８.福岡 FIR における 2013 年から 2030 年の空港需要予測
航空交通管理領域

1. INTRODUCTION
In a research project looking at Trajectory-BasedOperations (TBO), we created forecasts of air traffic
in the Fukuoka Flight Information Region (FIR) in
2030 to identify potential bottlenecks and capacity
insufficiencies. As part of this effort, we examined
airport and runway demand at the eight busiest
airports shown in Table 1, and have reported
preliminary results in [1]. In this paper, we present
our methodology and results in further detail.
Table 1: Target airports
ICAO CodeAirport NameRJAANarita International

RJBB

Kansai International

RJCC

New Chitose International

RJFF

Fukuoka

RJGG

Chubu Centrair International

RJOO

Osaka International (Itami)

RJTT

Tokyo International (Haneda)

ROAH

Naha

To create the 2030 air traffic scenarios, we applieda traffic growth forecast to 2013 air traffic data [2].
The forecast divided air traffic into flights between―regions‖ that are either states or groups of statesaggregated roughly by economic characteristics, andgives traffic growth factors as multipliers of thenumber of flights between regions from 2013 to 2030.
To forecast an airport’s future traffic, one could
simply take annual traffic counts for 2013 between
the airport and each region, multiply these by thecorresponding forecast traffic growth factors and sum.
This requires complete traffic statistics for each
airport, but such data may not be so readily available.
For this study in particular, we had data on all flights
in Fukuoka FIR on each day in 2013 except for※ブラウン マーク，平林 博子

February. Also, our available data contained only
flights conducted under Instrument Flight Rules (IFR),
omitting Visual Flight Rules (VFR) traffic.
We therefore used a simple method to estimate
airport annual air traffic and to forecast future traffic.
We selected two scenario days from 2013 which we
assumed to be representative of summer and winterschedule season’s traffic, multiplied the IFR traffic onthese days by six months and summed to obtain
estimated annual totals for each airport. We thencompared these estimates with the true annual traffic
totals from landing statistics. If the selected days
were indeed average, we assume that any significant
error would be due to ―missing‖ VFR flights, andderived ―correction factors‖ for significant errors.
Next, we applied our traffic growth forecast to the2013 scenarios to obtain 2030 traffic scenarios usinga ―copy-and-shift‖ method whereby growth traffic is
added by taking random flights from the original
scenario and shifting their times by ±20 minutes toavoid overconcentration of traffic while preserving
the airport's characteristic traffic flow peaks. Finally,
we applied our annual traffic estimation method to
the 2030 scenarios including the correction factors,
assuming VFR traffic growth to be static.
In this paper, we present our method to derive
annual airport traffic estimates for 2030 and show its
results. We also derive the typical runway demand(movements/hour) using histograms. Section 2
describes our data sources and methodology toestimate annual airport traffic, the traffic growthmodel and its application to create 2030 scenarios,
and runway demand analysis method. The results arepresented in section 3 and discussed briefly insection 4. Section 5 concludes the paper.
Note that the results presented here are based on
2013 information, and actual 2016 traffic was greaterthan anticipated. The emphasis of this paper is on ourmethodology, and the results should be revised withlater forecast data for practical use.
2. METHODOLOGY
2.1 Source Information
This study used Flight Data Management System(FDMS) data provided by the Japan Civil Aviation
Bureau (JCAB). The data contains flight plan andoperational information (including Actual Time of
Departure (ATD) and Actual Time of Arrival (ATA)at Japanese airports) for IFR flights that operated in
the Fukuoka FIR, omitting VFR and military flights.
Available data included all days in 2013 except
February, and complete data for 2014 and 2015.
Baseline traffic scenarios were derived from twodays, 11 Jan 2013 in the winter and 6 Sep 2013 in thesummer airline schedule periods respectively,
selected because that they contained no significant
weather that would cause delays. True traffic totals
for each airport (numbers of movements, where a
movement is a takeoff or a landing) were derived
from airport statistics compiled and published by
JCAB [3]. The data gives numbers of landings ofcivil IFR and VFR flights at each airport, as well as
passenger and freight volumes.
Our traffic growth forecast [2] was derived fromexisting sources. For domestic and internationaltraffic, we used JCAB forecasts based on research by
the National Institute for Land and InfrastructureManagement (NILM).
2.2 Estimation of Annual Airport Traffic
In Japan, airlines typically operate two schedule
periods — summer and winter — coinciding withdaylight saving time in Western Europe [4]. Overall
demand is higher during the summer than in thewinter. To estimate annual demand, we selected one
day from each schedule period that had no significant
weather-related delays and assumed these days to be―typical‖ of each approximately 6-month period. Let
us denote the airport traffic (number of movements)on these days as w for the winter period day and s forthe summer period day. Assuming these as averages,

an estimate of annual traffic Test is
Test = (365/2) (w + s)

