Is spatial dispersal the dominant trend in air transport development? A global analysis for 2006–2015

Journal of Air Transport Management 74 (2019) 1–12

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Is spatial dispersal the dominant trend in air transport development? A global analysis for 2006–2015

T

W.H. Wonga, Tommy Cheunga,∗, Anming Zhangb, Yue Wanga a

Department of Supply Chain and Information Management, School of Decision Sciences, Hang Seng Management College, Hang Shin Link, Siu Lek Yuen, Shatin, N.T, Hong Kong b Sauder School of Business, University of British Columbia, 2053 Main Mall, V6T 1Z2, Vancouver, BC, Canada

ARTICLE INFO

ABSTRACT

Keywords: Hub-and-spoke network Spatial dispersal Global air travel Hub bypassing Hub competition

The economic performance of a city or region is considered to be intertwined with its air travel capability. It is thus important for planners and stakeholders to understand the changes in the global aviation network. This study investigates the global aviation network, taking 10 years worth's OAG data from the years 2006–2015 and examines whether a spatial dispersal trend dominates the development of the aviation industry. It considers the aviation network at the airport level, and the airport–city level, which may consist of one or more airports. After clarification of the various definitions of concentration, we find that there appears a trend toward a dispersal pattern in the global aviation network at the airport level. On the other hand, there appears a slight concentration at the airport–city level. Besides, there have been some major capacity expansions at airports in the Middle East and East Asia, while the capacities of some traditional hubs in Europe and North America have become increasingly constrained since the 2008 global financial crisis. Furthermore, our study provides further observations consistent with the phenomenon of bypassing of traditional hubs, especially mega-hubs. Competition for passengers among hubs and secondary airports, especially in multi-airport cities, is discussed.

1. Introduction The airline industry has been growing steadily for decades. Air traffic is expected to maintain an annual growth rate of 4.6–4.9% in the next 20 years (Airbus, 2015, p. 14; Boeing, 2015, p. 23), implying a doubling of traffic volume in 15 years. The development of the aviation business and accessibility to air travel services have long-established interactions with the regional economy (e.g., Zhang, 2012; IATA, 2016). For example, Bel and Fageda (2008) found that a 10% growth in intercontinental destinations accessible by air transport led to a 4% growth in business headquarters in the corresponding European urban areas. Matsumoto et al. (2016) have shown that business connectivity had more influence on the level of international air passenger flows within East Asia than other variables such as GDP per capita, population, and distance between cities. After literature surveys, Fu et al. (2010) and Zhang et al. (2011) also concluded that the air transport liberalisation has led to substantial traffic and economic growth. Understanding the evolution of air travel network helps stakeholders to make better decisions when planning future aviation developments. In particular, these stakeholders like regional or international hubs need to understand their positions and the network changes in global aviation.

∗

Changes in policy, fluctuations in the economic environment, and oscillations of managerial trends in business contribute to the evolution of aviation network through the launching and curtailing of routes, changes in traffic volumes on existing routes, and alterations in the dependency of certain hubs. Prior to the Airline Deregulation Act in 1978, the airline network in the United States (US), and more generally global air travel, were largely a “point to point” system. Many studies in the early 2000s, however, concluded that the “hub and spoke” network system dominated the airline industry in the 1980s and 1990s, especially for the US domestic air travel (e.g., Reynolds-Feighan, 2001; Button, 2002). The hub-and-spoke networks were regarded as helping airlines to minimise costs, improve service quality, gain strategic advantages in airline rivalry, and facilitate alliance partnership (e.g., Brueckner et al., 1992; Hendricks et al., 1995; Oum et al., 1995; Oum and Zhang, 2001). This network strategy created a pattern of traffic concentration, with over 15 major hubs emerging in the US and Europe (e.g., Goetz and Sutton, 1997; Reynolds-Feighan, 2001). Similar hubbing strategies also dominated aviation developments in Southeast Asia between 1979 and 1997 (Bowen, 2000). Later studies nevertheless suggest a more mixed outlook, or even a dispersal trend in global air travel. Using data from 1990 to 2000,

Corresponding author. E-mail address: [email protected] (T. Cheung).

https://doi.org/10.1016/j.jairtraman.2018.09.011 Received 16 October 2017; Received in revised form 28 September 2018; Accepted 30 September 2018 0969-6997/ © 2018 Elsevier Ltd. All rights reserved.

