Connectivity Developments in Air Transport Networks at ...
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Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019
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Connectivity Developments in Air Transport Networks at Primary Asian
Airports
Hidenobu MATSUMOTO a, Koji DOMAE b
a Graduate School of Maritime Sciences, Kobe University, Kobe, 658-0022, Japan;
E-mail: [email protected]
b College of Global Communication and Language, Kansai Gaidai University,
Hirakata, 573-1008, Japan; E-mail: [email protected]
Abstract: This paper measures and compares the connectivity developments in air transport
networks at the primary airports in Asia. To determine how the connectivity at these airports
has developed in the specific markets, the connectivity figures are broken down by regions.
For an assessment of the model and its application, the paper conducts scenario analyses for
Chubu Centrair International Airport in Nagoya, Japan, on the connectivity impacts of an
additional flight from this airport to large hub airports in Europe, North America and Asia,
and of moving all domestic flights from Nagoya Airfield, the other airport in Nagoya, to this
airport. The results reveal that the most striking growth of air network connectivity
developments has been found at the three airports in Mainland China (Beijing, Shanghai and
Guangzhou) and Tokyo International Airport. The model is helpful for airports to assess their
network performance and their competitive hub status vis-a-vis other airports.
Keywords: Air network performance, Competitive hub status, NetScan connectivity model,
Scenario analysis, Chubu Centrair International Airport, Asia
1. INTRODUCTION
The growth of hub-and spoke operations has changed the competition among airports in a
structural way. Due to the rise of hub-and-spoke networks, airlines compete directly as well as
indirectly. Traditional measures on airport performance, such as passenger enplanements and
aircraft movements, fail to address in particular indirect connectivity via hubs.
To date, many studies have analyzed hub-and-spoke networks. One branch of research
is from the viewpoint of economic perspectives, with a focus on economies of density and
scope (Caves et al., 1984; Brueckner and Spiller, 1994), hub premiums (Borenstein, 1989;
Oum et al., 1995), entry deterrence (Zhang, 1995) and the role of hub-and-spoke networks in
airline alliances (Oum et al., 2000; Pels, 2001). Another branch of research is the field of
operations research, where the cost-minimizing approach is used to determine spatial
optimization of air networks (Kuby and Gray, 1993; O’Kelly and Miller, 1994; O’Kelly and
Bryan, 1998). A third branch uses the geographical approach, in which the structures,
performance and spatial dimension of hub-and-spoke networks are analyzed empirically (Ivy,
1993; Shaw, 1993; Bania et al., 1998; Burghouwt et al., 2003). These studies, however, take
into consideration air traffic flows purely from the demand aspect, without capturing the
airline network structures, schedule coordination and its resulting hub performance from the
supply aspect. Consequently, some studies have included the level of schedule coordination in
the measurement of performance and structure of hub-and-spoke networks. Veldhuis (1997)
Corresponding author.
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analyzes Amsterdam Airport Schiphol, focusing on the quality and frequency of indirect
connections. Burghouwt and Veldhuis (2006) evaluates the competitive position of West
European airports in the transatlantic market from this viewpoint, followed by Burghouwt et
al. (2009) and De Wit et al. (2009) which assess the competitive hub status of primary
airports in East and Southeast Asia.
The main purpose of this paper is to measure and compare the air network performance
and competitive hub status of primary airports in Japan and elsewhere in Asia between 2001
and 2017. Its special focus of attention is Chubu Centrair International Airport in Nagoya,
Japan. In this paper, the NetScan connectivity model is used to measure the connectivity
developments at these airports, taking into account the quantity and quality of both direct and
indirect connections. For an assessment of the model and its application, the paper conducts
scenario analyses for Chubu Centrair International Airport on the connectivity impacts of an
additional flight from this airport to large hub airports, and of moving all domestic flights
from Nagoya Airfield, the other airport in Nagoya, to this airport.
The remainder of this paper is organized as follows. The next section provides an
overview of the NetScan connectivity model. In Section 3, the connectivity developments in
air transport networks at the primary airports in Asia are measured and compared. In Section
4, after describing the dual airports system in Nagoya Metropolitan Area, scenario analyses
are conducted on the connectivity impacts of an additional flight to a large hub airport in
Europe, North America and Asia, and of moving domestic flights from Nagoya Airfield to
Chubu Centrair International Airport, followed by discussion and conclusion in Section 5.
2. MEASUREMENT OF NETWORK QUALITY
2.1 Four Types of Network Connectivity
In our approach, four types of network connectivity are distinguished as described in Figure 1.
1. Direct connectivity: flights between airports A and B without a hub transfer
2. Indirect connectivity: flights between airports A and B, but with a hub transfer at airport H
3. Onward connectivity: connections with a hub transfer at airport B between airports A and D
4. Hub connectivity: connections with a hub transfer at airport A between airports C and B
Figure 1. Four types of network connectivity Note: This paper does not consider onward connectivity.
Indirect connectivity
Onward connectivity
H
Direct connectivity
Hub connectivityC
D
A
A
A
A
B
B
B
B
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The quality of an indirect connection between airports A and B with a hub transfer at
airport H is not equal to the quality of a direct connection between airports A and B. In other
words, the passenger traveling indirectly will experience additional costs due to longer travel
times, consisting of transfer time and detour time. Transfer time equals at least the minimum
connecting time, or the minimum time needed to transfer between two flights at airport H.
