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Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019

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Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019 Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019 Connectivity Developments in Air Transport Networks at Primary Asian Airports Hidenobu MATSUMOTO a, Koji DOMAE b a Graduate School of Maritime Sciences, Kobe University, Kobe, 658-0022, Japan; E-mail: [email protected] b College of Global Communication and Language, Kansai Gaidai University, Hirakata, 573-1008, Japan; E-mail: [email protected] Abstract: This paper measures and compares the connectivity developments in air transport networks at the primary airports in Asia. To determine how the connectivity at these airports has developed in the specific markets, the connectivity figures are broken down by regions. For an assessment of the model and its application, the paper conducts scenario analyses for Chubu Centrair International Airport in Nagoya, Japan, on the connectivity impacts of an additional flight from this airport to large hub airports in Europe, North America and Asia, and of moving all domestic flights from Nagoya Airfield, the other airport in Nagoya, to this airport. The results reveal that the most striking growth of air network connectivity developments has been found at the three airports in Mainland China (Beijing, Shanghai and Guangzhou) and Tokyo International Airport. The model is helpful for airports to assess their network performance and their competitive hub status vis-a-vis other airports. Keywords: Air network performance, Competitive hub status, NetScan connectivity model, Scenario analysis, Chubu Centrair International Airport, Asia 1. INTRODUCTION The growth of hub-and spoke operations has changed the competition among airports in a structural way. Due to the rise of hub-and-spoke networks, airlines compete directly as well as indirectly. Traditional measures on airport performance, such as passenger enplanements and aircraft movements, fail to address in particular indirect connectivity via hubs. To date, many studies have analyzed hub-and-spoke networks. One branch of research is from the viewpoint of economic perspectives, with a focus on economies of density and scope (Caves et al., 1984; Brueckner and Spiller, 1994), hub premiums (Borenstein, 1989; Oum et al., 1995), entry deterrence (Zhang, 1995) and the role of hub-and-spoke networks in airline alliances (Oum et al., 2000; Pels, 2001). Another branch of research is the field of operations research, where the cost-minimizing approach is used to determine spatial optimization of air networks (Kuby and Gray, 1993; O’Kelly and Miller, 1994; O’Kelly and Bryan, 1998). A third branch uses the geographical approach, in which the structures, performance and spatial dimension of hub-and-spoke networks are analyzed empirically (Ivy, 1993; Shaw, 1993; Bania et al., 1998; Burghouwt et al., 2003). These studies, however, take into consideration air traffic flows purely from the demand aspect, without capturing the airline network structures, schedule coordination and its resulting hub performance from the supply aspect. Consequently, some studies have included the level of schedule coordination in the measurement of performance and structure of hub-and-spoke networks. Veldhuis (1997) Corresponding author. 2240 Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019 analyzes Amsterdam Airport Schiphol, focusing on the quality and frequency of indirect connections. Burghouwt and Veldhuis (2006) evaluates the competitive position of West European airports in the transatlantic market from this viewpoint, followed by Burghouwt et al. (2009) and De Wit et al. (2009) which assess the competitive hub status of primary airports in East and Southeast Asia. The main purpose of this paper is to measure and compare the air network performance and competitive hub status of primary airports in Japan and elsewhere in Asia between 2001 and 2017. Its special focus of attention is Chubu Centrair International Airport in Nagoya, Japan. In this paper, the NetScan connectivity model is used to measure the connectivity developments at these airports, taking into account the quantity and quality of both direct and indirect connections. For an assessment of the model and its application, the paper conducts scenario analyses for Chubu Centrair International Airport on the connectivity impacts of an additional flight from this airport to large hub airports, and of moving all domestic flights from Nagoya Airfield, the other airport in Nagoya, to this airport. The remainder of this paper is organized as follows. The next section provides an overview of the NetScan connectivity model. In Section 3, the connectivity developments in air transport networks at the primary airports in Asia are measured and compared. In Section 4, after describing the dual airports system in Nagoya Metropolitan Area, scenario analyses are conducted on the connectivity impacts of an additional flight to a large hub airport in Europe, North America and Asia, and of moving domestic flights from Nagoya Airfield to Chubu Centrair International Airport, followed by discussion and conclusion in Section 5. 