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O'Connor (2003) analyzed the shifting concentration of air transport connectivity across the world city hierarchy. Global air travel showed a de-concentration tendency, with a shift of passenger traffic volume “away from very large cities toward those next in rank” (O'Connor, 2003, p. 91), both within the world's top-100 airports and among the top-tier “globalised and world cities” (GaWC, a classification of cities to be described in more details in Section 4 below). Similarly, Burghouwt and Hakfoort (2001) and Burghouwt (2007) found that, although the intercontinental seat capacity of European airlines was still concentrated in a few large hub airports, intra-European traffic showed a de-concentration pattern due to the growth of regional and low-cost carriers (LCCs). Other studies have shown that as LCCs expanded their services, they tended to spread their capacity and routes more evenly across non-traditional hubs in their networks (Reynolds-Feighan, 2010; Suau-Sanchez and Burghouwt, 2011). Bel and Fageda (2008) and SuauSanchez and Burghouwt (2011) also observed a de-concentration trend in intercontinental seat capacity in Europe during recent years. This paper, we show that the spatial dispersal trend in global air transport has appeared to enter a new phase. Spatial dispersal has long been studied in urban development (Sinclair-Smith and Turok, 2012; Vasanen, 2009). In our context, a specific effect of spatial dispersal is deconcentration, which refers to the spreading of air traffic away from major hubs toward the next-sized airports (O'Connor 2003). The concepts of spatial dispersal and de-concentration are interrelated from the perspective of economic geography and airport competition. Spatial dispersal relates to the air travel spread on the global aviation network, which may due to changes in airport developments, airlines' route development strategies, and the travel patterns of passengers by region. Deconcentration relates to changes in the airport's operating environment, such as government aviation policy, airport competition, capacity constraints at major hubs, and the expansion of low-cost airline networks. The studies reviewed above mainly examined the data up to the 2000s; however, several “game changing” events have occurred in the airline industry in the past 10 years (2006–2015). These events include the roller-coaster ride of jet fuel prices (rising to a historical peak in 2008, then falling to one third of the peak, and now back to about half of the peak), the expansion of LCCs around the world (Gross et al., 2013), the mergers of major legacy carriers (e.g., Delta-Northwest, Lufthansa-Swiss International, United-Continental, and AmericanUSAir), and the partial or complete abandonment of a hub by its dominant carrier, an act known as “dehubbing.” According to Redondi et al. (2012), 37 airports around the world were dehubbed between 1997 and 2009, including Barcelona by Iberia, London Gatwick by British Airways, and Milan Malpensa by Alitalia (Burghouwt, 2014; see Wei and Grubesic, 2015, for a recent case in the US). The impact of the regional economy, changes in the structure of airlines, and the performance of airports, especially international hubs, are all strongly correlated with the evolution of global air travel network. Rimmer (2000) studied the effects of the Asian financial crisis on Southeast Asia's air traffic, where there was a shift in the emphasis on route development. Dobruszkes and Van Hamme (2011) used the carriers' routes and seats (passenger capacity) data (2003–2010) to study the effect of the economic crisis on air traffic volumes and discussed the impact on different continents' air services. Yet, these studies did not examine whether airport travel has become more concentrated or dispersed in the past decade. The objective of this study is to investigate whether the spatial dispersal trend continued with the aforementioned major events and changes by studying a global dataset from 2006 to 2015. This paper is organised as follows: The data collection method is described in Section 2, followed by preliminary findings on the global aviation network. Section 3 presents the main empirical results on network dispersal at the airport level, while the “hub bypassing” issue is discussed in Section 4, focusing on the network concentration at the level of a city that may consist of multiple airports. Finally, Section 5 contains concluding remarks.

2. Air traffic data and preliminary observations 2.1. Data collection Our data were mainly obtained from the Official Airline Guide (OAG). The OAG recorded 96% of passenger travel itineraries and the seat capacity planning of 987 airlines. The capacity data have been applied in many aviation studies, such as Guimerà et al. (2005), SuauSanchez et al. (2016), Chen (2016), and Dobruszkes et al. (2017). We collected monthly passenger capacity data reported by airlines between 2006 and 2015, which may allow one to observe their status before and after the 2008 global financial crisis (GFC). In addition, for the hubbypassing and multi-airport city studies, the actual itinerary data of passengers of all origin-destination (OD) pairs and transfers, from February 2010 to October 2015 were collected,1 consisting of over 90 million itineraries. We have formed a global air travel network using these data from over 3500 airports and over 25,000 routes, which include both monthly domestic and international flights around the world. This dataset solves the data problems, such as non-standard airline data and state-centrism, raised by Derudder and Witlox (2008). To understand the importance of a city in the network, multiple airports and city data have been consolidated into a single city entity, i.e., the figures for all in-city airports were summed to form a total city figure for comparison. Since there is a lack of full-year data for 2015 in our collection, we decided to pick the month of June as the reference month for this assessment. June was chosen because it is the middle month of the year and is generally regarded as neither “high season” nor “low season” in the airline industry. 2.2. Preliminary observations Fig. 1 shows the general trend of air passenger traffic in the last decade, according to the data collected from The World Bank (2016). Here we can see a fast growth between 2009 and 2010 which recovered the stagnant traffic during the GFC. Since 2010, global aviation has returned to the 2006–2007 growth rate. However, does this mean that everything has gone back to normal, or has the landscape of the aviation network been altered? Figs. 2 and 3 show all of the international routes with traffic volume greater than 50,000 passengers in June 2011 and June 2015, respectively. These figures illustrate the scale-free, small-world characteristics in the global aviation network, which align with the findings of Guimerà et al. (2005) from 2001. Comparing these figures, we can see that the intra-Asia routes have become more intense, and while London remains the centre of European air travel, Istanbul is on the rise. The traffic volumes between Dubai and some Middle Eastern and Southwest Asian cities are also increasing. Moscow and Simferopol exhibit similar increases. According to the figures, air traffics are getting concentrated at the hub airports. 3. Global aviation network evolution – airport level 3.1. Changes in concentration of global air travel We first assessed whether the global air travel network became more concentrated or more dispersed at the airport level for the recent decade. Following O'Connor (2003), we assessed passenger shares among the top-100 airports. Table 1 shows the distribution among the world's top-100 airports in terms of airlines' planned passenger volumes (capacity provision in June 2006, 2011, and 2015). As the table shows, the passenger shares of the top-20 airports dropped slightly from 36.6% to 35.1% between 2006 and 2015, although this represents a huge drop from 45.9% back in 2000. Conversely, the shares of the top 51–100 1

2

The OAG only provides actual passenger traffic from February 2010.

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Fig. 1. Annual passenger traffic (2006–2015).