The measurement of indirect connectivity is particularly important from the perspective
of passenger welfare; how many direct and indirect connections are available to passengers
between airports A and B? The concept of hub connectivity is particularly important for
measuring the competitive hub status of airports in a certain market; how does airport A
perform as a hub in the market between airports C and B?
2.2 Concept of Connectivity Units
Many passengers transfer at hub airports to their final destinations, even in case good direct
connections are available. Passengers’ choices depend on the attractiveness of the available
alternatives.
When measuring the attractiveness of a certain alternative, we consider frequencies and
travel time. As for fare differentiation, fares on non-stop direct routes are generally higher
than those on indirect routes. Fares on indirect routes are generally lower for on-line (or
code-shared) connections than for interline connections. Fares on a route are generally lower
if more competitors are operating on these routes. And finally, fares are ‘carrier-specific’ and
are depending on the ability of carriers to compete on fares. Therefore, it can be concluded
that fares are generally depending on the number of competitors on the route and the product
characteristics, like travel time, number of transfers, kind of connection (on-line or interline)
and the carrier operating on the route. So, fare differentiation is partly reflected in the route
characteristics.
The route characteristics mentioned are to be operationalized in a variable indicating
connectivity, expressed in so called ‘connectivity units (CNU’s)’. This variable is a function
of frequencies, travel time and the necessity of a transfer.
2.3 NetScan Connectivity Model
The NetScan connectivity model, developed by Veldhuis (1997), has been applied here to
quantify the quality of an indirect or a hub connection and scale it to the quality of a
theoretical direct connection. The model assesses the level of direct connectivity based on the
Official Airline Guide (OAG) flight schedules. Based on the direct connections, the model
builds viable indirect and hub connections. The model weighs these for their quality based on
transfer time and detour time involved, which results in the level of indirect and hub
connectivity provided. Figure 2 shows the scheme of NetScan Model.
First, direct connections have been retrieved from the OAG flight schedules (Step 1).
Then, indirect and hub connections have been constructed using an algorithm, which
identifies each incoming flight at a hub airport and the number of outgoing flights that
connect to it. The algorithm takes into account the minimum connecting time and puts a limit
on the maximum connecting time. In our case, we assume 30 minutes between domestic
connections and 45 minutes between domestic and international connections and between
international connections for the minimum connecting time and 420 minutes for the
maximum connecting time.
Next, NetScan assigns a quality index to every individual connection, ranging from 0 to
1 (Step 2). A non-stop direct connection is given the maximum quality index of 1. The quality
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index of an indirect or a hub connection will always be lower than 1, since extra travel time is
added due to transfer time and detour time for the passenger. The same holds true for a
multi-stop direct connection. Passengers face a lower network quality because of en-route
stops compared to a non-stop direct connection. If the additional travel time of an indirect or a
hub connection exceeds a certain threshold, the quality index of the connection equals 0. The
threshold between two airports depends on the travel time of a theoretical direct connection
between these two airports. In other words, the longer the theoretical direct travel time
between two airports, the longer the maximum indirect travel time can be. The travel time of a
theoretical direct connection is determined by the geographical coordinates of origin and
destination airport and the assumptions on flight speed and time needed for take-off and
landing. Furthermore, additional time penalties for transfer time have been incorporated into
the model. Passengers generally perceive transfer time as more inconvenient than flying time,
as additional risks exist of missing connections and loss of baggage.
By taking the product of the quality index and the frequency of the connection per time
unit (day, week or year), the total number of connections or connectivity units (CNU’s) can
be derived.
Figure 2. Scheme of NetScan connectivity model
Summarizing, the following formulas have been applied for each individual direct,
indirect and hub connection (Airports Council International, 2014). The air network
developments at each airport are assessed by calculating the direct, indirect and hub
connectivity.
60/)gcd*068.040(, kmt nonstopflightxy += (1)
)5.0(ln5 ,,max, ++= nonstopflightxy
nonstopflightxy
perceivedxy ttt (2)
flightsdirectfort actualflightxy
,
=actualperceived
yhxt,
)( (3)
flightsindirectfortptt transferhxy
actualflighthy
actualflightxh *)(
,,++
Step 1:
1. Minimum connecting time:
30 minutes (Domestic>>>Domestic)
45 minutes (Domestic>>>International
International>>>Domestic
International>>>International)
2. Maximum connecting time: 420 minutes
1. Retrieval of direct
connections from OAG
2. Indirect and hub
connections building
3. Calculation of CNU
Step 2:
NetScan assigns to each connection a quality
index, ranging from 0 to 1.
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nonstopflightxy
actualperceivedyhx ttif ,,
)(1
max,,)(
,
,max,
,,)(
)( 1 perceivedxy
actualperceivedyhx
nonstopflightxynonstopflight
xyperceivedxy
nonstopflightxy
actualperceivedyhx
yhx tttiftt
ttq
−
−−= ( 4 )
max,,)(0 perceived
xyactualperceived
yhx ttif
where, nonstopflight
xyt , : non-stop flight time between airports X and Y in hours,
kmgcd : great-circle distance in kilometers,
max,perceivedxyt : maximum allowable perceived travel time between airports X and
Y in hours,
actualperceivedyhxt
,)(
: actual perceived travel time between airports X and Y (via airport
H) in hours, actualflight
xyt , : actual travel time between airports X and Y in hours,
transferht : transfer time at airport H in hours,
xyp : penalty for transfer time (= nonstopflightxyt ,075.03 −= ), and
yhxq )( : quality index.
