Evolution of the European network and implications for self-connection

Journal of Air Transport Management 65 (2017) 18e28

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Evolution of the European network and implications for selfconnection Mattia Cattaneo, Paolo Malighetti, Stefano Paleari, Renato Redondi* Department of Management, Information and Production Engineering, University of Bergamo, Italy

a r t i c l e i n f o

a b s t r a c t

Article history: Received 29 April 2017 Received in revised form 13 July 2017 Accepted 25 July 2017

Self-connection has become an appealing alternative for passengers in the European air transportation market, along with the remarkable growth of the low-cost carriers (LCCs) network over the last decade. As the development of self-connectivity is not directly designed in airports and airlines' growth strategies, this study aims to deeply understand the evolution of self-connectivity options in the intraEuropean market over time. By implementing a quickest travel time approach, we analyse the number of quickest connections and the share of indirect quickest paths that remained un-managed in years 2006 and 2016. Results document that, overall, travelling in Europe has become faster (
5.7 min of weighted average), while European airports' coverage, that is, airport pairs that can be directly or indirectly connected, decreased from 65% to 53%. The strong increase in LCCs' seat capacity (74%) did not translate into a similar growth of indirect connections options. Due to LCCs' offer redistribution and traditional carriers' partial retreatment from the European market to concentrate on intercontinental destinations, 1-transfer managed or un-managed options available to passengers in Europe dropped by 9.5%, and the share of 1-transfer quickest paths achievable by self-connecting flights increased by only 3pp, from 66% in 2006 to 69% in 2016. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Indirect connectivity European network Minimum travel time

1. Introduction Today, more than in the past, self-connectivity has been increasingly recognised to be a valid alternative for passengers flying inside the European air transportation network. Passengers choosing to self-connect report that this type of mobility follows two main drivers, that is, a general concern about the price and the lack of services from traditional carriers (Walker, 2016). Airports and airlines, and, in particular, low-cost carriers (LCCs), have started to address the need of these passengers along with the hybridisation process they have recently undertaken (Klophaus et al., 2012; Morandi et al., 2015) that lead them to offer some form of facilitation for self-connecting passengers. For instance, Vueling and Norwegian have decided to provide passenger transfers between flights similar to the tests implemented by Ryanair at the airports of London Stansted and Barcelona in 2016, and Rome Fiumicino in 2017.1 Analogously, Malpensa and Gatwick airports set

* Corresponding author. E-mail address: [email protected] (R. Redondi). 1 http://www.airlive.net/news-ryanair-offers-connecting-flights-through-romefiumicino-airport/. http://dx.doi.org/10.1016/j.jairtraman.2017.07.006 0969-6997/© 2017 Elsevier Ltd. All rights reserved.

up platforms for self-connecting passengers (via Milano and GatwickConnects, respectively). In the wake of the important growth faced by the LCC network in the last decade, which has contributed to change people's attitudes of travelling and the entire geography of continental air transportation markets (Dobruszkes, 2013), a core issue for airports and airlines is to understand the potential for self-connections by evaluating its change over time. This assessment is indeed crucial for the decision to invest in self-connecting platforms and the offer of further services by airlines. Nevertheless, this increasing attention leads to new matters, especially because self-connection options were initially born as unexploited opportunities of connection that no company attempted to project. On the one side, the impetuous growth of point-to-point networks, and particularly those addressed by LCCs, has naturally fostered self-connections. However, on the other side, self-connections may have faced a decrease because more and more pairs are today directly connected, reducing room for indirect connections. Moreover, the presence of LCCs in small peripheral airports has also decreased the quality of connectivity offered by traditional carriers, making dayreturn flights more difficult when they remain the only operator (Zeigler et al., 2017). Crucially, the increasing interest of LCCs to

M. Cattaneo et al. / Journal of Air Transport Management 65 (2017) 18e28

move towards bigger airports, which might contribute to reduce their capacity in tertiary airports (Dobruszkes et al., 2017), has put self-connections under further threat. In order to understand how the options of self-connectivity have been offered along with the growth of the network, this study attempts to investigate such dynamics focusing on the entire European market in the last decade. To the best of our knowledge, there are no studies providing an in-depth and comprehensive analysis of the evolution of the phenomenon in the last decade. Only Maertens et al. (2016) report a first attempt to compare self-connectivity over time, focusing on one-stop connections performed by LCCs. Relying on Malighetti et al. (2008)’s contribution and implementing a quickest travel time approach, this study examines and compares the number of quickest connections remained un-managed over time. The remainder of the article is organised as follows. Section 2 reviews the literature on self-connectivity. Section 3 describes the methodology. Section 4 presents the empirical analysis detailing the evolution of the Intra-European market and the evolution of indirect connections in Europe, while Section 5 concludes. 2. Literature review Franke (2004) and Burghouwt (2007) are among the first to introduce the concept of self-help hubbing as a possibility to travel with a combination of tickets towards a destination that is not directly handled by the airlines themselves. Several studies have tried to explore the phenomenon in depth by improving the definition of feasible routes and accounting for the extent of the passengers' demand on the Origin-Destination (O-D) trip. Given the complexity of properly assessing network connectivity, measuring indirect connectivity has been always challenging. Ranging from easier measures based on temporal coordination of flight schedules at hub airports (Burghouwt and de Wit, 2005) to advanced algorithms for calculating the minimum path lengths (Malighetti et al., 2008), the literature proposes a series of incrementally restrictive refinement criteria in order to identify the set of available routes, like excluding paths operating under a minimum connecting time (Malighetti et al., 2008) or those that do not occur in both directions (Maertens et al., 2016). Further, the assessment of ‘self-help hubbing’ would be even more complex once we account for travellers' utility and choice of route and airports. For a comprehensive summary of the different models and applications of connectivity measures employed by the literature, see Burghouwt and Redondi (2013). In this respect, recent contributions try to understand the extent to which self-connectivity has been pervasive in air transport markets. By investigating the European air transportation network in 2006, Malighetti et al. (2008) identify a network structure that could allow passengers to undertake the so-called ‘self-help hubbing’ strategy. Specifically, they find that 66% of the fastest one-stop indirect connections were not operated by the alliances' system, thus allowing important growth prospects to LCCs able to exploit such a demand. Today, European self-connections still represent a great opportunity for LCCs. The LCC network, indeed, offers around 162,000 of weekly one-stop connections, accounting for just 22% of the entire network (Maertens et al., 2016). Whereas self-connection represents an opportunity for LCCs, their presence in airports is also shown to be a crucial factor facilitating self-connectivity itself (Suau-Sanchez et al., 2016), also for European holiday markets (Suau-Sanchez et al., 2017). This type of traffic has the possibility to increase when airports focus on short-haul intraregional markets (Burghouwt, 2007), when they have an offer distributed among different low-cost and traditional carriers, and when airlines and airports concretely cooperate to support passengers (Fichert and