Fig. 2. Major international routes around the globe by geographical location and volume, June 2011.

airports increased from 24.7% in 2000 to 32.7% in 2015. This is confirmed by Table 2, which shows that the growth rates of the top-20 airports (9.2% and 13.0%) were lower than the growth rates of those ranked 21–100 (17.1% and 17.0%) in the past five years. Among the top-100 airports, the lower-ranking airports had faster growth in their passenger shares, suggesting a dispersal pattern in the global aviation network at the airport level. We further explore the dispersal pattern across the whole aviation network. Based on their capacity volumes, we classified the airports into six groups (Table 3). The grouping is based on average passenger volume, with about four times the average volume between two consecutive groups. The dispersal pattern has become more prominent over time: the passenger volumes of the top-100 airports have increased by 31% in a decade, while the airports ranked 101 to 2000 have increased by 47–62%. At the same time, these lower-ranking airports also recorded a significant increment in terms of “degree” – defined as the

number of destinations from a given airport) – in other words, they have established many new routes during the period. 3.2. Changes in airport developments The dispersal pattern shown in Section 3.1 has led us to further investigate the effects on various airports. Using the 2006 ranking as the baseline, Table 4 shows larger increments in the passenger capacities of lower-ranking airports. It is apparent that the traffic growth was dominated by secondary and smaller airports, in line with the LCC expansion and the de-hubbing strategies in the US and Europe. Such fast development has led to many airports being moved up in their airport groups ranked by passenger capacity. Fig. 4 shows a histogram of the changes in airport group ranks across different regions between 2006 and 2015. The OAG's regional definition was adopted and its details can be found in Table 1 in the Appendix. The net difference line 3

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Fig. 3. Major international routes around the globe by geographical location and volume, June 2015.

shows that majority of the increases in airport group ranks took place in Asia, whereas most of the decreases occurred in North America, Western Europe, and Latin America. This clearly indicates a shift in aviation development in the past decade. Following Rimmer (2000), we identified the airports and routes with the highest increases and decreases in capacities, respectively. We selected the capacities in June 2007 (1 year before the GFC), 2010 (1 year after the GFC), and 2015 for comparison. Fig. 5 shows that most increments were found in cities located in Asia and the Middle East, while most decrements were found in the US and Europe one year after the GFC. In contrast, a significant surge took place in Asia and the Middle East, and some cities in the US a few years after the GFC (see Fig. 6). The average increase was more than double that in 2007–2010. The top-30 airports in terms of growth were all ranked in the top-100 airports in terms of volumes in 2015, whereas only 21 were ranked in the top-100 back in 2007. On average, these airports increased their ranking by 45 over the eight years. In contrast, there was a significant reduction in the average capacity decrease between 2010 and 2015. These statistics indicate that airports in Asia and the Middle East have been accounting for majority of the capacity expansion since the GFC.

decreases in passenger capacities by comparing the figures for 2010 and 2007 and for 2015 and 2010, respectively (see Appendix Table 1). In the first two years after the GFC, many airlines reduced their domestic capacities in the US and Western Europe, some developed cities in Asia, such as Hong Kong, and in Taiwan. The US domestic markets accounted for about 35% of the total decrease in volume. The decrease in international routes, which was less severe than that in domestic routes, mostly within the region, accounted for over 20% of the total drop. However, the total decrease in routes among the top 50 reduced significantly in the following few years. Again, most of the decreases were in the domestic routes of European countries, China, Taiwan, South Korea, and Brazil.2 The drop in international routes accounted for less than 10% of the total top-50 drop. A different pattern for the top-50 routes with increased capacity can be seen. In the first several years after the GFC, many airlines expanded their markets in Asia and domestic markets in Brazil. There was minimal expansion in international markets during this period, around only 13% of the top-50 routes expanded their capacities. In 2015, a few years after the GFC, the increase in top-50 capacities was almost double that between 2007 and 2010, with significant increases in domestic routes in Southeast Asia, Northeast Asia, European countries, and the US. Airlines also focused on developing international routes within Asia and the Middle East. In short, changes in both airports and routes show that the growth momentum has shifted from the US and Europe to Asia and the Middle East airports since the GFC. Given the speed of this geographical dispersion, the number of passengers flying to, from, and within Asia and the Middle East will eventually surpass those of both North America and Europe. This trend has been accompanied by a strong process of de-

3.3. Changes in routes We identified the top-50 routes with the highest increases and Table 1 Share of passengers among the world's top-100 airports by capacity, June. Rank

2006

2011

2015

Top 10 Rank 11-20 Rank 21-50 Rank 51-100

21.5% 15.1% 32.3% 31.1%

20.4% 14.9% 33.0% 31.7%

20.5% 14.6% 32.2% 32.7%

100%

100.0%

100.0%

2 As pointed out by an anonymous referee, the decrease in domestic routes could be due to high-speed rail (HSR) developments, at least for some markets. In particular, there have been some major HSR construction and expansion in China, South Korea, Japan, and Taiwan during and after GFC (e.g., Fu et al., 2012; Wan et al., 2016). Further examination on the issue would be an interesting research topic.

Source: OAG data. 4

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Table 2 Passenger capacity of the world's top-100 airports, June. Rank

2006

2011

2015

% change 2006–2015

% change 2011–2015

Total passengers cap. (Top 10) Avg. Passengers cap. (Top 10) Total passengers cap. (11–20) Avg. Passengers cap. (11–20) Total passengers cap. (21–50) Avg. Passengers cap. (21–50) Total passengers cap. (51–100) Avg. Passengers cap. (51–100)

887,589,189 88,758,919 625,409,968 62,540,997 1,338,666,270 44,622,209 1,286,288,620 25,725,772

968,958,106 96,895,811 706,633,967 70,663,397 1,568,739,687 52,291,323 1,505,167,966 30,103,359

1,117,193,262 111,719,326 794,484,092 79,448,409 1,759,424,134 58,647,471 1,784,896,194 35,697,924

25.9%

9.2%

27.0%

13.0%

31.4%

17.1%

38.8%

17.0%

Source: OAG data. Table 3 Passenger degree and passenger capacity changes, 2006–2015. Airport Group Rank

Top 100 101–300 301–700 701–1300 1301–2000 20013500+

Average

Degree Passenger Degree Passenger Degree Passenger Degree Passenger Degree Passenger Degree Passenger

cap. cap. cap, cap. cap. cap.