First, the maximum allowable perceived travel time is calculated. The maximum
allowable perceived travel time between airports X and Y depends on the non-stop flight time
between airports X and Y and a factor which decreases with distance. The non-stop flight
time is determined by the geographical coordinates of origin and destination airport and the
flight speed of an average jet aircraft taking into account the time needed for take-off and
landing. Over longer distances, passengers are willing to accept longer transfer and circuity
time. Therefore, the maximum allowable perceived travel time also depends on a factor which
decreases with distance, indicating that the further apart two airports are, the longer the
maximum allowable perceived travel time will be.
Second, the actual perceived travel time is determined. For direct connections, the
actual perceived travel time between airports X and Y equals the actual flight time. For
indirect connections, the actual perceived travel time equals the flight time on both flight legs
and the transfer time at airport H. As the transfer time is considered more uncomfortable than
the flight time, the transfer time is penalized by a factor which decreases with distance.
If the actual travel time is smaller than or equal to the average non-stop flight time, then
the weight of the connection equals one. In practice, this is only the case on direct flights
operated by aircraft that are at least equally fast as the average jet aircraft on which the
non-stop flight time is based. When the perceived travel time becomes larger than the
maximum allowable perceived travel time, then the weight of the connection is zero and the
connection will be considered unviable. In other cases, the perceived travel time lies between
the non-stop flight time and the maximum allowable perceived travel time. In these cases, the
weight of the connection depends on the relative difference between the perceived travel time
and the maximum allowable perceived travel time.
When the perceived travel time is relatively small compared to the maximum allowable
perceived travel time, then the weight of the connection will be high, and vice versa. The
connectivity of an individual direct or indirect connection equals its quality.
The CNU is calculated for each individual direct and indirect connection. This means
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that when a flight is offered with a daily frequency, the CNU’s for each of these seven flights
as well as for each possible connection have been calculated. The reason for distinguishing
between individual flights is twofold. First, the flights might be carried out by different types
of airplane during the week, leading to different flight time and therefore differing CNU’s.
Second, the same flight might connect to different flights, for example, on Monday than on
Friday.
2.4 Data and Study Airports
The data used in this analysis are from the OAG flight schedules for the third week of
September in 2001, 2009 and 2017. In this study, only online connections are considered as
viable connections. In other words, the passenger transfer has to take place between flights of
the same airline or the same global airline alliance partners. For the years 2004 and 2007,
three global airline alliances are distinguished: Oneworld, SkyTeam and Star Alliance. For the
year 2001, an additional alliance, Wings Alliance, is also distinguished, which submerged into
SkyTeam in 2004. In addition, actual codeshare agreements between airlines are also
considered when building connections.
The study area is specified as East Asia and Southeast Asia. The airports, selected and
analyzed in our study, are 15 primary airports in this area: Narita International Airport
(hereafter, Tokyo/Narita), Tokyo International Airport (Tokyo/Haneda), Kansai International
Airport (Osaka), Chubu Centrair International Airport (Nagoya), Incheon International
Airport (Seoul), Beijing Capital International Airport (Beijing), Shanghai Pudong
International Airport (Shanghai), Guangzhou Baiyun International Airport (Guangzhou),
Hong Kong International Airport (Hong Kong), Taoyuan International Airport (Taipei), Ninoy
Aquino International Airport (Manila), Suvarnabhumi Airport (Bangkok), Kuala Lumpur
International Airport (Kuala Lumpur), Singapore Changi Airport (Singapore) and
Soekarno-Hatta International Airport (Jakarta). The analysis considers the connectivity
between or via these airports and airports worldwide.
3. COMPARISON OF NETWORK PERFORMANCE AND HUB COMPETITIVE
STATUS AMONG PRIMARY AIRPORTS IN ASIA
3.1 Total Network Connectivity
Figure 3 shows the total network connectivity split up in direct, indirect and hub connectivity
at the primary Asian airports in 2017. As for direct connectivity, Chinese airports definitely
provided many direct connections: Beijing (5,737 CNU), Guangzhou (4,364 CNU), Shanghai
(4,239 CNU) and Hong Kong (3,308 CNU). Jakarta was the second largest airport in this
region with regard to direct connectivity and accommodated 4,827 direct flights in this year.
Furthermore, Tokyo/Haneda (4,143 CNU), Kuala Lumpur (3,728 CNU), Singapore (3,477
CNU), Bangkok (3,257 CNU) and Seoul (3,027 CNU) offered more than 3,000 direct flights.
Indirect connectivity was remarkable at Beijing (11,000 CNU), Singapore (10,561 CNU) and
Tokyo/Narita (10,029 CNU), followed by Shanghai (9,903 CNU), Hong Kong (8,739 CNU)
and Bangkok (7,953 CNU). With respect to hub connectivity, Beijing, Hong Kong and
Singapore were in the first tier, with 16,400 CNU, 14,520 CNU and 12,077 CNU,
respectively. Bangkok (10,766 CNU), Tokyo/Haneda (10,117 CNU) and Seoul (10,014 CNU)
were in the second tier. Indirect and hub connectivity at Guangzhou and Jakarta, in general,
were not so high, compared with direct connectivity.
