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Assessing the viability of an occupancy count prediction model SESAR Innovation Days 2017 Nicolas Suarez, Iciar Garcia Ovies , Danlin Zheng, CRIDA Jean Boucquey , EUROCONTROL Belgrade, 28 th November 2017 Contents Introduction Uncertainty

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Nicolas Suarez, Iciar Garcia‐Ovies, Danlin Zheng, CRIDA Jean Boucquey, EUROCONTROL

Assessing the viability of an occupancy count prediction model

SESAR Innovation Days 2017

Belgrade, 28th November 2017

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Contents

COPTRA SID 2017 2

Introduction

  • Uncertainty in ATM
  • COPTRA Project
  • COPTRA Validation

Exercise 01

  • Description
  • Methodology
  • Results

Exercise 02

  • Description
  • Methodology
  • Results
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Introduction

UNCERTAINTY IN ATM

COPTRA SID 2017 3

The actual DCB process is subject to uncertainty The actual DCB process is subject to uncertainty

COPTRA project aims at improving the demand predictions through the quantification of uncertainty in order to better understand the likely evolution of the demand and therefore improve decision making.

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COPTRA Project

DESCRIPTION

COPTRA General Presentation 2017 4 COPTRA General Presentation 2017 4

  • COPTRA is a SESAR Exploratory Research Project.
  • Activities are organised in 3 main WP:
  • WP02 Building Probabilistic Trajectories
  • WP03 Combining Probabilistic Trajectories
  • WP04 Application of Probabilistic traffic prediction to ATC planning

T T OT Pr

  • babilistic

T r ajec tor y F light Plan T r ajec tor y Cr itic al air c r aft and networ k impac t

FPL

Hotspot Pr

  • babilistic

Oc c upanc y Count

WP03 WP04 WP02

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COPTRA Project

ALGORITHM

COPTRA SID 2017 5

  • Obtain the probability

that a flight is in a sector

1 STEP

  • Compute the distribution
  • f the probabilistic
  • ccupancy count from

the individual probabilities of a flight being in a sector

2 STEP

  • Improve planning

accuracy in the tactical phase

RESULT

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COPTRA Project

VALIDATION EXERCISES

COPTRA General Presentation 2017 6

Initial viability of the COPTRA algorithm Operational applicability

  • f the

COPTRA algorithm

Asses the quality of the current occupancy count predictions Establish the initial viability

  • f the COPTRA algorithm

to improve occupancy count predictions Determine the potential improvements brought by the COPTRA approach in

  • ccupancy counts

prediction accuracy and uncertainty Evaluate the use of

  • ccupancy count

distributions in predicting hotspot Explore the visualization

  • f uncertainty in

enhanced occupancy count graphs

EXE 01 EXE 02 EXE 03 EXE 04 EXE 05

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EXERCISE 01

DESCRIPTION

COPTRA General Presentation 2017 7

Assess the accuracy and quality of current

  • ccupancy prediction

to establish the baseline for further validation Occupancy counts obtained through FPLs in 3 time horizons (‐3h, ‐1h and 0h) Occupancy counts obtained through the improved flight plan (imFPL)

COMPARE

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EXERCISE 01

imFPL

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FPL imFPL Average radar track

imFPL = FPL with no uncertainty

Most probable trajectory between a given city pair Methodology:

  • FPL (3 time horizons ‐3h, ‐1h, 0h)
  • Radar Track

COMBINES

The use of the imFPL will enhance the accuracy of the

  • ccupancy count predictions

used by ANSPs and NM

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EXERCISE 01

SCENARIO SELECTION

COPTRA SID 2017 9

1

  • Ranking of days with more

controller issued vectors

2

  • Ranking of sectors with more

controller issued vector

3

  • Ranking of origin/destination

with more controller issued vectors

4 SECTOR IN BARCELONA ACC 12th May 2016

LECBPP2 LECBP1L LECBP1U LECBLVL

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EXERCISE 01

METHODOLOGY

COPTRA SID 2017 10

Calculation of the occupancy count using FPLs at the three time horizons Calculation of the occupancy count using imFPL Calculate difference between occupancy count variables using Glass’ delta indicator