2006

2011

2015

Shares

Shares

% change

% change

June

June

June

2006

2015

2006–2015

2011–2015

243 3,458,338 101.9 821,375 42.5 207,944 16.9 56,887 7.9 15,450 4.6 3227

267 3,991,693 122.2 1,045,566 49.3 258,779 18.3 70,305 8.6 19,974 4.6 3856

292 4,536,808 133.9 1,204,814 58.4 305,313 21.7 86,114 9.9 24,978

53.8%

50.8%

25.5%

27.0%

12.9%

13.7%

5.3%

5.8%

1.7%

2.0%

0.8%

0.8%

20.0% 31.2% 31.4% 46.7% 37.4% 46.8% 28.1% 51.4% 24.7% 61.7% 1.2% 34.6%

9.5% 13.7% 9.6% 15.2% 18.5% 18.0% 18.5% 22.5% 15.4% 25.1% 2.0% 12.7%

4.7

4344

Source: OAG data.

Table 4 Passenger capacity changes. Airport Group (Ranked by Passenger Capacity)

% change 2006–2015

Top 100

101–300

301–700

701–1300

1301–2000

2001-3500+

26.4%

44.0%

50.3%

62.5%

75.9%

340.3%

(holding constant with 2006 rankings).

concentration; and in particular, the regions' airlines are seeking to find new routes to boost their regional and intercontinental connectivity.

gateway, where ci represents the number of transit passengers at the ith airport, and the volume of transit passengers is calculated as the total number of passengers who do not originate or terminate at the ith airport (p – odi).

3.4. Hub bypassing trend

ODi =

One reason for the dispersal of the global aviation network is a phenomenon often referred to as “hub bypassing.” In recent years, a number of studies has demonstrated a trend of hub-bypassing in the airline market (Bel and Fageda, 2010; Maertens, 2010; Suau-Sanchez et al., 2014). This seems to be an intermediate consequence of the international liberalisation of air traffic (Fu et al., 2010) and especially fast expansion of LCCs in recent decades. LCCs are well-known for their point-to-point services and frequent uses of secondary airports (e.g., Zhang et al., 2008; Gross et al., 2013; Vowles and Lück, 2013). Here we have partially adopted the measurements used by SuauSanchez et al. (2014) to measure the “hubness” of airports: the traffic generation ratio and connectivity ratio. The first indicator (Eq. (1): ODi) is calculated as the ratio between the number of passengers who originate or terminate at the ith airport (odi), and the total number of such passengers around the globe (p). The second indicator (Eq. (2): Ci) measures an airport's contribution to global air traffic as a connecting

Ci =

odi p ci

p

odi

(1) (2)

To calculate the importance of an airport in the global aviation network, we have classified airports using connectivity and traffic volumes. In the following analysis, we will define a “mega hub” as an airport that has a degree equal to or above 400, traffic volume equal to or above 4 million per month (about 0.84% of the world traffic in 2011), and transit volume equal to or above 10% of the airport's traffic volume. Although there are only 10 mega-hubs based on the definition, their total traffic accounted for about 11% of global traffic. A “major hub” is an airport that has degree equal to or above 250, traffic volume equal to or above 2 million per month, and transit volume equal to or above 5% of the airport's traffic volume. The total traffic volumes of major hubs accounted for about 20% of the world traffic. Airports 5

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Fig. 4. Changes in Airport Passenger Capacity Ranking Groups by the Regions (2015 vs. 2006).

Fig. 5. The Countries with Airports Having the Largest Increases and Largest Decreases in Passenger Volumes (2010 vs. 2007).

classified as mega-hubs and major hubs are listed in Table 5. For data availability reasons, our actual traffic volume data covers the 5-year period from 2011 to 2015. Table 5 shows that the majority of the mega- and major hubs experienced a drop in their passenger shares during our scope period. Only seven of the 41 mega- and major hubs have experienced considerable growth of at least 0.02% in total passenger shares between 2011 and 2015. The average drop was 0.04% and the largest was 0.27%. The drop in direct passengers was more visible than the drop in transit passengers. To study the relative changes over the years among different airports, we have calculated the weighted averages of transit volume changes for both mega-hubs and major hubs. The results show that the weighted averages of share changes for direct passengers were −12% for mega-hubs and −6% for major hubs. Further, the share changes for transit volume were −11% for mega-hubs, and −3% for major hubs. Airports such as Frankfurt (FRA), Paris Charles de Gaulle

(CDG), London Heathrow (LHR), Chicago O'Hare (ORD) and Atlanta (ATL) have lost their significant market shares, but over the period, their absolute traffic has increased by 24%, 11%, 14%, 13% and 6%, respectively. The latter case is the natural result of the growth of the overall market (see Fig. 1). These observations suggest that traffic is spatially better balanced than before (thus less concentrated). To see whether the mega and major hubs are being passed, we need to further consider the trends in the absolute number of connecting passengers.3 In particular, we analyse the absolute transit traffic (the number of connecting passengers) by all mega- and major hubs (as listed in Table 5), and the airports of “rest of the world” over two subperiods (2013 vs. 2011) and (2015 vs. 2011). As Table 6 shows, in absolute terms, the average transit traffic volumes of the mega and 3

6

We thank an anonymous referee for suggesting this to us.