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Figure 3. Direct, indirect and hub connectivity at primary Asian airports, 2017
3.2 Connectivity Developments
3.2.1 Direct connectivity
Figure 4 describes the direct connectivity developments between 2001 and 2017 at the
primary airports in Asia. The highest growth can be found at the three airports in Mainland
China. In particular, the figure at Shanghai increased by 598%. That of Guangzhou increased
by 262% between these years. One reason concerns the opening of new international airports
in 1999 and in 2004 in each of these cities, respectively. Jakarta (288%), Kuala Lumpur
(218%) and Seoul (218%) also experienced remarkable growth levels.
Figure 4. Direct connectivity at primary Asia airports, 2001, 2009 and 2017
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
CNU
2001 2009 2017
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
CNU
Direct connectivity Indirect connectivity Hub connectivity
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3.2.2 Indirect connectivity
Figure 5 describes the indirect connectivity developments between 2001 and 2017 at the
primary airports in Asia. The high growth of indirect connectivity at Tokyo/Haneda (2,494%)
can be definitely attributed to the resumption of international air services in 2010. In addition,
Guangzhou demonstrated quite a high growth (2,955%). In contrast, other airports in Japan
showed negative growth rates: Tokyo/Narita (-19%), Osaka (-32%) and Nagoya (-11%).
Figure 5. Indirect connectivity at primary Asia cities, 2001, 2009 and 2017
3.2.3 Hub connectivity
Figure 6. Hub connectivity at primary Asia airports, 2001, 2009 and 2017
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
CNU
2001 2009 2017
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
CNU
2001 2009 2017
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Figure 6 describes the hub connectivity developments between 2001 and 2017 at the primary
airports in Asia. Shanghai and Guangzhou experienced the highest growth percentages, with
12,876% and 5,630%, respectively. This was because these two airports had very low levels
of hub connectivity in 2001. The high growth of hub connectivity at Tokyo/Haneda (1,528%)
can be again due to the resumption of international air services in 2010. Osaka experienced
the negative growth percentage (-17%).
3.3 Directional Connectivity
3.3.1 Direct and indirect connectivity
The direct and indirect connectivity figures have been broken down by geographical regions
of the final destination to determine how the direct and indirect connectivity at each airport
has developed in specific markets: domestic, Japan, China (including Hong Kong and Macau),
East Asia, Southeast Asia, West Asia (Middle East), Other Asia, Oceania, Europe, North
America, South America and Africa.
Figure 7 shows the directional total connectivity (direct and indirect) at the Asian
primary airports in 2001, 2009 and 2017. These figures demonstrate in which market each
airport has a competitive status. The first observation is that all airports are rather poorly
connected to Other Asia (South Asia and Central Asia), South America and Africa.
Tokyo/Narita has the best competitive status in the transpacific market and a strong network
to European destinations. In addition, it is the gateway to South America, looking at the total
connectivity. Yet, the connectivity to domestic destinations is low because of the split-up of
operations between Tokyo/Haneda. It experienced the negative growth in the total
connectivity to North America over these years. This was because Japan Airlines downsized
its operations as a consequence of its bankruptcy in 2010, resulting in a considerable loss of
connectivity to this region. The total connectivity from Tokyo/Haneda to almost all regions
increased drastically over these years, definitely owing to the resumption of international air
services at this airport in 2010. The growth in the connectivity to North America and Europe
is remarkable. Osaka had, on the other hand, the largest connectivity to Europe in 2017 and
high connectivity to domestic and Asian destinations, in addition to North America. However,
Osaka experienced negative growth rates over the years. Meanwhile, Nagoya had the largest
connectivity to domestic destinations.
Seoul increased its connectivity especially to North America and Europe during the
period of analysis, mainly because of the successful network growth of Korean Air Lines and
Asiana Airlines to these regions. Seoul has little connectivity to domestic destinations because
of the split-up between Gimpo International Airport, the other airport in Seoul Metropolitan
Area. With respect to the four Chinese airports, Beijing and Guangzhou are highly accessible
from a domestic point of view (directly or indirectly), whereas Shanghai shows, besides
domestic connectivity, strong connectivity to North America. Hong Kong is serving, on top of
domestic destinations, North America and Europe, East and Southeast Asia and Oceania.
These Chinese airports demonstrate high percentage growth rates during the years analyzed.
Taipei had the largest connectivity to North America in 2017. As for the five ASEAN airports,
the connectivity of Bangkok and Singapore are characterized by a strong hub status in the
European market with the modest growth rates. On the other hand, Manila, Kuala Lumpur
and Jakarta are much more oriented towards domestic and Asian destinations. Kuala Lumpur
and Jakarta show high growth rates throughout the period of analysis.
