Airport Performance Model
Edition No.
:
1.400
Draft
Edition Issue Date
:
24 Nov 2008
Author
:
Robert Wigetman
Reference
:
GF/WKFF/004
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Document Title:
Document Reference:
Airport Performance Model
GF/WKFF/004
Table Of Contents
1
Goal ................................................................................................................................... 1
2
Performance Model .......................................................................................................... 1
2.1 Principals & Assumptions.............................................................................................. 1
2.2 Presentation ................................................................................................................. 2
3
Visualizations.................................................................................................................... 5
4
To Do ................................................................................................................................. 5
Table Of Figures
Figure 1: Performance vs. RWS Configuration............................................................................ 2
Figure 2: Performance vs. RWS Configuration with Fog ............................................................. 2
Figure 3: Throughput vs. RWS Configuration .............................................................................. 3
Figure 4: Throughput vs. RWS Configuration with Fog ............................................................... 3
Figure 5: Performance vs. RWS Configuration with Fog & Cross-Wind....................................... 3
Figure 6: Throughput vs. RWS Configuration with Fog & Cross-Wind ......................................... 4
Table Of Tables
Table 1: Actions To Do & Done ................................................................................................... 5
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1
GF/WKFF/004
Goal
(1)
Our goal is to provide a means to measure individual airport performance in such a way that
quantitative comparison of performance could be performed between different airports.
(2)
The requirements for this measurement are:
a)
airport performance must take into account individual airport's specific characteristics, e.g.
runway system configurations, weather, planned & actual (peak) throughput, etc.
b)
performance measurements must be comparable between airports, i.e. some form of
normalization must be applied.
c)
airport performance must be de-coupled from ATC capacity and performance of the
surrounding sectors, including the TMA.
d)
a model of disturbance phenomena (e.g. inclement weather, unplanned equipment
maintenance or failure, etc.) must be provided so that disturbance levels can be shown
and compared across airports and/or runway system configurations.
(3)
It would be desirable that the performance measurement were presented in such a way as to
enable a visual correlation of performance to disturbance phenomenon.
(4)
In view of this goal we will develop some ideas and present them in this note, supported by a
web-based demonstration of the results.
(1)
(2)
2
Performance Model
2.1
Principals & Assumptions
We suggest the following basic principals:
a)
Airport Performance is measured as the percentage of actual (i.e. observed) runway
system throughput relative to the nominal (or peak?) runway system throughput.
b)
Runway system throughput is systematically correlated to runway system configuration.
c)
Disturbance phenomena are characterized by a severity value on [-10, 0] which is
relative to the individual phenomenon, but this value is proper to the type of disturbance
and would not necessarily enable impact comparison of different types of disturbances,
e.g. bird presence severity -10 would probably have less impact than a snow-storm of
severity -10.
d)
Each runway configuration is characterized by a mapping such that each disturbance
phenomenon, with its associated severity, maps to a negative impact factor i.e. how much
negative influence this phenomena has on the airport runway system configuration's
throughput. This is a heuristic, or empirically determined value which could be one of the
first results of this study.
We can then apply these principals on two data sets:
a)
Past or historical data:
i)
providing performance measurements over time,
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Airport Performance Model
ii)
b)
GF/WKFF/004
presenting potential correlation between performance and disturbance phenomena
enabling the computation or refinement of disturbance impact factors on runway
system configurations.
Future or forecast data:
i)
2.2
predicting future performance based on forecast runway system configuration and
disturbance phenomenon (known from METAR/TAF and other sources such as
maintenance schedules, social action warnings, etc.).
Presentation
Hourly Performance
100%
RWS
Performance
(%)
10
8
6
4
2
0
-2
-4
-6
-8
-10
75%
50%
25%
0%
-25%
-50%
-75%
-100%
00:00
02:00
04:00
06:00
08:00
10:00
12:00
14:00
16:00
18:00
20:00
Fog Severity
RWS Bravo
RWS Alpha
RWS Actual/Nominal Throughput (%)
Disturbance
Severity
22:00
Hour
Figure 1: Performance vs. RWS Configuration
(1)
The above example shows the hourly performance for an airport with two RWS configurations
(called Alpha and Bravo) over one day. No disturbance phenomenon are presented. We have
assumed that RWS configuration Alpha provides the nominal throughput, whereas Bravo
provides approximately 90% of that of Alpha. In the next example, we will introduce a
disturbance.
Hourly Performance
100%
RWS
Performance
(%)
10
8
6
4
2
0
-2
-4
-6
-8
-10
75%
50%
25%
0%
-25%
-50%
-75%
-100%
00:00
02:00
04:00
06:00
08:00
10:00
12:00
14:00
16:00
18:00
20:00
Fog Severity
RWS Bravo
RWS Alpha
RWS Actual/Nominal Throughput (%)
Disturbance
Severity
22:00
Hour
Figure 2: Performance vs. RWS Configuration with Fog
(2)
This second example shows how the same RWS configurations behaved during fog of
severity -2. We observe that the fog was present from 8:00 to 16:00 and of constant severity
during that period. We also observe a reduction in RWS performance which was expected. The
graphics allow for easy correlation of the drop in performance to the fog phenomenon.
(3)
In the next charts we present the same information in terms of throughput, i.e. movements/hr.
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Hourly Throughput
vs.