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Fig. 6. The Countries with Airports Having the Largest Increases and Largest Decreases in Passenger Volumes (2015 vs. 2010).

major hubs have gone up from 47.5 million in 2011 to 59.7 million in 2015, while that of the airports of rest-of-the-world has gone up from 32.2 million to 48.1 million over the same period. There was a decline in “hubness” performance by most mega- and major hubs over the 2011–2015 period. In 2011, the total mega and major hub transit traffic volumes were 59.65% of the world transit traffic, and they have decreased to 57.12% and 55.37% in 2013 and 2015 respectively (see Table 6). When compared to the transit share of mega-hubs by the rest-of-the-world's airports, rest-of-the-world's airports were about 54% higher than the mega-hubs in 2011 and 97% in 2015. These comparisons show how transit shares of the mega or major hubs decrease over the scope period, against the rest-of-the-world's airports. Furthermore, there was a larger annual growth rate for the rest-of-the-world's airport transits than that of the mega or major hubs within the two periods. The total rest-of-the-world's transit traffic has grown significantly at a rate of about 11% per year over the period (2011–2015). In the same two periods, transit traffics at the mega and major hubs have only grown by approximately 4%–7% in 2011–2015. This illustrates that the mega and major hubs have lost their transit traffics to the rest-of-the-world's airports. These figures echo with the results of other studies demonstrating a hub-bypassing trend in recent years. The seven hub airports with significant growth are Dubai International Airport (DXB), Istanbul Atatürk Airport (IST), Narita International Airport (NRT), Incheon International Airport (ICN), Beijing Capital International Airport (PEK), Shanghai Pudong International Airport (PVG), and Guangzhou Baiyun International Airport (CAN), all of which are located in Asia or the Middle East. The growth of DXB and IST could be credited to the “open sky” liberalisation policy and the strategic geographical locations of the airports in connecting East and West, and South (e.g. Australia and New Zealand) and North (e.g., Europe), and the hub carriers' hubbing strategies (Logthetis and Miyoshi, 2018; O'Connell and Bueno, 2018). Government supports such as infrastructure provision and demand stimulation

through taxation policy have also contributed to Dubai's growth in the global transit share (CAPA, 2016). Istanbul's IST has expanded its network connections to attract both direct and transit passengers from more places around the world. Our data show that the number of “edges” (i.e., an edge refers to direct flights between any two airports; two-way flights between two airports are considered as two edges) for IST increased by over 42% from 2011 to 2015, which was the highest increment among all of the mega- and major hubs. The growth of NRT was due to the extension of its runway and instalment of a new terminal for LCCs; for instance, the runway extension helped increase the airport's hourly departure rate from 32 to 46 (Civil Aviation Bureau of Japan, 2009). ICN's success may be due to its high service quality and the prosperous tourism market in South Korea. ICN has been consistently ranked second among the world's best airports (Skytrax, 2015), and visitor arrivals in and departures from South Korea have increased by 35% and 52%, respectively, during this period (Korea Tourism Organisation, 2016). The success of the three Chinese airports, PEK, PVG, and CAN, was mainly due to the rapid growth of China's economy and air travel demand. The growth of these three airports was driven by OD traffic. In addition, some of the major hubs have demonstrated substantial growth (over 30%) in their transit passenger share during the research period. 4. Global aviation network evolution – city level After proving that aviation network dispersal exists at the airport level, our next question is how it affects a city's aviation traffic. To answer this question, we follow O'Connor (2003) framework using the shares of air passengers in different airport groups and city groups for comparison. Specifically, we apply the broader measure of cities' economic activities developed by the Globalisation and World City (GaWC) Project. This classification includes, for instance, the locations of the headquarters of powerful leading companies in the “control and command” industries (e.g., financial services, legal services, advertising, 7

Hub1 Hub1 Hub2 Hub2 Hub1 Hub1 Hub1 Hub1 Hub2 Hub2 Hub2 Hub1 Hub1 Hub2 Hub2 Hub2 Hub2 Hub2 Hub2 Hub1 Hub2 Hub2 Hub2 Hub2 Hub1 Hub2 Hub2 Hub2 Hub2 Hub2 Hub2 Hub2

FRA CDG IST AMS PEK

ATL ORD LHR LGW DXB MUC DFW

JFK

PVG

BCN DME FCO

ORY YYZ

LAX IAH

EWR

MAD

CAN

DEN LAS CLT

ICN LGA MIA MSP

HKG

Frankfurt Airport Charles de Gaulle Airport Istanbul Atatürk Airport Amsterdam Airport Schiphol Beijing Capital International Airport Atlanta International Airport O'Hare International Airport London Heathrow Airport Gatwick Airport Dubai International Airport Munich Airport Dallas/Fort Worth International Airport John F. Kennedy International Airport Shanghai Pudong International Airport Barcelona–El Prat Airport Moscow Domodedovo Airport Leonardo da Vinci–Fiumicino Airport Paris Orly Airport Toronto Pearson International Airport Los Angeles International Airport George Bush Intercontinental Airport Newark Liberty International Airport Adolfo Suárez Madrid–Barajas Airport Guangzhou Baiyun International Airport Denver International Airport McCarran International Airport Charlotte Douglas International Airport Incheon International Airport LaGuardia Airport Miami International Airport Minneapolis–Saint Paul International Airport Hong Kong International Airport

8 Hub2

Hub2 Hub2 Hub2 Hub2

Hub2 Hub2 Hub2

Hub2

Hub2

Hub2

Hub1 Hub2

Hub2 Hub2

Hub2 Hub2 Hub2

Hub1

Hub1

Hub1 Hub1 Hub1 Hub2 Hub1 Hub2 Hub1

Hub1 Hub1 Hub1 Hub1 Hub1

268

280 326 307 326

411 331 302

305

393

375

405 398

403 393

387 444 400

328

421

492 450 465 482 377 433 415

603 566 396 565 449

2011

2011

2015

Degree

Class

Code (IATA)

Airport

Table 5 Traffic and share of mega- and major hubs: June 2011 and June 2015.