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1. Tokyo/Narita 2. Tokyo/Haneda 3. Osaka
4. Nagoya 5. Seoul 6. Beijing
7. Shanghai 8. Guangzhou 9. Hong Kong
10. Taipei 11. Manila 12. Bangkok
13. Kuala Lumpur 14. Singapore 15. Jakarta
Figure 7. Directional direct and indirect connectivity at primary Japanese Airports, 2001,
2009 and 2017
0
500
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2,500Domestic
China
East Asia
Southeast Asia
West Asia
(Middle East)
Other AsiaOceania
Europe
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Africa
2001
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Japan
China
East Asia
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(Middle East)
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South America
Africa2001
2009
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Japan
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(Middle East)
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Europe
North America
South America
Africa
2001
2009
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Japan
East Asia
Southeast Asia
West Asia
(Middle East)
Other AsiaOceania
Europe
North America
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Africa
2001
2009
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(Middle East)
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Africa
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2017
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China
East Asia
Southeast Asia
West Asia
(Middle East)
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Oceania
Europe
North America
South America
Africa2001
2009
2017
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Japan
China
East Asia
Southeast Asia
West Asia
(Middle East)
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Oceania
Europe
North America
South America
Africa2001
2009
2017
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Japan
China
East Asia
Southeast Asia
West Asia
(Middle East)
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Oceania
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North America
South America
Africa2001
2009
2017
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Japan
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West Asia
(Middle East)
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Africa2001
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South America
Africa
2001
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2017
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3.3.2 Hub connectivity
Meanwhile, the hub connectivity figure has been broken down by geographical regions of the
final destination to determine how the hub connectivity at each airport has developed in
specific markets: domestic-Domestic, Asia-Asia (Within Asia), Asia-Oceania, Asia-Europe,
Asia-North America, Asia-South America, Asia-Africa and intercontinental.
Figure 8 shows the directional hub connectivity at the Asian primary airports in 2001,
2009 and 2017. There are some geographical differences with respect to the hub connectivity
among these airports. For example, Tokyo/Narita shows the strongest hub connectivity to
North America, whereas Tokyo/Haneda has the largest hub connectivity in the domestic
market. Seoul has the large hub connectivity to North America, Europe and within Asia.
Meanwhile, Beijing shows the strong connectivity to North America and Europe, in addition
to domestic airports. Hong Kong demonstrates the strong intercontinental hub connectivity
and Singapore the large hub connectivity to Oceania, Europe and within Asia. Jakarta, on the
other hand, has specialized in domestic and Asian hub connectivity, in other words,
connecting domestic airports and those in Asia.
Meanwhile, the three Chinese airports in Mainland China demonstrate the impressive
growth in hub connectivity to all geographical regions between 2001 and 2017. This indicates
that these Chinese airports are quickly developing as hubs, though hub connectivity levels at
these airports were rather low in 2001. In addition, hub connectivity via Tokyo/Haneda also
increased drastically during the period of analysis. Overall, there is only small intercontinental
hub connectivity at many of these airports.
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1. Tokyo/Narita 2. Tokyo/Haneda 3. Osaka
4. Nagoya 5. Seoul 6. Beijing
7. Shanghai 8. Guangzhou 9. Hong Kong
10. Taipei 11. Manila 12. Bangkok
13. Kuala Lumpur 14. Singapore 15. Jakarta
Figure 8. Directional hub connectivity at primary Japanese Airports, 2001, 2009 and 2017
0
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8,000Domestic
Within Asia
Asia-Oceania
Asia-Europe
Asia-North
America
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America
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Intercontinental
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2017
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America
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America
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Within Asia
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Asia-Europe
Asia-North
America
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America
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Intercontinental
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2009
2017
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Within Asia
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Asia-North
America
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America
Asia-Africa
Intercontinental
2001
2009
2017
0
1,000
2,000
3,000
4,000Domestic
Within Asia
Asia-Oceania
Asia-Europe
Asia-North
America
Asia-South
America
Asia-Africa
Intercontinental
2001
2009
2017
0
500
1,000
1,500Domestic
Within Asia
Asia-Oceania
Asia-Europe
Asia-North
America
Asia-South
America
Asia-Africa
Intercontinental
2001
2009
2017
0
1,000
2,000
3,000
4,000Domestic
Within Asia
Asia-Oceania
Asia-Europe
Asia-North
America
Asia-South
America
Asia-Africa
Intercontinental
2001
2009
2017
0
100
200
300
400Domestic
Within Asia
Asia-Oceania
Asia-Europe
Asia-North
America
Asia-South
America
Asia-Africa
Intercontinental
2001
2009
2017
0
500
1,000
1,500
2,000
2,500Domestic
Within Asia
Asia-Oceania
Asia-Europe
Asia-North
America
Asia-South
America
Asia-Africa
Intercontinental
2001
2009
2017
0
500
1,000
1,500Domestic
Within Asia
Asia-Oceania
Asia-Europe
Asia-North
America
Asia-South
America
Asia-Africa
Intercontinental
2001
2009
2017
0
1,000
2,000
3,000
4,000
5,000
6,000Domestic
Within Asia
Asia-Oceania
Asia-Europe
Asia-North
America
Asia-South
America
Asia-Africa
Intercontinental
2001
2009
2017
0
500
1,000
1,500
2,000Domestic
Within Asia
Asia-Oceania
Asia-Europe
Asia-North
America
Asia-South
America
Asia-Africa
Intercontinental
2001
2009
2017
0
2,000
4,000
6,000
8,000Domestic
Within Asia
Asia-Oceania
Asia-Europe
Asia-North
America
Asia-South
America
Asia-Africa
Intercontinental
2001
2009
2017
0
1,000
2,000
3,000
4,000Within Asia
Asia-Oceania
Asia-Europe
Asia-North
America
Asia-South
America
Asia-Africa
Intercontinental
2001
2009
2017
Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019
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4. SCENARIO ANALYSES FOR CHUBU CENTRAIR INTERNATIONAL AIRPORT
For an assessment of the model and its application, the paper conducts scenario analyses for
Chubu Centrair International Airport on the connectivity impacts of an additional flight from
this airport to large hub airports in Europe, North America and Asia, respectively, and of
moving all domestic flights from Nagoya Airfield, the other airport in Nagoya, to this airport.
