A Baseline for Terminal Airspace Design Assessment Tobias - - PowerPoint PPT Presentation

a baseline for terminal airspace design assessment
SMART_READER_LITE
LIVE PREVIEW

A Baseline for Terminal Airspace Design Assessment Tobias - - PowerPoint PPT Presentation

A Baseline for Terminal Airspace Design Assessment Tobias Andersson Granberg Linkping Valentin Polishchuk University Billy Josefsson LFV DK- SE FAB DK FIR + SE FIR Flights on Apr 30 2012 Flights on Apr 30 2013 Swedens 3


slide-1
SLIDE 1

A Baseline for Terminal Airspace Design Assessment

Tobias Andersson Granberg Valentin Polishchuk Linköping University Billy Josefsson LFV

slide-2
SLIDE 2

DK- SE FAB

slide-3
SLIDE 3

DK FIR

+

SE FIR

slide-4
SLIDE 4

Flights

  • n

Apr 30 2012

slide-5
SLIDE 5

Flights

  • n

Apr 30 2013

slide-6
SLIDE 6

Congestion hotspots: Airports

Sweden’s 3 largest airports:

  • Arlanda
  • Gothenburg
  • Bromma
slide-7
SLIDE 7

Stockholm “Air Portal”

slide-8
SLIDE 8

Justusson, B. (2015). Generalkarta 1958, S.Sverige, flygversion

Stockholm TMA (1958)

slide-9
SLIDE 9

1990’s -- Historical layout

  • Experts opinion
  • Hands-on patching
  • Rule-of-thumb
  • No global outlook

Stockholm TMA (now)

slide-10
SLIDE 10

Stockholm TMA (future)?

2012 -- LFV’s systematic study

  • improve the design with
  • ptimization tools
  • clean sheet approach
  • explore operational

concepts

slide-11
SLIDE 11
  • Optimal DESign of Terminal Airspace
  • Linköping University

+LFV

+ reference group

  • Funding for 2015--2018

– Swedish Gov. Agency for Innovation Systems

ODESTA Project

slide-12
SLIDE 12

This talk

  • (One possible) step towards operations
  • ptimization

○ single aspect

  • Feeders <-> entry/exit points assignment

○ capacitated matching ○ different paradigms

slide-13
SLIDE 13

Why such a study?

HUGE

  • ptimization problem

How to deal? Split into

  • subproblems
  • components
  • layers
slide-14
SLIDE 14

TMA design

Matching

  • demand

○ arrivals ○ departures

to

  • resources

○ available airspace ○ RWYs

(with help of middleware) STARS, SIDs, sectors, …

slide-15
SLIDE 15

Our focus

Outer rim

  • demand

○ arrivals ○ departures

  • resources

○ entry/exit points ○ considered fixed, given

slide-16
SLIDE 16

Problem shaping up

Output entry/exit point for each flight Input

  • resources

○ entry/exit points

  • demand

○ ?

slide-17
SLIDE 17

Demand

Mining historical data

  • EUROCONTROL’s

DDR2

  • .so6: SAAM 4D trajectories

last filled flight plans

  • to/from S-TMA in 2014

Other possible demand definitions

  • Simulated demand

○ x2, x3, ...

  • Projected demand

○ Random process, …

slide-18
SLIDE 18

For each flight

Extract

  • last point before TMA

entrance for in-flights

  • first point after TMA

exit for out-flights Call it feeder Path before/after feeder --

  • utside TMA designer interest

entry / exit points

slide-19
SLIDE 19

Minor cleanup

Excluded flights

  • not through an echart point
  • circular
  • ESCM, ESOW, ESSU

○ small airfields in the TMA

~200 flights overall

slide-20
SLIDE 20

Major cleanup Preprocessing: Feeder usage statistics

# of aircraft # of feeders Feeders # of aircraft / 10

<10% flights, in any time interval “Pareto-like” distribution

slide-21
SLIDE 21

Demand

Flights through 40 feeders, for each flight f

  • feeder F(f)
  • RWY(f)
  • time at F(f)
  • in/out
  • a/c type
slide-22
SLIDE 22

Demand Resource

Entry/exit points

Assignment

Flights through 40 feeders, for each flight f

  • feeder F(f)
  • RWY(f)
  • time at F(f)
  • in/out
  • a/c type
slide-23
SLIDE 23

Before assignment...