318

329 329 328 321

377 336 331

385

393

398

411 409

416 412

429 428 416

432

433

494 475 466 461 450 439 435

603 586 563 548 527

2015

4141

2877 2253 2912 2925

4438 3303 3284

3117

4063

2788

5168 3194

2091 2796

2694 2046 3197

3228

4098

7964 5671 5666 2847 3742 3032 5068

4515 4799 2962 3784 5647

2011

Traffic (‘000)

5293

3886 2451 3253 3149

4425 3641 3671

4290

4114

3265

6274 3766

2466 3415

3436 2413 3706

4838

4885

8470 6381 6446 3216 6118 3704 5282

5600 5305 5244 4526 7475

2015

0.87%

0.60% 0.47% 0.61% 0.61%

0.93% 0.69% 0.69%

0.65%

0.85%

0.58%

1.08% 0.67%

0.44% 0.59%

0.56% 0.43% 0.67%

0.68%

0.86%

1.67% 1.19% 1.19% 0.60% 0.78% 0.64% 1.06%

0.95% 1.01% 0.62% 0.79% 1.18%

2011

0.88%

0.64% 0.41% 0.54% 0.52%

0.73% 0.60% 0.61%

0.71%

0.68%

0.54%

1.04% 0.62%

0.41% 0.57%

0.57% 0.40% 0.61%

0.80%

0.81%

1.40% 1.06% 1.07% 0.53% 1.01% 0.61% 0.87%

0.93% 0.88% 0.87% 0.75% 1.24%

2015

Share of World Traffic

73%

83% 94% 56% 53%

54% 89% 25%

87%

71%

69%

81% 36%

92% 71%

92% 85% 71%

82%

82%

33% 54% 72% 93% 57% 62% 43%

50% 68% 58% 58% 85%

2011

69%

77% 88% 52% 51%

61% 88% 24%

89%

66%

68%

77% 43%

90% 63%

89% 81% 71%

88%

78%

31% 53% 66% 92% 47% 58% 41%

42% 64% 47% 58% 88%

2015

OD Traffic Proportion of Airport

−3%

1% −19% −16% −18%

−10% −13% −15%

13%

−24%

−8%

−7% 11%

−8% −14%

−1% −10% −8%

28%

−9%

−18% −13% −16% −10% 9% −8% −21%

−15% −16% 15% −4% 10%

OD

(continued on next page)

7%

34% 66% −11% −16%

−38% −13% −16%

−16%

−13%

−10%

8% −22%

9% 16%

27% 9% −13%

−25%

6%

−20% −14% 2% −7% 48% 0% −20%

5% −9% 64% −12% −25%

Transit

Change in World Share (2011–2015)

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−8% 34% 77% 71%

7% −7%

Mega Hubs % Major Hubs % Mega + Major Hub (Millions) Rest of the World's Airports % Rest of the World (Millions)

Transit Traffics Annual Growth Rate (Compound)

2011

2013

2015

2013 vs. 2011

2015 vs. 2011

26.23% 33.41% 47.5

23.68% 33.43% 55.5

22.65% 32.72% 59.7

4.88% 10.42% 8.02%

3.94% 7.27% 5.84%

40.35%

42.88%

44.63%

13.80%

10.58%

32.2

41.6

48.1

76% 80% 0.47% 0.60%

4.1. Aviation development on globalisation and the world city

290 272

2155 2815

2830 3632

0.45% 0.59%

Using the latest classification (GaWC, 2016), we assessed the cities in terms of the interlocking network model of their advanced business services. The GaWC hierarchy classifies major world cities into four categories, and our analysis focuses on the first three: Alpha, Beta, and Gamma, which together represent 212 cities (according to the latest classification update in 2016) and over 65% of the world's air traffic in 2015. The Alpha group consists of the most integrated global cities such as London, New York, Hong Kong, Tokyo, Beijing, and Dubai. Their distribution is widespread, with 16 in Europe, 15 in Asia, 7 in North America, 5 in Latin America, 3 in the Middle East, 2 in Southwest and 1 in Africa. The Beta group consists of cities such as Prague, Boston, Copenhagen, and Seattle, while the Gamma group includes less integrated cities such as St. Petersburg, Phoenix, Osaka, and Taizhong. O'Connor (2003) found that the number of airport passengers in Beta cities increased substantially from 1990 to 2000, with a 10-year growth rate of 83.4% (i.e. 6.25% per year), exceeding those of the Alpha (48.4%) and Gamma (59.4%) groups. After reviewing O'Connor (2003) classification of cities in the Beta group, which included 10 cities such as Sydney, Toronto, Madrid, and Moscow, we note that all of them have now been moved to the Alpha cluster. As these previous Beta cities were still the fastest growing group from 2011 to 2015 (see Table 7), their upgrading could help to explain why the current Alpha group has a higher growth rate than the Beta and Gamma groups. In terms of the passenger increase among different city-groups, the annualised growth rate of the Alpha group between 2006 and 2011 and between 2011 and 2015 was 3.50% and 4.01%, respectively, versus 4.18% and 3.31% for the Beta group, and 4.18% and 3.68% for the Gamma group (see Table 7). From 2006 to 2011, Beta and Gamma groups attained faster growth rate as compared to Alpha group. The difference was reversed in the next period from 2011 to 2015. In addition, the actual capacity difference was highest in the Rest of the World group from 2006 to 2011, but the Alpha group attained similar increment in 2011–2015 using one year less time. Alpha group maintained significant growth in the latter 4 years, while all other groups slowed down their capacity expansions during this period. All these suggested a tendency toward concentration in the Alpha cities.

254 297

Hub1: Mega Hub. Hub2: Major Hub.

Hub2 Hub2 NRT SEA

World Transit Traffics

and information technology), their asset values in the area, revenues and profits, and number of employees in a region. As these economic activities are known to have a strong influence on air traffic development, the GaWC classification is considered a proper fit for the analysis (Taylor and Derudder, 2016).