Disturbance Severity & RWS Configuration
Throughput
(mvts/hr)
20
10
15
8
6
10
Fog Severity
4
5
2
0
0
-5
-2
-4
-10
-6
-15
RWS Bravo
RWS Alpha
Actual RWS Throughput (Hourly)
Nominal RWS Throughput (Hourly)
Disturbance
Severity
-8
-20
-10
00:00
02:00
04:00
06:00
08:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
Hour
Figure 3: Throughput vs. RWS Configuration
(4)
(5)
The chart in Figure 3 requires some explanation:
a)
Nominal RWS throughput is taken to be the maximum number of movements/hr for the
best performing RWS configuration, tempered with what the airport is capable of handling
at the given time. For example, the example airport is only capable of 5 mvts/hr during the
night under best of circumstances.
b)
The yellow curve shows the actual observed throughput. We see that at 12:00 the actual
dropped below the nominal. We could easily correlate this to the change in RWS
configuration. Indeed, in our example, RWS Bravo has 10% less potential throughput
than RWS Alpha.
Now let’s again add the fog at severity -2.
Hourly Throughput
vs.
Disturbance Severity & RWS Configuration
Throughput
(mvts/hr)
20
10
15
8
6
10
Fog Severity
4
5
2
0
0
-5
-2
-4
-10
-6
-15
RWS Bravo
RWS Alpha
Actual RWS Throughput (Hourly)
Nominal RWS Throughput (Hourly)
Disturbance
Severity
-8
-20
-10
00:00
02:00
04:00
06:00
08:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
Hour
Figure 4: Throughput vs. RWS Configuration with Fog
(6)
The chart in Figure 4 allows us to see the correlation between the fog and the reduction in
observed throughput which began at 8:00. We note that despite the fog, throughput increased
slightly around 11:00 probably due to the general increase in nominal throughput for the airport
at that time.
(7)
In the next example we will present a day with multiple disturbances.
Hourly Performance
100%
RWS
Performance
(%)
10
8
6
4
2
0
-2
-4
-6
-8
-10
75%
50%
25%
0%
-25%
-50%
-75%
-100%
00:00
02:00
04:00
06:00
08:00
10:00
12:00
14:00
16:00
18:00
20:00
Cross-wind Severity
Fog Severity
RWS Bravo
RWS Alpha
RWS Actual/Nominal Throughput (%)
Disturbance
Severity
22:00
Hour
`
Figure 5: Performance vs. RWS Configuration with Fog & Cross-Wind
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(8)
GF/WKFF/004
Figure 5 shows how simultaneous disturbances can be visualized. Here we can see the
correlation between the combined presence of fog and cross-wind on RWS performance.
Hourly Throughput
vs.
Disturbance Severity & RWS Configuration
Throughput
(mvts/hr)
20
10
15
8
10
6
Cross-wind Severity
4
Fog Severity
2
RWS Bravo
5
0
0
-5
-2
-4
-10
-6
-15
RWS Alpha
Actual RWS Throughput (Hourly)
Disturbance
Severity
Nominal RWS Throughput (Hourly)
-8
-20
-10
00:00
02:00
04:00
06:00
08:00
10:00
12:00
14:00
16:00
18:00
20:00
22:00
Hour
Figure 6: Throughput vs. RWS Configuration with Fog & Cross-Wind
(9)
Figure 6 shows the same situation but presents RWS throughput rather than performance.
(10)
Performance data may be aggregated and presented at a higher level. In the following
examples, we have chosen to visualize airport runway system performance over an entire
AIRAC cycle. The presentation logic in Figure 7 and Figure 8 is similar to that presented earlier.
Daily Performance
100%
RWS
Performance
(%)
10
8
6
4
2
0
-2
-4
-6
-8
-10
75%
50%
25%
0%
-25%
-50%
-75%
-100%
1
3
5
7
9
11
13
15
17
19
21
23
25
Avg Total Disturbance Severity
RWS Bravo
RWS Alpha
Avg RWS Actual/Nominal Throughput (%)
Disturbance
Severity
27
Day in AIRAC Cycle
Figure 7: Performance vs. RWS Configuration aggregated over an AIRAC cycle
Average Daily Throughput
vs.
Disturbance Severity & RWS Configuration
RWS Bravo
25
20
Throughput
15
(mvts/hr) 10
5
0
-5
-10
-15
-20
-25
10
8
6
4
2
0
-2
-4
-6
-8
-10
1
3
5
7
9
11
13
15
17
19
21
23
25
RWS Alpha
Avg Total Disturbance Severity
Avg Actual RWS Throughput (Hourly)
Avg Nominal RWS Throughput (Hourly)
27
Day in AIRAC Cycle
Figure 8: Throughput vs. RWS Configuration aggregated over an AIRAC cycle
(11)
We believe that the charts presented above provide good visualizations of the multivariate
situation comprising:
a)
nominal throughput,
b)
actual (observed) throughput,
c)
runway system configurations,
d)
disturbance phenomena identification and severity,
e)
time.
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Visualizations
(1)
We suggest that the ideal presentation form would be web based, such that a click on the chart
would provide a drill-down into the specific day clicked. We would also provide drill-up to bring
the viewer to the next lower granularity view – in the above example this would bring up a view
over several AIRACs or more.
(2)
In the event that an airport had several RWS configuration, these would be indicated. The
viewer could select to view performance data for one or several configurations or all.
(3)
We would also provide the viewer with the ability to select to overlay multiple airports on the
same chart (an airport selection mechanism would be required).
(4)
Some of these features are available in the demo at:
http://gratefulfrog-ap-demo.appspot.com/.
4
Status
To Do
done
Action
document the demo spreadsheet fields, particularly on the RWS Configuration
test sheet, but all are needed.
update the web-type AIRAC demo
done
make charts showing view on the 28 day averages.
done
Create more realistic demo data.
done
include charts multiple disturbances on the same day,
done
Update all the terminology:
 throughput, peak throughput, average throughput; instead of capacity,
 Runway System, Runway System Configuration,
 disturbance instead of disruption.
done
Fuzz the correlation between disturbance and performance in the demo!
Create a way of representing RWS configuration, with a correlation to planned
(peak) throughput.
Table 1: Actions To Do & Done
done
done
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