(1)

If historical data are available on actual traffic, we
can find the estimate errors. In this study, we usedlanding statistics for each airport. If the number oflandings at an airport is L, then the actual annual
number of movements Tact is approximately given by
Tact = 2L(2)

assuming the number of aircraft parked at an airport
remains constant over time. The error factor e
between the actual and estimated traffic is thene = (Test – Tact) / Tact(3)

Since our w and s include only IFR flights, if they areclose to seasonal average then we assume that any
significant e is due largely to VFR traffic.
To obtain an airport annual traffic forecast, we firstcreate ―growth‖ traffic scenarios from the historicalbaseline day traffic and obtain the corresponding w
and s for the forecast year. We then use eq. (1) toobtain an annual traffic estimate and then correct for
the VFR traffic to obtain a prediction Tpred as
Tpred = Test (1 – e)(4)assuming that annual VFR traffic does not changeover the forecast period. Our assumptions are
summarised in Table 2.
Table 2: Annual traffic estimation assumptions
1

The one-day traffic totals w and s are typical(averages) for the summer and winterschedule 6-month periods respectively.

2

Significant discrepancy e between Tact andTest is largely due to ―missing‖ VFR traffic.

3

VFR traffic at an airport will not change
significantly over the forecast period.
Therefore, e can be used to correct a forecast
Test to derived a better traffic prediction at
airports with significant VFR traffic.

2.3 Traffic Growth Model and its Application
Our traffic growth model treats states or aggregatesof states as ―regions‖ are shown in Table 3 and gives
forecast growth factors of the numbers of flightsbetween them from 2013 to 2030. The growth factors
are based on forecast passenger and freight volumes
considering economic growth and are converted into
numbers of flights assuming trends such as a
reduction of average aircraft seating capacity and thegrowth of low-cost carriers. Japanese domestic flights
are between the same region (JP–JP).
Table 3: Regions for traffic growth forecast
CodeStates/Countries

KOR

Republic of Korea

CHN

People’s Republic of China &Given a baseline traffic scenario S and a set ofgrowth factors c(RA, RB) between regions RA and RB,
we derive a forecast traffic scenario S’ by a ―copy andshift‖ procedure. For each region pair (RA, RB), let
the set of flights from RA to RB in S be F(RA, RB) ={ f1, f2, … fn } and let F’(RA, RB) be thecorresponding traffic flow in the forecast scenario. If
there are n flights in F, the number of flights n’ in F’
is obtained as n’ = ceil (c(RA, RB) . n) where ceil (x)is the smallest integer greater than x. To make F’, wetake the set of n flights from F and add ―growth‖traffic, that is

Mongolia, excluding Taiwan.
TW

Taiwan.

OCE

Oceania including Hawai'i.

SWASIA

India, Nepal and the Middle East.

ASEAN

Association of Southeast Asian
Nations.

EUR

Europe, excluding CIS states.

CIS

Commonwealth of Independent
States.

NAM

Canada, the contiguous UnitedStates and Alaska.

JP

Japan.

Figure 1 shows the overall traffic growth in the
Fukuoka FIR. Data for 2011 and 2012 are historical,
2013–2030 are forecast. Domestic growth is largely
flat (around 6%) while international traffic (between
an airport in Japan and overseas) grows by ~80% andoverflights grow by ~70%. Growth factors are givenin [1].

F’ = { f1, f2, … fn, fn+1, …fn’}
where the ―growth‖ traffic fn+1… fn’ is created asfollows:
1. Pick a random flight fk ∈ F where k = rand(0, n)
and rand(a, b) is a function that gives a random
integer in the interval (a, b)
2. Create a flight f’ = shift (fk, rand(-20,20)) where
shift (a, b) is a function that shifts the arrival anddeparture times of flight a by b minutes.
3. Add f’ to F’.
4. Repeat steps 1–3 until there are n’ flights in F’.
Since growth traffic is selected randomly, each S’
differs slightly. We therefore created 50 2030 forecast
scenarios corresponding to each 2013 baseline
scenario and took the averages.
The forecasts have the following characteristics:The growth traffic is added between existing city
pairs. We do not model the creation of routes.
The ratios of traffic between each airport and aregion remain constant; that is, the traffic growthmodel does not account for competition for
traffic between airports or changes in airport
preference.