Hub2 Hub2

−27% −6% −27% 15% 7% −20% 19% −19% −7% −23% −9% −11% −9% −4% 48% 75% 56% 93% 93% 78% 78% 48% 77% 56% 95% 95% 78% 83% 0.46% 0.75% 0.43% 0.49% 0.39% 0.63% 0.71% 0.59% 0.80% 0.57% 0.53% 0.43% 0.71% 0.70% 2780 4531 2618 2935 2330 3827 4261 2797 3807 2697 2528 2060 3399 3352 315 309 304 303 301 300 300 333 290 294 290 258 280 308 Hub2 Hub2 Hub2 Hub2 Hub2 Hub2 Hub2 DTW SIN PHL BOS PMI BKK SFO

Detroit Metropolitan Airport Singapore Changi Airport Philadelphia International Airport Logan International Airport Palma de Mallorca Airport Suvarnabhumi Airport San Francisco International Airport Narita International Airport Seattle–Tacoma International Airport

Hub2 Hub2 Hub2 Hub2 Hub2 Hub2 Hub2

Transit OD 2015 2011 2015 2011 2011 2011 2011

Code (IATA) Airport

Table 5 (continued)

2015

Degree Class

2015

Traffic (‘000)

2015

Share of World Traffic

OD Traffic Proportion of Airport

Change in World Share (2011–2015)

Table 6 Global transit traffics 2011–2015.

4.2. Effects of multiple airports in cities We have shown that the aviation network has become slightly concentrated at the city level, according to the GaWC (2016) classification. However, the findings in Section 3.1 suggest a clear movement 9

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Table 7 Airport passenger capacity by GaWC category (2016) and changes between 2006 and 2015. Category of City

No. of Cities

2006

2011

Alpha Avg. passenger cap. per city α

49

215,939,777 4,406,934

256,450,803 5,233,690

Beta Avg. passenger cap. per city β

81

130,105,984 1,606,247

Gamma Avg. passenger cap. per city γ Rest of the World

83

69,708,021 839,856 227,079,842 83,732

No. of Airports 2712

2015

Change 2006–2011

Change 2011–2015

Annual Growth % 2006–2011*

Annual Growth % 2011–2015*

300,099,732 6,124,484

40,511,026 826,756

43,648,929 890,794

3.50%

4.01%

159,638,420 1,970,845

181,838,562 2,244,921

29,532,436 364,598

22,200,142 274,076

4.18%

3.31%

85,529,009 1,030,470 273,926,145 101,005

98,823,876 1,190,649 310,699,979 114,565

15,820,988 190,614 46,846,303 17,274

13,294,867 160,179 36,773,834 13,560

4.18%

3.68%

3.82%

3.20%

Source: GaWC (2016), and the authors' calculation at the city level. June of each year is picked as the reference point. For airports belong to rest of the World, only those operated between 2006 and 15 were calculated. *(compound).

airports are located in important cities (alpha cities), passengers would not bypass the cities. Instead, they would choose the secondary airports. In short, these multi-airport cities experienced intra-airport competition.

Table 8 Comparison of hub and secondary airports' performance. Average performance indicators

Number of Airports

Average traffic growth, June 2011–June 2015 (%)a

Hub, Primary Secondary

22, 18 42

21.1%, 26.4% 254.3%

a

5. Concluding remarks In this paper, we expand the scope of previous studies on the global aviation network. Our analysis of the data on airports' capacities and traffics during the period of 2006–2015 contributes three valuable findings to the literature. First, the air network structure and traffic have evolved since the GFC, with a dispersal of capacity volumes from top-ranking to lowerranking airports. Specifically, air travel has grown rapidly in Southeast Asia, Northeast Asia, and the Middle East, where airports were not traditionally major ones. At the airport level, we see a spatial dispersal to regional and metropolitan airports. Similar dispersal characteristics have been reported in those growth of larger urban regions, where population growth has spread to surrounding rural areas while economic activities have become increasingly concentrated in urban centers (Vasanen, 2009). The phenomenon is related to the intensifying competition among hub airports, and to the disparity in capacity development. Second, the speed and connectivity of air travel greatly facilitate business activities and distribute economic growth across geographic space. In the globalised economy, the geographic dispersal of economic activities is common. This leads to greater demand for air services, both international and domestic, as part of the extended and enlarged value chain activities, and deregulation of air transportation has added to this effect. The recent expansions in several Asia and Middle East megaairports and other regional airports located in fast-growing cities have contributed to the geographic dispersal. Aided by the expansion of lowcost airline services, the growth rates of these airports sometimes surpass the global average. While a portion of traffic demand is spatially dispersed to regional and metropolitan airports, others remain spatially stable at the city level aviation network. Those globally dispersed airports, and especially fast-growing airports, typically cluster in a multiairport region with a major airport or hub nearby. This indicates that multi-airport regions continue to matter to the growth of the network. What needs to be better understood is the relationship between the effects of spatial dispersal or concentration/de-concentration and the spatial evolution of the aviation network. Third, we demonstrate that the hub-bypassing phenomenon occurs at a higher rate for mega-hubs than for major hubs, suggesting that the rivalry between mega and major hubs to attract passengers has intensified, and the reliance on traditional hubs is gradually eroding. It is interesting to note that this dispersal (of the point-to-point airline network prior to the deregulation)–concentration (intensification of the

Calculated based on actual traffic volume using the OAG dataset.