4.1 Dual Airport Systems in Nagoya Metropolitan Area
4.1.1 Chubu Centrair International Airport
Chubu Centrair International Airport is an international airport on a manmade island, 35 km
south of Nagoya in the central region of Japan (see Figure 9). It is classified as a first class
airport and is the main international gateway for the third largest metropolitan area in Japan
with its population size of more than twenty million in the catchment area. The airport had
reached its maximum capacity and currently processes around four million international
passengers and six million domestic passengers, ranking 8th busiest in the nation.
When Chubu Centrair opened in 2005, it took over almost all of Nagoya International
Airport’s (now Nagoya Airfield) commercial flights. However, there were several withdrawals
from Chubu Centrair after the airport commenced its operation. American Airlines operated a
route to Chicago for less than seven months in 2005. In 2008, after a few years of service
from Chubu Centrair, several airlines cancelled certain flights, including Malaysia Airlines’
suspension of flight to Kuala Lumpur, Jetstar ending its operation, Continental Airlines
stopping its Honolulu flight and United Airlines’ suspension of flight to San Francisco.
Emirates and Hong Kong Express Airways left the airport in 2009, although the latter
resumed its service from 2014. Garuda Indonesia and EVA Air left the airport in 2013. V Air
withdrew from the airport and ended its operation in 2016.
Meanwhile, new international services started mainly to Chinese airports to service the
influx of inbound tourists from China. Furthermore, AirAsia Japan, a Japanese low-cost
carrier, launched in 2017 from its base at Chubu Centrair. A low-cost carrier terminal is
scheduled to be completed at this airport in 2019.
Figure 9. Location of Chubu Centrair International Airport and its aerial photo
Chubu Centrair International Airport
Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019
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4.1.2 Nagoya Airfield
Nagoya Airfield, former Nagoya International Airport, served as the main international airport
for Nagoya Metropolitan Area until the opening of Chubu Centrair in 2005. It is also known
as Komaki Airport or Nagoya Airport. Figure 10 shows the location of Nagoya Airfield and
its aerial photo.
Figure 10. Location of Nagoya Airfield and its aerial photo
During the 1980s and early 1990s, Nagoya International Airport was a busy airport
because of the overflow from other international airports in Japan, New Tokyo International
Airport (now Narita International Airport) and Osaka International Airport (Itami Airport).
Since the opening of Kansai International Airport in 1994, the airport’s main traffic source has
been the nearby automotive and manufacturing industries, causing carriers such as United
Airlines to stop flying to Nagoya. In addition, the airport was hampered by its location in a
residential area, limiting the number of flights, as well as the operating hours. Because of
these reasons, a new airport, Chubu Centrair, was built on an island south of Nagoya. In 2005,
nearly all of Nagoya International Airport’s commercial flights moved to Chubu Centrair. On
the same day, the old airport became a general aviation and airbase facility, as well as was
renamed to the current names.
It is now a domestic secondary airport, which is the main hub for Fuji Dream Airlines,
the only airline that offers scheduled domestic services from this airport. Therefore, the
domestic air services in Nagoya Metropolitan Area are split up between Chubu Centrair and
Nagoya Airfield, which deteriorates the domestic network connectivity and hub competitive
status of Chubu Centrair.
4.2 Connectivity Impacts of Additional Flights
In this section, scenario analyses for Chubu Centrair are conducted on the connectivity
impacts of an additional flight from this airport to large hub airports: Frankfurt by Lufthansa,
Detroit by Delta Air Lines and Kuala Lumpur by AirAsia.
4.2.1 Frankfurt
Table 1 shows the indirect connectivity impacts of an additional flight of Lufthansa from
Chubu Centrair to Frankfurt by countries. In other words, this indicates the additional onward
Nagoya Airfield
Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019
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connectivity from Frankfurt by countries (see Figure 1). According to this table, one
additional flight of Lufthansa from Chubu Centrair to Frankfurt increases the indirect
connectivity to domestic destinations in Germany by 10.89 CNU from 32.54 CNU to 43.44
CNU. Other remarkable examples include Italy (additional 6.82 CNU), France (additional
4.03 CNU), United Kingdom (additional 3.95 CNU), Austria (additional 3.70 CNU),
Switzerland (additional 3.04 CNU) and Spain (additional 3.00 CNU).