“Airline dream”: Great Circle path GCD(f) = GCD(F(f),RWY(f)) GCDF =

Σf GCD(f)

Far from reality, “ATCOs nightmare”, ignores even entry/exit points, …

F(f) RWY(f)

slide-24
SLIDE 24

GCD-Greedy

“Airline dream” s.t. use of entry/exit pts w(F(f),E) = GCD(F(f),E) + GCD(E,RWY(f)) GCD-Greedy(f) = minE w(F(f),E) GCD-Greedy =

Σf GCD-Greedy(f)

Unstructured FF in TMA, “ATCOs bad dream”, …

F(f) RWY(f)

slide-25
SLIDE 25

Current-Greedy

“Airline dream” s.t. use of entry/exit pts and STARs/SIDs w(F(f),E) = GCD(F(f),E) + Current(E,RWY(f)) Current-Greedy(f) = minE w(F(f),E) Current-Greedy =

Σf Current-Greedy(f)

Structured flow in TMA, but potentially overloaded points…

F(f) RWY(f)

slide-26
SLIDE 26

Points usage statistics

max point load (# of aircraft) # of hrs with max load

GCD-Greedy Current-Greedy

slide-27
SLIDE 27
  • Split time into intervals
  • f length T = 1 hr

○ standard in ATM? ○ T = 30min, 1.5hr are OK too ○ rolling horizon (20/60min) ○ …

  • Within every interval

any entry / exit point has ≤ N = 7 flights

○ historical max load (2014) ○ just total, separation ignored ○ cost(N) dependence below

Capacity constraints

F(f) RWY(f)

slide-28
SLIDE 28

Minimum-weight capacitated one-side-perfect matching in weighted complete bipartite graph

Graph on 2 sets (bipartite)

  • edge between any F,E (complete)
  • w(F,E): edge weight (weighted)

(N-)Matching: set of edges, s.t.

  • any F incident to an edge of M (perfect)
  • any E incident to ≤N edges of M (capacitated)

Min-weight matching (w-min N-matching): N-matching with min total edge weight Efficient algorithms exist (reduce to mincost flow) Feeders Entry/exits

slide-29
SLIDE 29

F(f) RWY(f)

GCD-Match

w(F(f),E) = GCD(F(f),E) + GCD (E,RWY(f)) For each hour h M*h = w-min N-matching GCD-Match =

Σh M*h

No overloaded points, but unstructured FF in TMA (“ATCOs bad dream”), …

slide-30
SLIDE 30

F(f) RWY(f)

Current-Match

w(F(f),E) = GCD(F(f),E) + Current(E,RWY(f)) For each hour h M*h = w-min 7-matching Current-Match =

Σh M*h

slide-31
SLIDE 31

F(f) RWY(f) Actual (historical) flown distance Current-Current(f) = GCD(F(f),E (f)) + GCD(E(f),RW(f)) Current-Current =

Σf Current-Current(f)

Current-Current

slide-32
SLIDE 32
slide-33
SLIDE 33
slide-34
SLIDE 34

GCD-Greedy − Current-Greedy: cost of STARs/SIDs

GCD-Match − Current-Match: similar (STARs/SIDs stretch factor)

slide-35
SLIDE 35

GCD-Match − GCD-Greedy: human factors

Current-Match − Current-Greedy: similar (sector load ignored)

slide-36
SLIDE 36

GCD-Greedy − GCDF: cost of flying in/out via the pts

small (pts spread evenly around); Feeders--Points graph is a good spanner

slide-37
SLIDE 37

CurrentCurrent − GCDF: overall cost of control

~ price of anarchy: best centralized outcome / best individualized outcome

slide-38
SLIDE 38

Greedy: overloaded_hrs(N)

N = 7 : ~1/2 of hrs are overloaded

GCD-Greedy Current-Greedy

slide-39
SLIDE 39

Matching: cost(N) GCD-Match Current-Match

High sensitivity to N around N=10-15

slide-40
SLIDE 40

Summary

  • Subproblem in TMA optimization

○ feeders--entry/exit points matching

  • Local flow modification

○ keep the rest intact

  • How much is the control?

○ where efficiency may be lost/gained ○ room for improvement

  • Applicable to any TMA

○ human decides T and N ○ the rest is (almost) automated

Extensions

  • Weigh distance w.r.t. a/c type

○ not all a/c are equal ○ noise, not only distance

  • Bound sector load

○ not single point load

  • Optimize entry/exit points locations

○ don’t keep them fixed

  • Re-sectorize

○ better locate the pts and ○ sector boundaries balance workload

slide-41
SLIDE 41

!Happy Matchings!

Tobias Andersson Granberg Valentin Polishchuk

firstname.lastname@liu.se

Linköping University Billy Josefsson LFV

slide-42
SLIDE 42
slide-43
SLIDE 43
slide-44
SLIDE 44

S-TMA (with a BMA STAR and SID)

slide-45
SLIDE 45
slide-46
SLIDE 46
slide-47
SLIDE 47
slide-48
SLIDE 48
slide-49
SLIDE 49