19

Klophaus, 2016). Early research on this topic has also tried to identify the most crucial airports in terms of self-connectivity. Malighetti et al. (2008) document that airports like Amsterdam Schiphol, Munich, and Charles de Gaulle-Paris register the highest potential due to their central position in the European network. More focused on the role played by LCCs, Maertens et al. (2016) highlight that Barcelona, London Stansted, and London Gatwick exhibit great opportunities to growth via LCC transfer. By forecasting the amount of selfconnection traffic, also Suau-Sanchez et al. (2016) corroborate the fact that London Gatwick (followed by Manchester) would be the airport registering the highest increase in traffic in case of growth. The high potential related to self-connectivity lead London Gatwick to implement a platform (GatwickConnects) able to facilitate passengers’ self-help hubbing by reducing the risk of losing onward flights and the ones associated with baggage transfer. The initiative started in 2015 with three carriers: easyJet, Norwegian, and the WOW Icelandic LCC. Given the major evolution of air transportation and, more specifically, the increased competitive pressure associated with the intense activity of LCCs (Dobruszkes, 2013) and their on-going hybridisation (Klophaus et al., 2012; Morandi et al., 2015), estimating the extent to which self-help hubbing has evolved over time today allows a better understanding of whether this phenomenon has benefited passengers’ opportunity to travel inside the network. To the best of our knowledge, Maertens et al. (2016) is the only study attempting to show how self-connectivity has changed in the last decade, although limiting their analyses to one-stop connections and not relying on a quickest travel time approach. By implementing a more comprehensive and robust investigation of self-connectivity, this paper aims to estimate the overall evolution of self-help hubbing in the European air transportation network by disentangling the role played by LCCs and that of alliances. The purpose is to highlight the change in the degree of overall network coverage, and finally measure whether the level of betweenness centrality of European airports has changed. 3. Methodology This study employs the methodology by Malighetti et al. (2008) known as the quickest travel time approach. In the literature, there are several measures of connectivity. Some of them only consider the presence of scheduled flights, with no reference to the feasibility of connections in intermediate airports. Other, more complex models, instead, analyse the quality of connections, by estimating passengers' utility based on quality attributes, including waiting times and flight fares. More recently, Maertens et al. (2016) rely on OAG data and estimate the number of possible unique, weekly, onestop connections in the European low-cost and flag carriers' network. They specifically consider connections in both directions, with connecting thresholds between 45 min and 5 h, and a detour factor lower than 150%. Taking a global perspective and still applying limits to the detour factor and the maximum travel increase, Suau-Sanchez et al. (2016) attempt to evaluate the current development and implications of self-help hubbing for both airports and airlines. First, they implement a connection builder algorithm on OAG 2014 data to identify the set of valid travel itineraries; then, they analyse passengers’ choices developing a forecasting model aimed to identify airports with the highest selfconnecting potential. More in detail, they consider different measurable characteristics per route, such as the price (available for only 28.8% of MIDT itineraries) and the specific minimum connecting times of airports, which, however, may not always coincide with feasible self-help hubbing connecting times. Most recently, applying the same methodology, Suau-Sanchez et al. (2017)

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investigate the potential of self-connectivity of holiday destinations in the Mediterranean areas, highlighting that about 1.5% of passengers that book in European holiday markets are self-connecting today. Drawing upon these contributions, we compare our analysis with that carried out by Malighetti et al. (2008). They found that only 1/3 of the quickest 1-transfer connections available in the European airport network were offered within alliances in 2006. In this respect, we aim to understand the evolution of the European indirect market for connections in a ten-year period, from 2006 to 2016. The European aviation market profoundly changed over this period, with the expansion of the LCCs and the decrease in the offers by non-LCC carriers, which preferred to concentrate on more profitable long-haul intercontinental markets. This brought about a marked reduction in fares and an increase in passenger volumes. However, less predictable is the impact of the evolution of the intraEuropean market on the indirect connection markets, and the role still played by alliances. To facilitate the comparison, we also consider the same assumptions made by Malighetti et al. (2008), which are as follows: - a minimum connecting time (MCT) of 60 min for all intraEuropean connections; this period may still be considered acceptable for flights within EU.2 Self-connecting passengers generally need to perform additional activities once they connect in intermediate airports, as they do not have a single ticket to their final destination. That may involve exiting controlled areas and begin a new check-in and security controlled procedures for the next flight, with the exception of a few airports (Milan Malpensa and Gatwick) where self-connections are supported. However, if passengers do not carry hold luggage but only hand baggage, as for the majority of European passengers flying with LCCs (Maertens et al., 2016), the self-connecting process speeds up considerably. In this case, they may even avoid exiting the controlled areas as Schengen transfer passengers do not need to repeat security procedures. However, the feasibility of this strategy depends on the organization of passenger flows within the intermediate airports.3 There are airports, such as Amsterdam Schiphol, where the arriving passengers coming from Schengen areas mix with security controlled departing passengers. In this case, if transfer passengers have already performed the online check-in for the next flights, they may go directly to the gate. A sensitivity analysis on MCT is implemented to test the robustness of our findings (Section 4.2). - to guarantee the feasibility of connections, our analysis has been focused on actual scheduling for a specific and typical day in autumn: Wednesday, 12 October 2016, that is, almost exactly ten years after the day considered in the original study, Wednesday, 15 October 2006;

- we apply the time-dependent minimum travel time approach by Miller-Hooks and Patterson (2004); - we compute all possible minimum travel times in the network, STTijt, defined as the shortest travel time from airport i to airport j, starting at a specified time t; we only exclude the indirect connections when origin and arrival airports are located in the same area. To identify the overlapping area we employ a 100 km limit of road network, also including ferryboats, if available, in order to account for the geography of territories. For example, in case of North Scandinavia, relatively close airports (in terms of crow flies distance) cannot be considered in the same area due to the lacking of road and ferries connections.4 We do not need to include a specific routing factor limit because our approach focuses on the quickest connections. As a consequence, all connections having a long detour (e.g. flying from Amsterdam to Brussels airport through Rome Fiumicino) are naturally excluded, as there are much quicker, even direct, alternatives. For the same reason, we did not include a maximum connecting time limit either. If a specific connection makes passengers to wait several hours for the next flight to destination, either there are faster alternatives, or the connecting one is still the quickest, thus passengers have to wait patiently for it, as alternatives take more time or there are not alternatives. This approach has been classified as ‘a best in class’ methodology (Burghouwt and Redondi, 2013), and is particular suited to evaluate the efficiency of travelling into an airport network. Furthermore, differently from other simpler connectivity measures, it considers paths between origin and destination airports involving more than one stop. Even if most passengers in Europe travel with direct or one-stop flights, to reach small airports in remote regions, more than one-stop paths may be necessary, given the lack of alternatives. Optimal travel times include both flight time and waiting time in any intermediate airport needed to reach the final destination. This analysis also depends on the starting time of each flight. By taking an early flight from a generic airport, we should be able to reach more destinations. On the other hand, we may also experience an increase in our waiting time in intermediate airports. If we take a late flight, however, we may miss connections and fail to reach our destination. We included all European airports with scheduled flights in the period considered located in the 28 EU members, still comprising United Kingdom, plus Norway, Switzerland and Iceland, where air transport has been fully liberalized. There may be several O-D pairs that cannot be connected during the day, either because small airports with only a few flights per day are involved, or because of the lack of coordination in intermediate airports. We also compute the betweenness centrality of each intermediate airport. That can be defined as the number of times an intermediate airport lays on quickest paths between any O-D pairs. We employ betweenness as a quantitative measure of an airport potential for indirect connections. 4. Empirical analysis