toward dispersal among the top-100 airports. Here we explored the underlying reason for the significant drop in global traffic share among these hubs. One contributing factor is that many of the mega-hub airports that have experienced a significant drop in market share are located in multiple airport cities, such as Frankfurt Airport (FRA), Charles de Gaulle Airport (CDG), London Heathrow Airport (LHR), John F. Kennedy International Airport (JFK), and Toronto Pearson International Airport (YYZ). We are aware that there are several definitions of multiple airport regions, such as those suggested by Brueckner (2003), Derudder et al. (2010) and O'Connor and Fuellhart (2016), based on a certain distance from a particular hub airport.4 As this study focused on the city level, airports in questions ought to belong to the same city. Although there were around 3400 airport cities over the period 2011–2015, only 36 of these cities had at least one “sufficiently large” secondary airport5; i.e., with a monthly traffic volume of at least 100,000. This criterion ensures that secondary airports are of a comparable size to their hub counterparts. Only 20% of all airports in the world had such a monthly volume in 2015, but their total volume contributed to over 92% of global passenger traffic. The 36 airport cities with sufficiently large secondary airports had a total of 82 airports: 22 belonged to the category of mega-hub or major hub airports as defined in Section 3.4, 18 were the largest traffic airport in the city (primary), and 42 were secondary airports. Table 8 shows that the average traffic growth among secondary airports was 254.3% per airport from 2011 to 2015, while the average traffic growth of hub or primary airports was merely 23.4%. This reveals a clear spatial dispersal of traffic volume to the secondary airports in multi-airport cities, and may explain why hub bypassing can only be found at the hub airports, not at the city level. As most of these hub

4 For an alternative definition of multiple airport regions, see the recent interesting study by Sun et al. (2017). (add reference). 5 Bangkok, Beijing, Belfast, Berlin, Brussels, Buenos Aires, Chicago, Dallas, Dusseldorf, Florence, Frankfurt, Houston, Istanbul, Jakarta, Kuala Lumpur, London, Milan, Moscow, New York, Orlando, Osaka, Oslo, Paris, Phoenix, Rio de Janeiro, Rome, Sao Paulo, Seoul, Shanghai, Stockholm, Taipei, Tenerife, Tokyo, Toronto, Venice, Washington DC (in alphabetical order).

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hub-and-spoke network in the 1980s and 90s)–dispersal pattern of global air transport is somewhat similar to the network evolution (dispersal–concentration–dispersal) observed in the container shipping industry (e.g., Li et al., 2012). In this paper, we only discuss the phenomenon of multi-airport cities. As hub bypassing may occur for a hub airport with airports closely located in neighbouring cities, the impact of such a multiple airport region needs to be explored in the future. Moreover, after demonstrating the trend of hub bypassing at the mega-hub level, a comprehensive study could be carried out to identify the winner(s) and loser(s) from this development, and to examine whether the instalment of infrastructure or other policy changes are effective tools to counteract such phenomenon.

anonymous referees whose constructive comments have led to a large improvement of the paper. Parts of this research have been presented at seminars at the University of Hong Kong and University of Sydney (Institute of Transport and Logistics Study), and at the World Aviation Hub Conference (Incheon) and the 5th European Aviation Conference (Amsterdam). We thank Guillaume Burghouwt, Jaap de Wit, Becky Loo, Meifeng Luo, Pere Suau-Sanchez, and the seminar/conference participants for their helpful comments. We gratefully acknowledge the financial support received from the Public Policy Research Funding Scheme (project number: 2014.B12.001.15A), the Central Policy Unit of the Hong Kong Special Administrative Region Government, the Faculty Development Scheme (project number: UGC/FDS14/B03/15), RGC, and the Li and Fung Institute for Supply Chain Management and Logistics at Chinese University of Hong Kong.

Acknowledgement We would like to thank the editor (Rico Merkert) and two Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jairtraman.2018.09.011. Appendix Table 1 Top-50 changes in route capacities (2007 June vs 2015 June). Zone / Country

Africa

AS1

AS3

AS4

EU1

EU2

LA3

2010 Ys 2007 Top 50 Increase in routes, total seats changes (number of routes) Domestic 80754 229591(2) 1716226 203877 155532 165710 (1) (15) (3) (2) (2) Internati64660 370004 121280 onal (1) (4) (1) 2015 Ys 2010 Top 50 Increase in routes, total seats changes (number of routes) Domestic 301247 2041812 973878 1179845 303425 (2) (11) (6) (6) (2) Internati264018 750691 144196 314557 onal (2) (5) (1) (1) 2010 Ys 2007 TOD 50 Decrease in routes, total seats changes (number of routes) Domestic -510993 -659091 -577552 -88623 (5) (6) (5) (1) Internati-55183 (1) -522412 -396893 -73567 onal (5) (5) (1) 2015 Ys 2010 Top 50 Decrease in routes, total seats changes (number of routes) Domestic -196043 -756970 -965709 -109205 -98669 (4) (13) (13) (20) (1) Internati-103249 onal (2)

LA4

ME

US

SW

Total

122740 (1)

4349156 (43) 640950 (7)

1440919 (14)

74266 (1) 85006 (1)

159541 (2)

153446 (1)

426119 (3)

1827992 (10)

6781645 (38) 1899581 (12)

-1480269 (21)

-3316528 (38) -1048055 (12)

-296845 (4) -49880 (1)

-49834 (1) -99082 (2)

-229473 (5)

-44481 (1)

-2702748 (44) -296692 (6)

Notes: AS1: Afghanistan. India; AS2: Central Asia. AS3: Southeast Asia; AS4: China. Hong Kong, Macau. Japan. Taiwan. Korea; LA1&2: Cuba. Jamaica. Puerto Rico. Costa Rica, Mexico, Panama. LA3: Ecuador, Mexico. Puerto Rico: LA4: Argentina. Brazil. EU1: Italy. Turkey. Spain. UK, Germany, France; EU2: Russia Federation. Romania; ME: Middle East; US: USA; SW: Australia and New Zealand

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