2.4 Runway Demand
To examine "typical" runway demand during
normal operations, we plotted histograms of ATA and
ATD for each airport binned at one-hour intervals foreach schedule season.
Figure 1: Overall traffic growth forecast

Table 4: Estimated annual traffic and growth for each airport
AirportTest (2013)

Tact (2013)e

Test (2030)

RJAA

228,308

233,3882.2%

383,980

RJBB

141,255

131,930

–7.1%

RJCC

137,780

134,312

RJFF

157,681

RJGG

Tpred (2030)

Growth0383,98072%

223,198–7.1%

216,641

64%

–2.6%

148,7380148,74811%

170,640

7.6%

183,595+7.6%

197,548

16%

86,50588,5782.3%

116,983

0116,983

32%

RJOO

129,210

136,132

5.1%

136,510+5.1%

143,4725%

RJTT

395,113403,2422.0%

448,403

0448,403

11%

ROAH

120,450

147,30218.2%

135,233+18.2%

159,845

9%

We assume that the pattern of runway demand oneach 2013 scenario day is ―typical‖, and verified thisby comparing to the median and maximum runway
demand in each one-hour bin for all days in theschedule periods for which data were available. (Weselected the median rather than the mean because of
its lower sensitivity to outliers that may occur whennormal operations are disrupted.) For 2030 demand,
we averaged the demand in each histogram bin overthe 50 2030 summer and 50 2030 winter scenarios.

Corr.

traffic shown in Table 4. The actual 2013 andpredicted 2030 traffic are shown in Figure 3.

3. RESULTS
3.1 Annual Airport Traffic
Figure 2 shows actual annual airport traffic Tact andestimated traffic Test for 2013. The values and errors efor 2013 are given in Table 4. The error magnitudes
are within 5% except for RJBB, RJFF and RJOO and
ROAH, and are within 10% except for ROAH.
Comparing FDMS records for IFR flights 2014 and2015 with landing statistics (which include VFR
flights) reveals differences of around 6.6% for RJFF
and 18.5% for ROAH. These figures are consistent
with the RJFF and ROAH errors in Table 4, and weassume these errors are due largely to VFR traffic.
The RJBB and RJOO errors are largely due to thedifference between traffic on the scenario days andthe true seasonal averages.
Taking estimates from the averaged 2030 scenarios,
we applied corrections for errors in the 2013 data
greater than 5% and derived forecast annual predictedtraffic Tpred for 2030 and the growth over the 2013

Figure 2: Annual traffic Test and Tact for 2013

Figure 3: Actual 2013 and predicted 2030 annual
traffic
3.2 Runway Demand
Figure 4 shows histograms of runway operations(ATA and ATD) at RJAA for the 2013 winter andsummer scenario days with ―error bars‖ showing the
maximum and median numbers of movements ineach bin time interval during the schedule season.
The closer the top of each bar is to the median (thebottom of the error bar), the nearer to seasonal
average operations were during the scenario day.
Operations at RJAA on the scenario days are close to
average except for 18:00-19:00JST in the summer,
where the traffic is at the schedule period maximum.
The results for the other airports are similar. We
conclude that the scenario days are reasonably
representative of runway operations during the 2013
winter and summer schedule seasons.
Histograms for all airports are omitted due to lackof space, but as a typical example Figure 5 shows the
RJAA runway demand histograms for the winter2013 scenario day (top) and the average of the 2030
winter scenario days (bottom), with departures andarrivals indicated separately.
From these histograms, we extracted the peakhourly values from the summer and winter scenarios
to determine the typical peak required runway
throughput during normal operations for each airport.
The results are shown in the summary table, Table 5.

Figure 5: RJAA runway demand for winterscenarios: 2013 (top), 2030 (bottom)
3.3 Results Summary

Figure 4: RJAA runway demand for winter (top)and summer (bottom) 2013 scenario days withschedule period median and maximum

Table 5 summarises our airport traffic forecast
results. 2030 traffic values are Tpred from Table 4
rounded to the nearest multiple of 1,000. The
numbers of runways at each airport and the 2016
operating times are shown to help judge whether
airport capacity will be sufficient. For ease ofcomparison, the 2013 and 2030 traffic are plotted in
Figure 6 with the airports in reverse 2013 rank order.
The changes in peak hourly movements from 2013 to2030 are plotted in Figure 7.
Figure 8 plots proportions of international anddomestic traffic in Test. (Note that these are notcorrected for VFR traffic, which is entirely domestic.)
The mainly domestic airports (RJTT, RJOO, ROAH)
have only slight growth (<15%) while airports withsignificant international traffic grow more rapidly.