2

Drawing on early research we consider a more conservative threshold (60 min) than that considered by past studies: 45 min (Maertens et al., 2016; Suau-Sanchez and Burghouwt, 2012; Burghouwt, 2007; Burghouwt and de Wit, 2005; Veldhuis, 1997). 3 As argued by Fichert and Klophaus (2016) it is always possible to self-connect two or more flights, especially those operated by LCCs, leading passengers towards some difficulties: 1) they have to bear the possibility of losing connections because of delays or the drop of flights; 2) in the large majority of the case they have to deal with luggage’ re-check and 3) the increase of ticket prices just after the booking of the first part of the connection, or the fact that self-connecting passengers have to pay a tax increasing the price of the tickets thus overcoming the price of a single ticket that provides the entire connection without self-connecting.

4.1. Evolution of the intra-European market The first part of the empirical analysis aims to reconstruct how the intra-European market evolved from 2006 to 2016. An in-depth understanding of the major factors driving market growth in terms

4 In presence of inter-model competition between air transport and high speed train (HST), some airport pairs above the threshold of 100 km may not be served.

M. Cattaneo et al. / Journal of Air Transport Management 65 (2017) 18e28

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Table 1 Offer variations in the European network between 2006 and 2016 by carrier groups. Carrier group

2006

2016

Change % 2006e2016

Flight number Offered seats Average stage Flight number Offered seats Average stage Flight number Offered seats Average stage length (‘000) (‘000) length (‘000) (‘000) length 833 1008 1431 1396 1084

94,392 109,733 151,898 207,571 91,801

1063 708 980 1278 1115

728 705 1196 2211 865

101,940 93,819 160,576 361,547 85,474

1374 907 1184 1450 1517


12.6%
30.1%
16.4% 58.3%
20.2%

8%
15% 6% 74%
7%

29% 28% 21% 13% 36%

European network 5753

655,395

1131

5705

803,357

1415


0.8%

23%

25%

OneWorld SkyTeam Star Alliance Low Cost Carriers Other Carriers

of direct flights offered is necessary to comprehend the development of indirect connectivity. Table 1 shows the evolution of the intra-European network, in terms of flight number, offered seats, and average stage length. Overall, interestingly, the number of flights offered decreased slightly from 2006 to 2016 (
0.8%: 5753 in 2006 vs. 5705 in 2016), while offered seats increased by almost 23% (655,395 in 2006 vs. 803,357 in 2016). This is the result of airlines employing larger aircraft and also denser seat configuration on average, even when offering short-to-medium haul flights in Europe. This, in turn, also facilitates airlines to connect directly more distant airports, as shown by the increase in the average stage length (þ25%: 1131 in 2006 vs. 1415 in 2016). The overall picture masks different trends for groups of airlines. The number of intra-European flights decreased significantly for the three alliances, while in the case of Skyteam, also offered decreased seats. On the contrary, the growth of LCCs has been remarkable, with offered flights and seat capacity increased by 58% (1396 in 2006 vs. 2211 in 2016) and 74% (207,571 in 2006 vs. 361,547 in 2016), respectively. As a result, the intra-European market share of LCCs5 in terms of seat capacity increased significantly from 31.7% in 2006 to 45% in 2016 (Table 2). On the other hand, while all three alliances decreased their shares on the intra-European network to focus on less-contended long-haul markets, Skyteam shows the highest losses, especially in terms of seat capacity, with a drop from 16.7% in 2006 to 11.7% in 2016. This was mainly due to the disempowerment of Alitalia, its de-hubbing from Malpensa and the choice of Air France-KLM to focus more on long haul routes (Air FranceeKLM, 2011). Table 3 compares the airports operating in Europe in 2006 and 2016. As for 2006, Malighetti et al. (2008) include in their analysis all airports with scheduled flights (478). We apply the same criteria for 2016, and include 480 airports. The majority of airports in both periods have an annual offer of seats in departure flights lower than 0.1 million seats. Specifically, in 2006, they accounted for 39.3% of the airports considered, but offered only 1.1% of the overall seat capacity. Their relevance in the European network has still decreased from 2006 to 2016, while their number remains almost unchanged (39%). In the latter year, they only account for 0.8% of seat capacity, corresponding to a decrease in offered seats of 12.2% (7.2 in 2006 vs. 6.3 in 2016) with respect to the initial year. Therefore, airports did not equally gain from the growth of the intra-European market. As a general result, small and very small airports suffered most, while the highest increases in capacity

5 In order to identify the LCCs active in 2006 and 2016 we integrate several sources: papers that analyse the LCC phenomenon published on scientific journals (Malighetti et al., 2008; Klophaus et al., 2012); the ranking of low cost traffic ranking published by Airline Business and the profile of LCCs provided by CAPA, Center for Aviation (www.centreforaviation.com).

regarded medium and large airports with annual intra-European seat capacity in departure flights higher than 5 million.

4.2. Evolution of indirect connections in Europe In this section, we aim to study the effect of the intra-European market development on indirect connections. For each O-D pair we compute the quickest paths by applying the time-dependent quickest path approach (Malighetti et al., 2008). We first compare the quickest connections available during the entire day with respect to the number of transfers involved. Panel A of Table 4 reports the results obtained by Malighetti et al. (2008) referring to 2006, including connections occurring with more than 2 transfers as, even if not as common, they still take place, especially when travellers cannot choose their destinations (e.g., business meetings, job interviews). We added the last columns to show the details for Ryanair and easyJet, the two major LCCs operating in Europe. Panel B shows the same figures for 2016. We can easily compute the degree of network coverage as the ratio between the number of O-D pairs connected and the number of possible O-D pairs. In a network of n airports, the latter is n*(n-1) less the number of O-D between airports located in the same area (see the methodology section). Thus, for 2006, the degree of coverage is 148,175/(478*477-1034) ¼ 0.653, meaning that, at the time, a passenger could successfully complete 65% of possible travels between any pair of EU airports within the day. In 2016, the degree of coverage decreased to 122.787/(480*479e943) ¼ 0.536. Therefore, although direct connections increased from 5790 to 5982 due to the expansion of LCCs through the establishment of new point-to-point routes, the number of 1-transfer indirect connections that could be successfully completed in a day dropped significantly by 9.5%. The main reason may lay in the declining role of small and very small airports (Table 3). Their number kept constant from 2006 to 2016, amounting to about 39% of the total number of airports, but their capacity shrunk. Traditional carriers reduced their presence in small airports to concentrate on more profitable routes with higher demand. This often resulted in small airports losing their flights to the alliances’ hubs, thus drastically decreasing their connectivity to the network. On the other hand, although LCCs initially expanded from secondary airports, they avoided very small airports, which, due to their insufficient infrastructures, were often unable to accommodate bigger aircraft, such as Boeing 737 or Airbus A320. Looking at direct connections, when direct flights are available, the contraction of alliances’ offers is evident. Specifically, in 2006 they offered 38.7% (2212) of direct quickest connections. The figure decreased to 32% (1915) in 2016, while the share offered by Ryanair and easyJet doubled from 14.3% to 28.6%. However, the expansion of LCCs is much less pronounced when looking at indirect connections. The three alliances even increased their share of 1transfer connections from 25.1% to 26.2%, but in absolute