Table 1. Additional indirect connectivity from Chubu Centrair by countries (CNU)
Country Before After Difference
Argentina 0.28 0.37 0.09
Austria 11.08 14.78 3.70
Belgium 2.86 4.02 1.16
Bulgaria 0.12 0.16 0.04
Belarus 0.65 0.85 0.20
Canada 0.08 0.10 0.03
Switzerland 9.12 12.16 3.04
Czech Republic 1.41 1.88 0.47
Germany 32.54 43.44 10.89
Denmark 3.38 4.51 1.13
Spain 9.00 12.00 3.00
France 13.34 17.37 4.03
United Kingdom 10.91 14.86 3.95
Greece 0.72 1.00 0.29
Croatia 3.72 5.14 1.42
Hungary 1.62 2.16 0.54
Ireland 1.79 2.39 0.60
Israel 0.13 0.17 0.04
Italy 21.24 28.05 6.82
Luxembourg 1.53 2.04 0.51
Netherlands 2.94 3.93 0.99
Norway 1.37 1.85 0.49
Poland 7.31 9.75 2.44
Portugal 0.35 0.47 0.12
Russia 0.57 0.76 0.19
Sweden 2.99 3.99 1.00
United States 0.24 0.32 0.08
Form a hub connectivity perspective shown in Table 2, the strongest gains are found in
Japan (additional 4.37 CNU), because connections between a Lufthansa flight and flights of
All Nippon Airways are constructed at this airport, both of which are the Star Alliance
members. In addition, hub connectivity at this airport to Philippines (additional 0.52 CNU),
Guam (additional 0.40 CNU), Hong Kong (additional 0.38 CNU) and China (additional 0.33
CNU) becomes larger by the connections between a Lufthansa flight and flights of the Star
Alliance members.
Table 2. Additional hub connectivity at Chubu Centrair by countries (CNU)
Country Before After Difference
China 0.98 1.31 0.33
Guam 1.21 1.61 0.40
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Hong Kong 1.14 1.52 0.38
Japan 13.10 17.46 4.37
South Korea 0.34 0.46 0.11
Philippines 1.55 2.06 0.52
Singapore 0.46 0.62 0.15
Thailand 0.33 0.44 0.11
Vietnam 1.74 1.91 0.17
4.2.2 Detroit
Table 3 shows the indirect connectivity impacts of an additional flight of Delta Air Lines from
Chubu Centrair to Detroit by countries. In other words, this indicates the additional onward
connectivity from Detroit by countries. As shown in this table, one additional flight of Delta
Air Lines from Chubu Centrair to Detroit increases the indirect connectivity mainly to
domestic destinations in the United States by 24.92 CNU from 118.81 CNU to 143.72 CNU.
Form a hub connectivity perspective shown in Table 4, the strongest gains are found in
China, because connections between a Delta Air Lines flight and flights of the SkyTeam
members, including China Southern Airlines and China Eastern Airlines, are constructed at
this airport.
Table 3. Additional indirect connectivity from Chubu Centrair by countries (CNU)
Country Before After Difference
Brazil 1.10 1.66 0.55
Canada 3.15 3.78 0.63
United Kingdom 0.17 0.20 0.03
United States 118.81 143.72 24.92
Table 4. Additional hub connectivity at Chubu Centrair by countries (CNU)
Country Before After Difference
China 12.93 16.01 3.09
South Korea 3.75 4.50 0.75
Taiwan 0.24 0.36 0.12
4.2.3 Kuala Lumpur
Table 5 shows the indirect connectivity impacts of an additional flight of AirAsia from Chubu
Centrair to Kuala Lumpur by countries. In other words, this indicates the additional onward
connectivity from Kuala Lumpur by countries. Currently, there is no direct flight between
these two airports, so this scenario analysis suggests the impact on indirect connectivity
(onward connectivity) of launching a new route to Kuala Lumpur by AirAsia Japan or the
affiliate airlines of AirAsia, such as AirAsia X.
As shown in this table, one flight of AirAsia from Chubu Centrair to Kuala Lumpur
increases the indirect connectivity to Malaysia (4.69 CNU), Indonesia (2.99 CNU), Thailand
(1.16 CNU) etc.
As explained Section 4.1.1, AirAsia Japan launched out in October 2017 with a first
flight from this airport to Sapporo (New Chitose Airport), so there was no hub connectivity
during the period of analysis (September).
Table 5. Additional indirect connectivity from Chubu Centrair by countries (CNU)
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Country Before After Difference
Australia - 0.003 0.003
Indonesia - 2.99 2.99
India - 0.37 0.37
Cambodia - 0.35 0.35
Sri Lanka - 0.23 0.23
Myanmar - 0.36 0.36
Malaysia - 4.69 4.69
Singapore - 0.57 0.57
Thailand - 1.16 1.16
Vietnam - 0.27 0.27
4.3 Impacts on Connectivity of Moving Domestic Flights from Nagoya Airfield to Chubu
Centrair International Airport
Another scenario analysis is furthermore conducted on the connectivity impacts of moving
domestic flights from Nagoya Airfield to Chubu Centrair. This means that the domestic flights
by Fuji Dream Airlines, the only carrier providing scheduled services at Nagoya Airfield, are
moved to Chubu Centrair. The first observation is that the number of 154 direct domestic
flights is added at Chubu Centrair, as shown in Table 6 by destination airports. To be more
specific, six new destinations are added, including Aomori, Yamagata, Hanamaki, Izumo,
Kochi and Kitakyushu. Direct connectivity to Fukuoka, Niigata and Kumamoto increase by
35 CNU, 7 CNU and 14 CNU, respectively.
Table 6. Additional direct connectivity from Chubu Centrair by destination airports (CNU)
Destination airport Before After Difference
Aomori - 21.00 21.00
Fukuoka 84.00 119.00 35.00
Yamagata - 14.00 14.00
Hanamaki - 28.00 28.00
Izumo - 14.00 14.00
Kochi - 14.00 14.00
Niigata 14.00 21.00 7.00
Kitakyushu - 7.00 7.00
Kumamoto 21.00 35.00 14.00
As for indirect domestic connectivity, the total number of 5.46 CNU is added to New
Chitose (4.40 CNU) and Fukuoka (1.06 CNU), as shown in Table 7. Table 8 shows them form
a hub connectivity perspective, indicating that these additional indirect domestic connectivity
are constructed at Yamagata (4.40 CNU) and Niigata (1.06 CNU).