2 OBJECTIVES

  • 1. Determine the quality of the current occupancy count

estimations and determine the occupancy count error

  • 2. Establish the baseline for further validation

experiments

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EXERCISE 01

RESULTS

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EXERCISE 01

RESULTS

[Insert name of the presentation] 12 EXE 01 SD MSE Glass' Δ CI t‐test LECBLVL 3h 2,7506 31,0000 1,5690 [0.5672;2.5708] 4,2689 1h 2,5774 28,2857 1,2258 [0.3050;2.1465] 3,4353 0h 2,4862 14,0000 0,5393 [‐0.2674;1.3461] 1,5351 LECBP1L 3h 2,4099 45,4286 1,5018 [0.5169;2.4869] 4,8116 1h 3,1483 31,3571 1,1979 [0.2831;2.1126] 3,5203 0h 3,3553 21,1429 0,9297 [0.0671;1.7923] 2,6638 LECBP1U 3h 4,4308 68,1429 1,6671 [0.6398;2.6943] 4,2906 1h 3,6132 54,9286 1,5480 [0.5515;2.5445] 4,3904 0h 4,4973 34,2857 1,1227 [0.2235;2.0218] 2,8669 LECBPP2 3h 1,6723 31,9286 1,8668 [0.7851;2.9483] 5,9928 1h 3,1796 11,1429 0,6649 [‐0.1570;1.4867] 1,64186038 0h 2,6520 6,2857 0,3069 [‐0.4798;1.0936] 0,8327

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EXERCISE 02

DESCRIPTION

COPTRA General Presentation 2017 13

Assess the initial viability of the COPTRA algorithm Real occupancy counts Predicted occupancy counts with COPTRA algorithm

COMPARE

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EXERCISE 02

METHODOLOGY

COPTRA SID 2017 14

Calculation of the real occupancy count using radar tracks Calculation of the predicted occupancy count using COPTRA algorithm Calculate difference between occupancy count variables using Glass’ delta indicator

2 OBJECTIVES

  • 1. Improve the prediction of hotspots through the

provision of probabilistic occupancy counts

  • 2. Understand the use of probabilistic occupancy counts
  • n contiguous sectors
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EXERCISE 02

RESULTS

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EXERCISE 02

RESULTS

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EXERCISE 02

RESULTS

COPTRA SID 2017 17

EXE02

SD MSE Glass' Δ LECBLVL 1,3842 2,5104 0,6456 LECBP1L 1,8319 2,1097 0,5061 LECBP1U 2,3142 4,3931 0,5191 LECBPP2 2,4153 5,8417 0,4630

EXE01 vs EXE02

SD MSE Glass' Δ LECBLVL 3h 2,7506 31,0000 1,5690 1h 2,5774 28,2857 1,2258 0h 2,4862 14,0000 0,5393 EXE02 1,4315 4,4413 1,0952 LECBP1L 3h 2,4099 45,4286 1,5018 1h 3,1483 31,3571 1,1979 0h 3,3553 21,1429 0,9297 EXE02 2,0090 5,6655 0,8744 LECBP1U 3h 4,4308 68,1429 1,6671 1h 3,6132 54,9286 1,5480 0h 4,4973 34,2857 1,1227 EXE02 2,5778 10,9181 0,9398 LECBPP2 3h 1,6723 31,9286 1,8668 1h 3,1796 11,1429 0,6649 0h 2,6520 6,2857 0,3069 EXE02 2,1673 13,0107 1,4133

Values of glass delta show a medium size effect of the similarity between the two dataset. The values of glass delta corresponding to EXE02 shown in the table are, in general, between the same indicator for 1h and 0h of the EXE01 (predicted occupancy). In the best cases, the size effect is even better than 0h predicted

  • ccupancy (LECBP1U).
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Limitations of the results

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Only archived data Limited network view Mathematical viability of the algorithm

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Conclusions

COPTRA SID 2017 19

  • Description of the operational context of the use of

uncertainty in a trajectory based

  • perations

environment.

  • Description of the validation approach of COPTRA.
  • Establishment of a baseline to explore the viability
  • f the COPTRA algorithm.
  • Improvements in the occupancy count prediction

through the use of the COPTRA algorithm.

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This project has received funding from the SESAR Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 699274

The opinions expressed herein reflect the author’s view only. Under no circumstances shall the SESAR Joint Undertaking be responsible for any use that may be made of the information contained herein.

Thank you very much for your attention!