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M. Cattaneo et al. / Journal of Air Transport Management 65 (2017) 18e28

Table 2 Market shares in the intra-European network in 2006 and 2016 by carrier groups. Carrier group

Market share 2006

OneWorld SkyTeam Star Alliance Low Cost Carriers Other Carriers

Market Share 2016

Offered flights

Offered seats

ASKs

Offered flights

Offered seats

ASKs

14.5% 17.5% 24.9% 24.3% 18.8%

14.4% 16.7% 23.2% 31.7% 14.0%

14.7% 13.3% 19.6% 37.6% 14.9%

12.8% 12.4% 21.0% 38.8% 15.2%

12.7% 11.7% 20.0% 45.0% 10.6%

12.4% 8.4% 16.0% 50.9% 12.2%

Table 3 Intra-European offers from the selected airports in 2006 and 2016, classified by seat capacity. Capacity on dep. flights (million seats)

2006

From

to

Airport number

% num

Offered seats

% seats

Airport number

% num

Offered seats

% seats

Airport number

Offered seats

0 0.1 0.25 0.5 1 3 5 10 20

0.1 0.25 0.5 1 3 5 10 20 40

188 70 51 50 63 20 20 12 4

39.3% 14.6% 10.7% 10.5% 13.2% 4.2% 4.2% 2.5% 0.8%

7.2 10.7 18.5 34.8 110.4 74.2 146.7 165.1 87.9

1.1% 1.6% 2.8% 5.3% 16.9% 11.3% 22.4% 25.2% 13.4%

187 76 41 48 61 20 24 16 7

39.0% 15.8% 8.5% 10.0% 12.7% 4.2% 5.0% 3.3% 1.5%

6.3 12.1 14.3 35.3 119.0 76.9 162.4 221.6 155.5

0.8% 1.5% 1.8% 4.4% 14.8% 9.6% 20.2% 27.6% 19.4%


0.5% 8.6%
19.6%
4.0%
3.2% 0.0% 20.0% 33.3% 75.0%


12.2% 13.3%
23.0% 1.4% 7.8% 3.7% 10.7% 34.2% 77.0%

478

100%

655

100%

480

100%

803

100%

0.4%

22.6%

European network

2016

Change % 2006e2016

Table 4 The number of minimum travel time paths offered by alliances’ networks in 2006 (panel A) and 2016 (Panel B). The first columns of panel A are from Malighetti et al. (2008), Table 5, p. 62. Data refer to two typical days in autumn: Wednesday, 12 October 2016 vs. Wednesday, 15 October 2006. Number of transfers

Joint alliances

One World

SkyTeam Star Network % operated by alliances

Operated by Ryanair

Operated by easyJet

% operated by Ryanair and EasyJet

Panel A - 2006 Direct 1 2 3 4 5 6

2212 9532 4250 593 8 e e

689 1989 667 297 4 e e

599 3150 919 115 4 e e

990 4444 2666 181 e e e

38.7% 25.1% 6.5% 1.7% 0.2% 0.0% 0.0%

494 2.006 241 7 e e e

324 112 e e e e e

14.3% 5.6% 0.4% 0.0% 0.0% 0.0% 0.0%

Total

16,595

3646

4787

8281 148,175 11.2%

2748

436

2.1%

Panel B - 2016 Direct 1 2 3 4 5 6

1915 9004 2963 206 3 0 0

605 1988 517 38 0 0 0

436 2589 427 58 0 0 0

916 4441 2019 110 3 0 0

32.0% 26.2% 5.6% 0.8% 0.1% 0.0% 0.0%

1083 2048 406 14 e e e

630 309 22 e e e

28.6% 6.9% 0.8% 0.1% 0.0% 0.0% 0.0%

Total

14,091

3148

3510

7489 122,787 11.5%

3551

961

3.7%

5709 37,986 64,887 34,470 4719 165 9

5982 34,376 53,172 24,836 4090 322 9

numbers we observe a decrease from 9532 to 9,004, due to the lower degree of network coverage in 2016. Therefore, in terms of available indirect connections, the European scenario deteriorated significantly from 2006 to 2016. The strong increase in LCCs capacity (Tables 1 and 2) did not benefit indirect connections. On the contrary, the alliances retreat from the European market to concentrate on intercontinental destinations caused a reduction of indirect connectivity, as the flights from secondary airports to their respective hubs reduced. The new pointto-point flights offered by LCCs could not offset the connectivity loss because of their lack of coordination, even when offered from

their major bases in Europe. Indeed, new indirect connectivity only occurred ‘by accident’, in some cases even against the strategy of LCCs, which often opposed the ‘self-help connections’ carried out by their passengers. Only recently, Ryanair and easyJet began evaluating the introduction of some forms of coordination to facilitate their passengers' indirect transfers. As we discussed in the methodology section, connecting time could vary. Therefore, we check the robustness of our results carrying out a sensitivity analysis of available indirect routes for different minimum connecting times, whose results are reported in Table 5. We increase the minimum connecting times only for self-