Table 7. Additional indirect connectivity from Chubu Centrair by destination airports (CNU)
Destination airport Before After Difference
Aomori 1.19 1.19 0.00
Akita 1.99 1.99 0.00
New Chitose 16.50 20.91 4.40
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Fukue 6.96 6.96 0.00
Fukuoka 0.72 1.78 1.06
Hakodate 2.98 2.98 0.00
Ishigaki 14.71 14.71 0.00
Miyazaki 2.63 2.63 0.00
Memanbetsu 1.20 1.20 0.00
Miyako 5.58 5.58 0.00
Naha 16.38 16.38 0.00
Rishiri 0.09 0.09 0.00
Nakashibetsu 4.39 4.39 0.00
Tsushima 0.20 0.20 0.00
Wakkanai 6.12 6.12 0.00
Table 8. Additional indirect connectivity from Chubu Centrair by hub airports (CNU)
Hub airport Before After Difference
Akita 0.43 0.43 0.00
New Chitose 18.18 18.18 0.00
Fukuoka 17.29 17.29 0.00
Yamagata - 4.40 4.40
Hakodate 0.07 0.07 0.00
Haneda 36.48 36.48 0.00
Ishigaki 3.13 3.13 0.00
Niigata 0.02 1.08 1.06
Kumamoto 1.27 1.27 0.00
Kagoshima 1.36 1.36 0.00
Matsuyama 0.51 0.51 0.00
Nagasaki 3.41 3.41 0.00
Narita 134.84 134.84 0.00
Naha 19.89 19.89 0.00
Sendai 4.92 4.92 0.00
Regarding hub connectivity, additional 57.73 CNU are found in the domestic market,
including the routes between Fukuoka and Yamagata (7.49 CNU), between Hanamaki and
Fukuoka (6.78 CNU), between Kumamoto and Aomori (4.84 CNU) and between Kumamoto
and Hanamaki (4.11 CNU). Among the domestic routes shown in Table 9, Chubu Centrair
functions as a hub airport on the route between Niigata and Fukuoka (2.45 CNU), and hub
connectivity on this route increases to 5.43 CNU after moving the domestic flights from
Nagoya Airfield to this airport.
Table 9. Additional hub connectivity at Chubu Centrair by domestic routes (CNU)
Domestic route Before After Difference
Aomori-Izumo - 1.56 1.56
Fukuoka-Aomori - 2.95 2.95
Fukuoka-Yamagata - 7.49 7.49
Yamagata-Fukuoka - 3.85 3.85
Yamagata-Kochi - 2.55 2.55
Yamagata-Kitakyushu - 2.22 2.22
Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019
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Yamagata-Kumamoto - 2.11 2.11
Hanamaki-Fukuoka - 6.78 6.78
Hanamaki-Kochi - 1.22 1.22
Hanamaki-Kitakyushu - 0.96 0.96
Hanamaki-Kumamoto - 3.31 3.31
Kochi-Hanamaki - 1.75 1.75
Kochi-Niigata - 2.65 2.65
Niigata-Fukuoka 2.45 5.43 2.98
Niigata-Kochi - 1.37 1.37
Niigata-Kitakyushu - 1.17 1.17
Niigata-Kumamoto - 3.86 3.86
Kumamoto-Aomori - 4.84 4.84
Kumamoto-Hanamaki - 4.11 4.11
5. CONCLUSION
In this paper, we applied the Netscan connectivity model for the analysis of the network
performance and hub competitive status of fifteen selected primary airports in Asia over the
period from 2001 to 2017. The Netscan connectivity model measures the number of direct and
indirect connections for each airport and weighs it for its quality in terms of transfer time and
detour time. We classified network connectivity into four: direct, indirect, onward and hub.
All connectivity is expressed in one indicator, connectivity units (CNU’s).
The results revealed that the most striking connectivity growth took place at the three
major airports in Mainland China, which are quickly developing into major hubs: Beijing,
Shanghai and Guangzhou. The number of direct, indirect and also hub connectivity at these
three airports increased at a much higher rate than at other airports in our sample. As for
Shanghai and Guangzhou, opening of new international airports boosted network
performance. All connectivity at Tokyo/Haneda increased drastically over these years
analyzed, definitely owing to the resumption of international air services at this airport in
2010. On the contrary, other airports, such as Osaka, Nagoya and Taipei, experienced a
deteriorating network performance during the period of analysis.
The results presented here may be useful for airports in the assessment of their network
performance as well as benchmarking their competitive hub status vis-à-vis other airports, as
demonstrated in the scenario analyses in this paper. We have presented our results at a fairly
aggregated level. The NetScan connectivity model allows for much more detailed analysis of
an airports’ competitive hub status, including at the level of the geographical corridor (the
Transpacific or Europe-Asia market, for example) or even at the individual market level.
These analyses are, however, left for future research.
ACKNOWLEDGEMENTS
This research was subsidized by the Japan Society for the Promotion of Science (JSPS),
Grant-in-Aid for Scientific Research (C) (Grant Number: 17K03688) and Grant-in-Aid for
Scientific Research (B) (Grant Number: 17H02039), and Kansai Airport Research Institute.
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