundspeed

Change

Descent

Rate of

Position with

Glidepath

Position with Runway

Centerline

#

04R

%

#

13C

%

#

31C

%

No change

Within limits

On Glidepath On Runway Centerline

721 74.56% 411 29.19% 987 84.22%

Not On Runway Centerline

35 3.62% 492 34.94% 52 4.44%

Above Glidepath On Runway Centerline

_{Not On Runway Centerline}

3 0.31% 0 0.00% 38 3.24%

2 0.21% 96 6.82% 0 0.00%

Excessive

On Glidepath On Runway Centerline

2 0.21% 1 0.07% 1 0.09%

Not On Runway Centerline

0 0.00% 2 0.14% 0 0.00%

Above Glidepath On Runway Centerline

_{Not On Runway Centerline}

0 ₀ 0.00% _{0.00% 24 1.70%}0 0.00% 1 ₀ 0.09% _0.00%

Greater than

10 knots

Within limits

On Glidepath On Runway Centerline

177 18.30% 14 0.99% 82 7.00%

Not On Runway Centerline

18 1.86% 233 16.55% 3 0.26%

Above Glidepath On Runway Centerline

_{Not On Runway Centerline}

1 0.10% 0 0.00% 5 0.43%

0 0.00% 102 7.24% 0 0.00%

Excessive

On Glidepath On Runway Centerline

4 0.41% 0 0.00% 3 0.26%

Not On Runway Centerline

3 0.31% 2 0.14% 0 0.00%

Above Glidepath On Runway Centerline

_{Not On Runway Centerline}

2 0.21% 0 0.00% 0 0.00%

0 0.00% 31 2.20% 0 0.00% ILS available Chicago Heights (CGT Joliet (JOL)