M. Cattaneo et al. / Journal of Air Transport Management 65 (2017) 18e28

18.0%

124,000

14.0% 120,000

12.0%

118,000

10.0%

116,000

8.0% 6.0%

114,000

% offered by alliances

16.0%

122,000

No. connec ons

connecting passengers, while maintaining the 60 min limit for transfer passengers managed by alliances or by traditional carriers. With respect to results of Table 4 for 2016, the overall number of quickest connections available decreases by 3.2% when MCT is set to 90 min (122,787 for 60 min vs. 118,808 for 90 min), and by 6.5% when MCT is set to 120 min (122,787 for 60 min vs. 114,766 for 120 min). As one would expect, the reduction regards only self-help connections, while quickest connections managed by alliances increase both in absolute and relative terms (they become more competitive with respect to self connections) with a share increasing from 11.5% in the base case, to 14.5% and to 16.3% in case self-connection MCT equals to 90 and 120 min respectively (see Fig. 1). Connections offered within the Ryanair and EasyJet networks reduce with longer MCT. Given those low-cost carriers do not yet manage intermediate passengers, we considered them as wholly self-connecting in the sensitivity analysis. When accounting only for quickest 1-transfer connections, the decrease in their number is more limited,
1.7% when MCT is set to 90 min (33,783 connections) and
5% when MCT is set to 120 min (32,660 connections). We maintain that a 60 min MCT may be adequate even for selfconnecting intra-European passengers. However, where it is not so, we think that the sensitivity analysis confirms the robustness of our results. In previous analysis, whose results are reported in panels A and B of Table 4, the airports were included in the intra-European network if they had at least one scheduled flight in both 2006 and 2016. By this criterion, the resulting networks are not perfectly overlapping (478 vs. 480 airports). Furthermore, there are several cases of small airports with a scheduled offer only in one of the two years. In order to compare the connectivity of European networks in 2006 and 2016 on more equal footing, we carried out a further analysis on the quickest paths, by considering only airports active in both years. As shown in Table 6, in this case, the airports considered were 432, with a total number of possible O-D pairs of 185,361 ¼ 432*431e831, being 831 the number of O-D pairs located in the same area and excluded from the analysis. The table compares the O-D quickest paths by the number of transfers required in 2006 on the rows, and by that required in 2016 on the columns. For example, there are 48,784 O-D pairs which did not have a connection in either year (no link-no link entry). On the diagonal of the matrix, there are the O-D pairs, which remained connected by the same number of transfers in 2006 and 2016. In the lower triangular matrix, we find the O-D pairs which improved their connections, with a lower number of transfers required in 2016 with respect to 2006, while in the upper triangular matrix the opposite case is reported.

23

4.0% 112,000

2.0%

110,000

0.0%

60

75

90

105

120

Minimum Connec ng Time (minute) No. Connec ons

% alliances

Fig. 1. Sensitivity analysis of the number of quickest connections and the share offered by alliances with respect to changes in the Minimum Connecting Time (MCT) for selfconnections. Data refer to Wednesday, 12 October 2016.

Overall, 57,281 O-D pairs did not have a connection in 2006. The number increased to 72,346 in 2016, confirming the previous result about the worsening of the degree of network coverage, mainly due to the reduction of capacity in small airports in 2016. Table 7, panel A, summarises the main findings of the comparison between the 2006 and 2016 networks. There are 67,459 O-D pairs (36.1%) that maintained a connection with the same number of transfers in both periods. However, the average travel times for those connections worsened by 9.5 min per connection on average. Therefore, although their quickest paths involved the same number of transfers, their travel times increased due to higher connecting times in intermediate airports. The number of O-D pairs with improved connections in 2016 (18,893) is greater than the number of O-D pairs involving a higher number of transfers, (18,166). However, the average reduction in travel times of the former is about 104.7 min, that is, less than the average increase in travel time due to the higher number of transfers required to complete the connection (144 min). The last row of Table 7, panel A, shows that the average travel time for O-D pairs which could be connected in both 2006 and 2016 increased by 12.3 min, mainly because of the effect of worsened coordination in intermediate airports. The last two columns report statistics about network coverage and travel time changes, both weighted by the size of the O-D pairs in terms of seat capacity. It is possible to note that O-D pairs unconnected in 2006 and 2016 represent only 2.1% and 2.4% of size-

Table 5 Sensitivity analysis of the number of quickest connections with respect to changes in the Minimum Connecting Time (MCT) for self-connections. MCT for connections offered by alliances remains at 60 min in all scenarios. Data refer to Wednesday, 12 October 2016. Year 2016

Minimum Connecting Time (MCT) for self-connections (minute) 60 (base case)

75

90

105

120

All transfer connections

No. Connections % to MCT ¼ 60 min % alliances % Ryanair % easyJet

122,787 0.0% 11.5% 2.9% 0.8%

120,810
1.6% 13.2% 2.9% 0.7%

118,808
3.2% 14.5% 2.6% 0.7%

116,852
4.8% 15.4% 2.5% 0.7%

114,766
6.5% 16.3% 2.4% 0.7%

1-transfer connections

No. Connections % to MCT ¼ 60 min % alliances % Ryanair % easyJet

34,376 0.0% 26.2% 6.0% 0.9%

34,159
0.6% 30.0% 6.2% 0.6%

33,783
1.7% 32.4% 5.2% 0.6%

33,291
3.2% 34.0% 5.0% 0.5%

32,660
5.0% 35.5% 4.7% 0.4%

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M. Cattaneo et al. / Journal of Air Transport Management 65 (2017) 18e28

Table 6 Comparison between 2006 and 2016 networks considering airports with scheduled flights in both periods. Data refer to two typical days in autumn: Wednesday, 12 October 2016 vs. Wednesday, 15 October 2006. 2016 Network

2006 Network

No link direct 1 transfer 2 transfers 3 transfers 4 transfers 5 transfers 6 transfers Total

No link

Direct

1 transfer

2 transfers

3 transfers

4 transfers

5 transfers

6 transfers

Total

48,784 157 3456 9853 8404 1629 63 e 72,346

68 3846 1621 310 25 e e e 5870

1020 1254 22,242 7848 618 15 e e 32,997

4084 190 6754 30,332 7280 207 e e 48,847

2826 16 613 6810 10,199 936 5 e 21,405

471 e 16 443 1794 833 28 e 3585

28 e e 8 119 140 7 e 302

e e e e e 8 1 e 9

57,281 5463 34,702 55,604 28,439 3768 104 e 185,361

Table 7 Comparison of network performances in 2006 and 2016 in terms of airport pairs (panel A) and of city pairs (panel B). Data refer to two typical days in autumn: Wednesday, 12 October 2016 vs. Wednesday, 15 October 2006. No. of pairs

% of total pairs

Change in travel times 2016e2006 (minutes)

% of weighted pairs

Weighted change in travel times 2016 e2006 (minutes)

58,544

31.4%

2.1%

73,609

39.4%

2.4%

O-D pairs with the same number of transfers of whom involving at least 1 transfer O-D pairs with a lower number of transfers in 2016 O-D pairs with a higher number of transfers in 2016

67,459 63,613 18,893

36.1% 34.1% 10.1%

9.5 10.1
104.7

76.8% 36.2% 13.7%

3.3 6.9
135.6

18,166

9.7%

144.0

6.3%

155.8

Overall change in travel times on O-D pairs connected in both 2016 and 2006

104.518 56.5%

12.3

97.2%


5.7

Panel A: O-D pairs O-D pairs with no direct and indirect connections in 2006 O-D pairs with no direct and indirect connections in 2016

Panel B: City pairs 55,062

33.5%

1.4%

69,023

42.0%

1.6%

City pairs with the same number of transfers of whom involving at least 1 transfer City pairs with a lower number of transfers in 2016 City pairs with a higher number of transfers in 2016

57,408 54,220 15,160

34.9% 33.0% 9.2%

10.1 10.7
98.6

82.7% 29.5% 10.1%

2.4 6.6
128.9

15,054

9.2%

140.8

5.2%

151.0

Overall change in travel times on city pairs connected in both 2016 and 2006

87,622

53.3%

12.6

98.0%


3.2

City pairs with no direct and indirect connections in 2006 City pairs with no direct and indirect connections in 2016

weighted airport-pairs respectively, confirming that they mainly involve small and very small airports. Interestingly, the weighted total travel times decreased by 5.7 min on each route. This means that, on average, O-D pairs that suffered the most severe increases in travel times involve secondary airports of lower size. However, the weighted travel time changes for indirect O-D pairs (i.e. more than direct) with the same number of transfers in both years increased by 6.9 min, as a consequence of the worsening coordination in intermediate airports, and still represent about 36.2% of size-weighted airportpairs. Overall, on average, travelling in Europe became more efficient in terms of weighted travel times. This is the result of the growth of direct connections especially by LCCs. However, when a direct flight is not available, travel times increase, as the new connectivity supplied by LCCs is of lower quality than that offered by traditional carriers. The analysis considers so far size-weighted airport-pairs.

