日欧の空域データにおける共通の傾向
Common Trends in Japanese – andEuropean Airspace Data

C. Gwiggner, 蔭山 康太 , 長岡 栄
ENRI, Tokyo
Happyo Kai 2008

Outline●Traffic Gaps●

Data Analysis– Trends– Propagation●

Comparison Japanese / European Data

●Future WorkAnalysis of Traffic Flow●

Capacity/demand
imbalances●

Congestion●Delays

EUROPEJAPAN[PRR 2001-2007], [NextGen 2007]

Analysis of Traffic Flow●EUROPE

Capacity/demand
imbalances●

Congestion●

DelaysATM Performance● Flow Management●

JAPAN[PRR 2001-2007], [NextGen 2007]

Flight Plan Processes
FDPde-tour
NOTAM

Airline
AMMeteoATMCATCIM...

IMATCIM

ATCRDP-2h-30min

EDCTTouchdownt[AIP 2008, AIMJ 2008]

Flight Plan Processes
FDPde-tour
NOTAM

Airline
AMMeteo

ATMC

ATCIM

...

IMATCIM

ATCRDP-2h-30min

EDCTTouchdownt[AIP 2008, AIMJ 2008]

Definition: Traffic Gaps
●Uncertainty Factors

known– Demand Uncertainty– Capacity Uncertainty– Flow Control

Uncertainty

unknown●Traffic Gaps
– Differences between

Traffic Gaps

planned and delivered
traffic at sector entries[Ball et al. 2005], [Eurocontrol 2002]

Definition: Traffic Gaps
●Uncertainty Factorsknown

For an en-route sector:– Demand Uncertainty– Capacity Uncertainty– Flow Control

Traffic Gaps

Uncertainty
PLN_t : number of planned entries in time slot t (before take-off)●TrafficREAL_t :Gapsnumber of observed entries(radar data)●unknown●– Differences between

Traffic Gaps

planned and delivered
traffic at sector entries[Ball et al. 2005], [Eurocontrol 2002]

Trend Analysis

Trend Analysis
GAP

●

●

1144

288

GAP

●

48

96

124
time-slot

48

Night/Dayvariance
A peak in themorningGAP

1

Fluctuation ~ 0X-axis: time slot (1 day)Y-axis: gaps between planned and observed traffic
Top: 5 min, middle: 15 min, bottom: 30 min[Gwi et al. 2007, 2006][Wanke et al. 2006, 2005]

Trend Analysis
GAP

●

●

1144

288

GAP

●

Night/Dayvariance
A peak in themorning48
no daily repeating
patterns96● no unexpected distributions (not shown)
●

GAP

1

Fluctuation ~ 0

1

19
time-slot

X-axis: time slot (1 day)Y-axis: gaps between planned and observed traffic
Top: 5 min, middle: 15 min, bottom: 30 min

48[Gwi et al. 2007, 2006][Wanke et al. 2006, 2005]

REAL

REAL

Trend Analysis

PLN●

Distribution

PLN●

Average value– One single peak

– Systematic gaps

– Poisson (per column k)– As a function of density[GwiDuo 2006, GDN 2008]

Trend Analysis

REALREAL}

Controller workload ?
● Unused capacity
●

PLN

●

Distribution

PLN●

Average value– One single peak

– Systematic gaps

– Poisson (per column k)– As a function of density[GwiDuo 2006, GDN 2008]

Trend Analysis●REALREAL}

Controller workload ?
● Unused capacity●Descriptive modelPLN●

Distribution−k nke PREAL=n| PLN=k =n!
logk =kPLN●●

Causal model

Average value([Gwi 2007])– One single peak

– Systematic gaps

– Poisson (per column k)– As a function of density[GwiDuo 2006, GDN 2008]

Propagation of Gaps

Correlation Analysis
S1ij  k=●Propagation:●

Compensation: 0S3

0ij k[ii 0  jj 0]1 /2

i , j : sectors
k : time lag : cross−covarianceS2“ Traffic gaps propagate naturally
through the network, because
aircraft cannot stand still.”“ The question is whether controllers
have strategies to compensate
the gaps.”[Gwi, Kage, Naga 2008]

Correlation Analysis●S1

S3

Propagation:

0Compensation: 0● No negative correlation found k●

ij  k=ij[ii 0● jjNo
0]1 /2unexpected propagation
found“ Traffic gaps
propagate naturally

i , j : sectors
k : time lag : cross−covarianceS2

through the network, because
aircraft cannot stand still.”“ The question is whether controllers
have strategies to compensatethe gaps.”[Gwi, Kage, Naga 2008]

Visualization

non- local
local

Propagation along major routes● No unexpected propagation●

Visualization

EUROPEJAPANComparison JP / EU

Comparison JP/EU
Technique

Attribute

Multi Time plot Mean
Var (night/day)Distribution
Core(abs, rel) Mean
Variances
Tails(cond) Poisson
Trend
Log-like
Propagation TimeSpace

Data
similarsimilarsimilarsimilardifferdiffersimilarsimilarsimilarsimilar

Airspace●

Similarities– Procedures– Mechanisms– Equipment●

Differences– Flow patterns– ATFM– Culture

Comparison JP/EUTechniqueAttribute

DataAirspace●
Multi Time plot Meansimilar
Similarities
Var (night/day) similar– Procedures● All analyzed major trends are similar
DistributionCoresimilar– Mechanisms● Tails of distributions show minor differences(abs, rel) Meansimilar● Differences
Variancesdiffer– Flow patterns
Tails
differ– ATFM(cond) Poisson
similar
Trend
Log-likesimilar– Culture
Propagation Timesimilar
Spacesimilar

Conclusions●

Data Analysis of Traffic Gaps
– Systematic Gaps●the more planned, the less arrive●largely due to randomness– Propagation●

only along major routes

Conclusions●

Data Analysis of Traffic Gaps
– Systematic Gaps●

the more planned, the less arrive●

largely due to randomness– Propagation●●

only along major routes

Comparison Japanese / European Data
– All analyzed major trends are similarFuture Work●

Flow Analysis– density / delay relationship– model of air traffic flow●

Flow Optimization– trajectory based ATM– understandable decision support[Bayen et al. 2005], [Helbing 2001][Menon et al. 2004]

Thank you for yourattention.
Please ask your questions!