2. MEASUREMENT OF NETWORK QUALITY 2.1 Four Types of Network Connectivity In our approach, four types of network connectivity are distinguished as described in Figure 1. 1. Direct connectivity: flights between airports A and B without a hub transfer 2. Indirect connectivity: flights between airports A and B, but with a hub transfer at airport H 3. Onward connectivity: connections with a hub transfer at airport B between airports A and D 4. Hub connectivity: connections with a hub transfer at airport A between airports C and B Direct connectivity A B Indirect connectivity A H B Onward connectivity A B D Hub connectivity C A B Figure 1. Four types of network connectivity Note: This paper does not consider onward connectivity. 2241 Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019 The quality of an indirect connection between airports A and B with a hub transfer at airport H is not equal to the quality of a direct connection between airports A and B. In other words, the passenger traveling indirectly will experience additional costs due to longer travel times, consisting of transfer time and detour time. Transfer time equals at least the minimum connecting time, or the minimum time needed to transfer between two flights at airport H. The measurement of indirect connectivity is particularly important from the perspective of passenger welfare; how many direct and indirect connections are available to passengers between airports A and B? The concept of hub connectivity is particularly important for measuring the competitive hub status of airports in a certain market; how does airport A perform as a hub in the market between airports C and B? 2.2 Concept of Connectivity Units Many passengers transfer at hub airports to their final destinations, even in case good direct connections are available. Passengers’ choices depend on the attractiveness of the available alternatives. When measuring the attractiveness of a certain alternative, we consider frequencies and travel time. As for fare differentiation, fares on non-stop direct routes are generally higher than those on indirect routes. Fares on indirect routes are generally lower for on-line (or code-shared) connections than for interline connections. Fares on a route are generally lower if more competitors are operating on these routes. And finally, fares are ‘carrier-specific’ and are depending on the ability of carriers to compete on fares. Therefore, it can be concluded that fares are generally depending on the number of competitors on the route and the product characteristics, like travel time, number of transfers, kind of connection (on-line or interline) and the carrier operating on the route. So, fare differentiation is partly reflected in the route characteristics. The route characteristics mentioned are to be operationalized in a variable indicating connectivity, expressed in so called ‘connectivity units (CNU’s)’. This variable is a function of frequencies, travel time and the necessity of a transfer. 2.3 NetScan Connectivity Model The NetScan connectivity model, developed by Veldhuis (1997), has been applied here to quantify the quality of an indirect or a hub connection and scale it to the quality of a theoretical direct connection. The model assesses the level of direct connectivity based on the Official Airline Guide (OAG) flight schedules. Based on the direct connections, the model builds viable indirect and hub connections. The model weighs these for their quality based on transfer time and detour time involved, which results in the level of indirect and hub connectivity provided. Figure 2 shows the scheme of NetScan Model. First, direct connections have been retrieved from the OAG flight schedules (Step 1). Then, indirect and hub connections have been constructed using an algorithm, which identifies each incoming flight at a hub airport and the number of outgoing flights that connect to it. The algorithm takes into account the minimum connecting time and puts a limit on the maximum connecting time. In our case, we assume 30 minutes between domestic connections and 45 minutes between domestic and international connections and between international connections for the minimum connecting time and 420 minutes for the maximum connecting time. Next, NetScan assigns a quality index to every individual connection, ranging from 0 to 1 (Step 2). A non-stop direct connection is given the maximum quality index of 1. The quality 2242 Journal of the Eastern Asia Society for Transportation Studies, Vol.13, 2019 index of an indirect or a hub connection will always be lower than 1, since extra travel time is added due to transfer time and detour time for the passenger. The same holds true for a multi-stop direct connection. Passengers face a lower network quality because of en-route stops compared to a non-stop direct connection. If the additional travel time of an indirect or a hub connection exceeds a certain threshold, the quality index of the connection equals 0. The threshold between two airports depends on the travel time of a theoretical direct connection between these two airports.
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