However, in Europe there are several multi-airport systems with different airports serving the same city or territory. In these cases, the network coverage might be underestimated, as passengers have different alternatives to reach their destinations. For example, when considering travels between the Milan area and the London area, some airport pairs do not have a direct connection, such as from Bergamo Orio al Serio Airport (BGY) to London Heathrow (LHR). However, passengers can easily travel by car or public transport to an adjacent airport, as Milan Linate, which offers direct flights to London Heathrow, rather than choosing a timeconsuming transfer connection. Table 7, panel B, reports the main findings of the comparison between the 2006 and 2016 with a city-pair perspective. The network includes 406 different European cities, each served by at least an airport. The share of unconnected city-pairs increased from 33.5% (55,062) in 2006 to 42.0% (69,023) in 2016, confirming the decrease in network coverage also evident from the airport O-D analysis (panel A).

M. Cattaneo et al. / Journal of Air Transport Management 65 (2017) 18e28

In terms of performance, travel times on city pairs increased by 12.6 min, with respect to an increase of 12.3 min in case of airport O-D pairs. When considering travel times weighted by the size of the airport systems, travelling in Europe became more efficient,
3.2 min on average for each city pair, so confirming again results obtained for O-D pairs. However useful to confirm the robustness of our results, the figures about city-pairs travel time performances should be read with caution when comparing variations from 2006 to 2016. In fact, the specific O-D pairs necessary to link city-pairs may change over time, and so comparing travel times for city-pairs should also require an evaluation of the accessibility to reach the different alternative airports. Table 8 compares the different network paths in 2006 and 2016, by weighting the O-D pairs by seat capacity of the connected airports (panel A) and cities (panel B). As also remarked in Table 7 (panel A), the share of unconnected airport O-D pairs increased from 2.1% to 2.4% (from 58,544 to 73,609 pairs), confirming the decreasing coverage of the European network. On average, airports involved in unconnected O-D pairs have an annual seat capacity of 378,515 in 2016, corresponding to an increase of 19.2% with respect to 2006. By considering that seat capacity at a European level increased by 23% (Table 1), such airports grew less than the European average, as expected. When considering O-D pairs linked by direct flights (i.e. direct connections), their weighted share increased from 46.8% to over 50%, as a result of the low-cost expansion on the direct market. The net effect of the increased relevance of direct connections is a reduction in the share of 1-transfer and more than 1-transfer connections, as these O-D pairs obtain direct links. This is the main gain for travellers, since an increased share of the European market can be accessed directly by new flights, resulting in lower travel times. However, the remaining 1-transfer O-D pairs involved airports with a still significant size, over 2.7 million seats in departure flights per year, which increased by 24.8% from 2006 to 2016, more than the European average. The O-D pairs connected by 1-stop itineraries, accounting for 41.7% of the 2016 European market, are indeed those that suffered most from the worsening coordination

25

in intermediate airports. For the same reason, travel times increased even further for more than 1-transfer itineraries, although they are increasingly less relevant for travellers, with a weighted share of the European market of around 7% in 2016. The net effect is a decrease in the travel time, weighted by the size of O-D pairs, of about 5.7 min, as reported in Table 7 (panel A). The main developments from 2006 to 2016 are also confirmed when looking at city-pairs (Table 8, panel B). As one would expect, direct city-pairs represent a higher market share than direct O-D pairs, 60.2% against 50.1% respectively in 2016 as the availability of direct connections increases when considering the joined offers of airports serving the same city. As a result, the average sizes of cities, in terms of offered seats, are greater than in the case of airport O-D pairs. By considering airports offering scheduled flights in both periods, the comparison confirms the robustness of our previous results regarding the increasing number of O-D pairs with no available connections. Furthermore, Table 7 points out a worrying decline in coordination in intermediate airports and a general worsening of travelling in Europe when no direct flight is available. The last part of the empirical analysis compares the betweenness centrality of the European airports computed for 2016 with that obtained for the 2006 period by Malighetti et al. (2008). As introduced in the methodology section, the betweenness measures the number of times passing by an intermediate airport is required to complete the quickest path between any O-D pairs. Table 9 reports the average betweenness per period (from 6:00 to 24:00) for the 20 most relevant intermediate airports, by distinguishing the betweenness conveyed by the alliances’ network, and by Ryanair and easyJet network. Unsurprisingly, the European airports that intermediate the highest number of quickest paths are Amsterdam Schiphol, Oslo Gardermoen, and Stockholm Arlanda. In the case of Amsterdam, the airport has offered to passengers a high number of routes due to the still extended European network by KLM and other Skyteam members in addition to the fact that the its routing factor is far less than other big airports for several O-D city-pairs, such as Heathrow, because of its central geographical position in Europe The cases of Oslo Gardermoen and Stockholm Arlanda, instead, highlight their

Table 8 Comparison of network performances in 2006 and 2016 in terms of O-D pairs (panel A) and city pairs (panel B) weighted by the seat capacity of airports, and average seat capacities in departure flights of the connected airports and cities. Data refer to two typical days in autumn: Wednesday, 12 October 2016 vs. Wednesday, 15 October 2006. 2006

2016

Change 2006e2016

Panel A: O-D pairs No. of transfers O-D weight

Average airport size (seats) O-D weight

Average airport size (seats) O-D weight (percentage points)

% change airport size

Unconnected direct 1 2 3 4 5 6

317,607 5,920,255 2,215,549 747,896 334,971 229,249 173,640 129,966

378,515 7,263,375 2,764,835 866,805 385,608 277,633 273,022 161,157

19.2% 22.7% 24.8% 15.9% 15.1% 21.1% 57.2% 24.0%

2.1% 46.8% 43.3% 8.2% 0.9% 0.1% 0.0% 0.0%

2.4% 50.1% 41.7% 6.3% 0.6% 0.1% 0.0% 0.0%

0.3 3.3
1.6
1.9
0.3 0.0 0.0 0.0

Panel B: City-pairs No.of transfers City-pair weight Average city size (seats)

City-pair weight Average city size (seats)