Table 5: Summary results
Airport

2030

2013

GrowthRunways

OperatingTime (JST)Traffic

Peak/hrTraffic

Peak/hr

RJAA

233,38863

384,000

101

72%

2 (3?)

06:00–23:00

RJBB

131,93036

217,000

59

64%

2

24hr

RJCC

134,31233

149,000

36

11%

2

24hr

RJFF

170,64040

198,00046

16%

1 (2 planned)

07:00–22:00

RJGG

88,57823

117,000

34

32%

1

24hr

RJOO

136,13238

143,000

39

5%

2

07:00-21:00

RJTT

403,24278

448,000

88

11%

4

24hr

ROAH

147,30235

160,00039

9%

1 (2 planned)

24hr

Figure 6: Traffic growth from 2013 to 2030 plottedin reverse rank order for 2013

Figure 8: Test for 2013 and 2030 with proportionsof domestic and international traffic
In particular, traffic at RJAA is expected to increaseby >70%, and RJAA will remain the top international
airport in Japan, while RJBB will grow by more than
60% as the international gateway for the Kansai
region. RJGG will grow by over 30% but the overall
traffic level is 2030 is still only relative modest at
117,000 movements per year.
4. DISCUSSION

Figure 7 Peak hourly movements at each airport
for 2013 and 2030

We now discuss airports in more detail based on
our forecast and add speculations regarding theability of airports to satisfy future demand. Capacity
constraints differ at each airport and include
environment (noise), air traffic control andmanagement, parking and terminal constraints [4],but we do not consider these comprehensively in thisbrief assessment.
4.1 RJCC, RJGG, ROAH
The projected demand for these three airports
appears to be within capacity given the available andplanned runways and their 24-hour opening(indicating fewer noise restrictions), provided thatterminal and parking capacity are sufficient.
RJCC serves the metropolitan area of Sapporo,
Hokkaido's largest city. Although the airport is inland,
the surrounding population density is relatively low
which allows it to operate 24 hours. In 2013, onlyaround 5% of its traffic was international, and overall
growth to 2030 is expected to be ~11% to 149,000
movements/year. There is almost constant hourly
demand between 08:00 and 22:00 and a peak of lessthan 40 movements/hour.
RJGG was built on an artificial island to replace
Nagoya Airport (RJNA), which is in a built-up area.
It has a single runway and 24-hour operation. Itsinternational traffic means that it will grow by ~32%
from 2013 to 2030, needing a capacity of around
117,000 movements a year and runway capacity ofaround 35 movements/hour from its single runway tohandle a traffic peak between 09:00 and 10:00 JST.
ROAH serves the island of Okinawa and is a hub
for short services to smaller islands in the Ryukyu
archipelago. The airport is 24-hour and there areplans for a second runway. We expect traffic to growfrom around 147,000 movements in 2013 (including~18% VFR traffic) to around 160,000 movements in
2030 assuming the proportion of VFR traffic remains
the same. There is a single traffic peak in the middleof the day that in 2030 is expected to be around 40
movements/hour in the summer (excluding VFR). A
second runway could better allow VFR traffic tocoexist with IFR traffic during peaks by segregation.
4.2 Kanto Area (RJAA, RJTT)
RJAA is expected to remain Japan’s primaryinternational airport and will need to handle around
384,000 movements in 2030. Because it is built
inland, noise concerns mean that it does not have
24-hour operation. To help meet demand, a third