City-pair weight (percentage points) % change city size

Unconnected direct 1 2 3 4 5 6

1.6% 60.2% 33.0% 4.8% 0.4% 0.0% 0.0% 0.0%

0.2 3.2
1.6
1.5
0.2 0.0 0.0 0.0

1.4% 57.0% 34.6% 6.3% 0.7% 0.0% 0.0% 0.0%

744,938 8,581,764 3,544,130 1,253,012 623,046 435,639 335,455 e

994,230 10,054,889 4,423,080 1,561,250 689,849 567,790 350,068 484,036

33.5% 17.2% 24.8% 24.6% 10.7% 30.3% 4.4% 0.0%

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M. Cattaneo et al. / Journal of Air Transport Management 65 (2017) 18e28

Table 9 Average number of quickest connections per period (from 6:00 to 24:00) for all network alliances, Ryanair, and easyJet. Comparison 2006 vs. 2016. Data refer to two typical days in autumn: Wednesday, 12 October 2016 vs. Wednesday, 15 October 2006. Betweenness

Alliances

Ryanair

easyJet

Airport

Code

Network (A)

Change 2006e2016

All alliances (B)

B/A

B/A 2006

Ryanair (C)

C/A

C/A 2006

easyJet (D)

D/A

D/A 2006

Amsterdam Oslo Stockholm ARN Munich Frankfurt Copenhagen Madrid Rome FCO Paris ORY Paris CDG Barcelona Helsinki London LHR Athens London STN London LGW Brussels Vienna Düsseldorf Berlin TXL

AMS OSL ARN MUC FRA CPH MAD FCO ORY CDG BCN HEL LHR ATH STN LGW BRU VIE DUS TXL

634 571 497 492 433 412 307 302 285 280 222 212 198 195 143 141 137 136 124 116

18% 91%
8%
2% 15% 50%
2% 22%
25%
28%
23%
50% 4%
2%
49%
1% 19%
6%
21% 58%

97 47 79 100 147 56 57 40 15 47 1 26 27 15 0 1 27 24 9 15

15% 8% 16% 20% 34% 14% 18% 13% 5% 17% 1% 12% 14% 8% 0% 1% 20% 18% 7% 13%

17% 16% 14% 24% 31% 16% 21% 18% 19% 27% 14% 7% 20% 0% 1% 13% 18% 22% 14% 9%

0 0 0 0 0 0 1 0 0 0 2 0 0 1 40 0 0 0 0 0

0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 28% 0% 0% 0% 0% 0%

0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 14% 0% 0% 0% 0% 0%

2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 10 0 0 0 0

0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 7% 0% 0% 0% 0%

0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% 0%

9006


11.1%

936

10.4%

14.4%

107

1.2%

0.6%

28

0.3%

0.1%

European network

prominent role as gateways to local national airports. A very similar ranking was obtained by Malighetti et al. (2008) for 2006. Overall, the average number of quickest paths per period intermediated by all European airports in 2016 is 9,006, corresponding to a decrease of 11.1% with respect to the 2006 level. The role played by alliances declined still further, passing from 14.4% of overall betweenness in 2006 to 10.4% in 2016. Ryanair and easyJet increased the betweenness conveyed by their networks. However, in 2016 the Ryanair network intermediated only 1.2% of the network betweenness, while easyJet only 0.3%. As most indirect travels in Europe involve 1-transfer paths and more than one stop is seldom required (mainly to access small peripheral airports), Table 10 reports the number of times a European airport intermediates quickest paths with one stop, that is, the so-called 1-transfer betweenness. Amsterdam Schiphol is again first, followed by the Franz Josef Strauss International airport (Munich) and the Frankfurt am Main International airport, two other airports with an extensive alliance network located in central Europe. The main hubs of the three alliances saw an increase in opportunities for 1-transfer quickest connections, with the exception of Paris Charles de Gaulle, in which that figure dropped by 33% with respect to 2006. This is indeed likely due to the fact that Air France has focused on long-haul routes in the most recent years (Air FranceeKLM, 2011) while LCCs (and Air France's subsidiaries operating local flights) have not remarkably increased the shorthaul counterpart at Paris Charles de Gaulle. The share of 1transfer quickest connections offered by the main alliance hubs varies between 45% of Paris Charles de Gaulle and 82% of Frankfurt. The latter figure is not surprising, as the Frankfurt airport remains the European hub most dominated by its major carrier, Lufthansa. Overall, the share of 1-transfer quickest self-connections increased by only 3pp, from 66% in 2006 to 69% in 2016. Therefore, the role of alliances in the European indirect market reduced from 2006 to 2016, and the opportunities for self-connection increased, but not to a great extent, as one would expect when observing the high growth of LCCs market shares (Table 2). The major issue for airports and airlines is how to tap the huge potential for indirect connectivity not exploited by alliances. One possibility would be for the major LCCs in Europe, especially

Ryanair and easyJet, to start offering connections to their passengers, as they have been recently considering. In this respect, the last columns of Table 10 show the 1-transfer betweenness potential for Ryanair and EasyJet. As of 2016, Ryanair is the second airline after the Lufthansa group by number of passengers, while easyJet is fifth after the OAG and Air France e KLM groups (ICCSAI Fact Book, 2016). Despite this, the two LCCs intermediate only 4% and 1.3% of available 1-transfer quickest connections, respectively. The vast amount of indirect connectivity not exploited by alliances arises by mixing uncoordinated offers of independent carriers. This may be defined as ‘by chance’ or random connectivity. If LCCs aim to increase the indirect connectivity potential they can offer to their passengers, they may consider improving their schedule coordination, by creating wave systems as in the case of traditional carriers. However, that may go against the maximisation of aircraft utilisation ratios, one of the key factors behind the LCCs phenomenon. Another possibility for LCCs to exploit indirect connectivity would be to make agreements with other carriers, to increase the likelihood of catching indirect connectivity. The airport operators may be in a better position to help passengers to self-connect by creating priority channels to speed up their security controls, to recover their luggage, and to check-in for their outgoing flights. Except for the expected traffic increase from this strategy, the airport operators can also make a profit by charging for this self-connecting service, which may include an insurance package against the risk of missing flights. The airports with the highest potential to exploit are those with an overlapping presence of different kinds of carriers, both traditional and LCCs, where the airport offer is not overly concentrated at an airline level. Some examples are Copenhagen Kastrup, Leonardo Da Vinci (Fiumicino) International, Barcelona International, and London Gatwick airports (Tables 9 and 10). However, to the best of our knowledge, despite different airports evaluated to introduce such a service, only two major European airports are still offering a self-connecting service, that is, London Gatwick (GatwickConnects) and Milan Malpensa (ViaMilano). In the last decade, LCCs have increasingly become very sensitive to the economic tide dropping and opening more routes than in the past.

M. Cattaneo et al. / Journal of Air Transport Management 65 (2017) 18e28

27

Table 10 Average number of 1-transfer quickest connections per period (from 6:00 to 24:00) for all network alliances, Ryanair, and easyJet. Comparison 2006 vs. 2016. Data refer to two typical days in autumn: Wednesday, 12 October 2016 vs. Wednesday, 15 October 2006. 1-transfer betweenness

Alliances

Airport

Code

Network (A)

Change 2006e2016

All alliances (B)

B/A

B/A 2006

Ryanair (C)

C/A

C/B 2006

easyJet (D)

D/A

D/A 2006

Amsterdam Munich Frankfurt Madrid Paris CDG Copenhagen Stockholm ARN Rome FCO Oslo Paris ORY Barcelona London STN London LGW London LHR Brussels Palma De Mallorca Vienna Zürich Düsseldorf Dublin

AMS MUC FRA MAD CDG CPH ARN FCO OSL ORY BCN STN LGW LHR BRU PMI VIE ZRH DUS DUB

173 137 117 84 81 73 72 70 63 58 54 47 42 40 32 32 31 28 28 27

47% 10% 31% 12%
33% 5%
31%
10% 43%
48%
19%
54% 6% 19% 25% 82% 11% 39% 10%
42%

82 62 96 42 36 21 28 29 13 11 0 0 1 20 15 2 12 14 3 0

47% 46% 82% 50% 45% 29% 39% 41% 20% 20% 1% 0% 1% 50% 46% 8% 39% 50% 12% 0%

41% 60% 79% 63% 50% 28% 19% 29% 28% 50% 34% 0% 19% 49% 11% 4% 55% 52% 31% 3%

0 0 0 1 0 0 0 0 0 0 2 31 0 0 0 1 0 0 0 7

0% 0% 0% 1% 0% 0% 0% 1% 0% 0% 3% 66% 0% 0% 0% 5% 0% 0% 0% 26%

0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 38% 0% 0% 0% 0% 0% 0% 0% 17%

1 0 0 0 1 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0

1% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 1% 20% 0% 0% 0% 0% 0% 0% 0%

0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 3% 0% 0% 0% 0% 0% 0% 0%

1734


9.0%

542

31%

34%

69

4.0%

2.6%

23

1.3%

0.5%

European network

Ryanair

This instability (de Wit and Zuidberg, 2012) has increased difficulties faced by airports when managing to coordinate the offers by different carriers.

5. Conclusion The increasing popularity of the self-connection alternative for passengers has been often associated with the important development of the LCC European network over the last decade. Previous research highlights the concrete possibility for passengers to implement a strategy of ‘self-help hubbing’, as two-thirds of the fastest indirect connections are found not to be operated by the alliance system (Malighetti et al., 2008). Starting from this, we analyse how this opportunity has changed along with the evolution of the European network. By implementing a quickest travel time approach, we consider the European market between 2006 and 2016 and examine the options of self-connectivity comparing the number of quickest connections remained un-managed. Our results point out that the airport coveragedthe number of airport pairs that can be connected with both direct and indirect paths, managed and selfconnected pathsddecreased from 65% to 53%. On the one hand, accounting for the importance of connected airport pairs, the data shows that, overall, travelling in Europe has become faster between 2006 and 2016 (
5.7 min). On the other hand, the percentage of unconnected O-D pairs has not definitively changed between 2006 (2.1%) and 2016 (2.4%). Our findings also suggest that the huge increase in LCCs' traffic (þ74% in seat capacity and þ58% in offered flights) has not been able to compensate for the change of focus of alliances from the European market to the intercontinental destinations. This was mainly due to lack of coordination of the LCCs' offer, as well as to a declining role of small and very small airports, which have not been able to accommodate bigger LCCs’ aircraft in physical or economic/ demand terms. Indeed, the share of 1-transfer quickest connections not operated by alliances (i.e. self-connections) increased by only 3pp, from 66% in 2006 to 69% in 2016. Finally, our analysis shows that even if Ryanair and easyJet would start to manage passenger

easyJet

transfers within their network, the interest towards selfconnections would not diminish. Indeed, at least in terms of quickest alternatives, their network only captures 5.3% of 1-transfer connections. Although the opportunities for self-connectivity have developed under the evolution of LCCs in the European domestic market, the way how the LCCs’ network has changed during the last decade provides some hints to better understand the phenomenon itself and its progress. On the one side, LCCs started to abandon smaller airports due to market saturation and regional factors. These airports are indeed located in smaller cities too far from the city center and with low numbers of residents and tourism/business activities (Dobruszkes et al., 2017). In the last decade, these factors have been furtherly exacerbated by the advent of the economic crisis as peripheral areas have suffered more in term of demographic diaspora and socioeconomic frictions and by the cuts of governmental subsidies (direct or through agreements with local or regional public bodies). On the other side, self-connectivity has benefitted from the fact that relying on LCCs has become the norm within Europe and not only for leisure but also for an increasing number of business passengers. This has been also facilitated by the fact that the economic difficulties associated to the economic crisis have increased consumers' price sensitiveness (Rajaguru, 2016). Still our analysis shows that self-connectivity is not simply a matter of if/when easyJet and Ryanair will start directly managing connections within their network: a big share of self-connectivity is indeed outside a specific airline/alliance despite the great expansion of these two major European low cost carriers. This suggests that today it is still quite complex to understand how and which players could take advantage of self-connectivity into the market. If LCCs and traditional airlines seem not really able to fully exploit these opportunities, also airports might face problems due to difficulties in informing passengers of the existence of airportmanaged connections and the handling of many associated tasks. In fact, passengers are seldom aware that the fastest voyage from A to B may pass through a third airport. Third parties, IT platform and/ or assurance companies, may be able to bring the information about existing opportunities to passengers and to cope with

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connection uncertainties better but, since they do not have control over handling, it is unlikely that they alone can improve the use of self-connections. Thus, only initiatives with multiple players are likely to further exploit these market opportunities. Ultimately, the analysis of the evolution of this dynamic highlights that the market has become more passenger-centric; although airlines struggle to set up the optimal solutions for their travellers, the network can intrinsically offer better opportunities (at least in terms of travel time). Furthermore, along with the drop of managed services by traditional carriers and the switch of LCCs towards bigger airports, self-connectivity results to be a phenomenon not exclusively related to small airports in remote regions, but, instead, it integrates the connectivity designed by alliances. References Air FranceeKLM, 2011. Annual Report 2010-11. http://www.airfranceklm.com/sites/ default/files/publications/rapport_annuel_2010-2011_va.pdf. Burghouwt, G., Redondi, R., 2013. Connectivity in air transport networks: an assessment of models and applications. J. Transport Econ. Pol. 47 (1), 35e53. Burghouwt, G., 2007. Airline Network Development in Europe and its Implications for Airport Planning. Ashgate Publishing, Ltd. Burghouwt, G., de Wit, J., 2005. Temporal configurations of European airline networks. J. Air Transport Manag. 11, 185e198. de Wit, J.G., Zuidberg, J., 2012. The growth limits of the low cost carrier model. J. Air Transport Manag. 21, 17e23. Dobruszkes, F., 2013. The geography of European low-cost airline networks: a contemporary analysis. J. Transport Geogr. 28, 75e88. Dobruszkes, F., Givoni, M., Vowles, T., 2017. Hello major airports, goodbye regional airports? Recent changes in European and US low-cost airline airport choice. J. Air Transport Manag. 59, 50e62. Fichert, F., Klophaus, R., 2016. Self-connecting, codesharing and hubbing among

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