terminal opened in 2015 and there are proposals toconstruct a third runway. Figures 4 and 5 indicate a
mid-afternoon arrival peak around at 15:00 and an
evening departure peak around at 18:00. There iscurrently an agreement with local communities tolimit movements to 68/hour, but our analysis
indicates that peaks could exceed 100/hour in 2030,
and could exceed the current cap even if the traffic
could be evenly redistributed within the current
operating hours. Since RJAA has many long haul andconnecting flights, the scope for redistributing flights
may be limited.
RJTT will remain Japan’s busiest airport andprimary domestic hub. Our forecast shows ~11%
growth to 2030. Hourly runway demand is much
more constant than at RJAA during daylight hours,
and peak demand grows from 78 movements/hour to
88 movements/hour divided between four dependent
runways.
A limitation of our traffic growth model is that it
does not take into account factors that could alter the
distribution of traffic between airports. RJTT is closer
to Tokyo, Kawasaki and Yokohama than RJAA andis hence more convenient, and its location in Tokyo
Bay means that it can operate with fewer noise
constraints. Its international growth is being actively
promoted, and will be largely at the expense of RJAA.
However, although demand is high, internationaltraffic at RJTT has not grown as much as expected
because of difficulties in increasing capacity. To meethigh demand at both RJTT and RJAA, the applicationof recategorised wake turbulence separation standards,
dynamic wake vortex separation and GBAS curved
approaches could help increase runway capacity andreduce community noise.
4.3 Kansai Area (RJBB, RJOO)
RJOO, an inland airport in a heavily built-up area,
was the primary international and domestic airport for
Osaka and the Kansai region prior to the constructionof RJBB on an artificial island. It was intended toclose RJOO after RJBB opened in 1994, but RJOO
remains open since it is more convenient to reachfrom Osaka city and it remains as a domestic airport,
while RJBB serves as Kansai's international airport.
We expect RJOO to continue to handle most of theregion’s domestic traffic, and since it will grow only
slightly from around 136,000 movements in 2013 to
143,000 movements in 2030, capacity should not be
an issue.
While RJBB has only around a third of thedomestic traffic of RJOO, it will grow by more than
60% due to international demand, requiring some
217,000 movements in 2030 and a peak runway
demand of around 60 movements/hour. These aresimilar to RJAA traffic in 2013, but we have notconsidered terminal or parking capacity. In recent
years, RJBB traffic has been growing more rapidly
than expected, particularly international flights to
Asia and with low-cost carriers.
4.4 Kyushu (RJFF)
RJFF is the largest airport in Kyushu and serves thearea around the Fukuoka metropolis. It has a single
runway and expansion is constrained by its location
in a built-up area and nearby mountains. Operating
hours are between 07:00 and 22:00. The airport is
expected to grow by ~16% from 171,000 movements
in 2013 to 198,000 movements in 2030. There are
two peaks in demand, 10:00-11:00 and 17:00-18:00,
and peak traffic is expected to increase from 40
movements/hour to 46 movements/hour. To meetdemand, an additional dependent runway is planned
within the airport’s existing boundary, but space for
additional terminal and parking capacity is limited.
As we have seen, RJBB, RJGG were built on
artificial islands to relieve airports with limited spacefor growth in noise-sensitive areas. Kitakyushu
airport (RJFR), built on an artificial island, opened in2006 and is around 65 km from RJFF. However, it isa 40-minute bus journey from RJFR to Kokura
railway station and an additional 30 minutes from
Kokura to Hakata station in Fukuoka city centre. In
contrast, Hakata station can be reached from RJFF in
6 minutes. It will therefore be hard to offload traffic
from RJFF to RJFR to compete with RJFF without

significant incentives.
5. CONCLUSION
We present a forecast of airport demand for theeight busiest airports in Japan for 2030, based on a2013 forecast. Due to a lack of complete data in our2013 baseline, we devised a method to estimate
annual traffic demand from two assumed typical days.
We applied the method to averaged traffic growthscenarios, correcting for "missing" VFR traffic using
historical data, to obtain traffic predictions.
It should be noted that our traffic growth model
was based on 2013 projections and in fact, overall
growth in 2016 has been greater than expected. The
forecast should therefore be revised with later data.
A limitation of our forecast is that it does not take
into account factors such as policy and competition
that alter the distribution of traffic between airports.
However, planning does not always give the desired
outcomes.
ACKNOWLEDGEMENT
The authors thank JCAB for providing the data forthis study, and colleagues Mrs. H. Aoyama and
Mr. I. Yamada for airport capacity discussions.
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[2] H. Hirabayashi, M. Brown, Y. Fukuda, S. Inoueand S. Nagaoka: A Study of Flight Scenario
based on Air Traffic Demand Forecast.
51st Aircraft Symposium, November 2013.
[3] Airport Management State, Japan Civil Aviation
Bureau http://mlit.go.jp/koku/15_bf_000185.html[4] Outline of Japan Schedule Coordination (JSC),
2016 Edition. Japan Schedule Coordination.
http://www.schedule-coordination.jp/about/pdf/brochure(ENG).pdf