Connectivity of intercity passenger transportation in China: A multi-modal and network approach

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Connectivity of intercity passenger transportation in China: A multi-modal and network approach Zhenran Zhua, Anming Zhanga,c,⁎, Yahua Zhangb a b c

Sauder School of Business, University of British Columbia, 2053 Main Mall, Vancouver, BC V6T 1Z2, Canada School of Commerce, University of Southern Queensland, Toowoomba, Queensland, Australia China Academy of Financial Research, Shanghai Jiao Tong University, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Connectivity Intercity passenger transportation Air and rail transport Node connectivity Link (route) connectivity

This paper proposes a dynamic weighted model to measure the connectivity of intercity passenger transportation in China. We consider both quality and quantity of the connections of two transport modes: air and rail. Among the 23 major cities selected, Shanghai is revealed to have the highest connectivity level, leading in both air and rail connectivity. Hong Kong, Kunming, and Urumqi are the three cities that predominantly rely on air transportation whose contribution to the connectivity exceeds 80%. This research also suggests that the connections between international cities and China's domestic network are highly concentrated on a few cities, namely, Hong Kong, Shanghai, Beijing, and Guangzhou, and that Seoul is the best connected international city in terms of its transport links with China. Shanghai-Nanjing has been found to be the best-connected city pair, primarily due to the significant contribution from high-speed rail (HSR) service. Our study shows that the contribution from train service is more than 80% for 19 of the 20 top-ranking domestic routes measured by connectivity. In addition, HSR has become a preferred and dominant option over air on a number of longdistance routes up to 1,300km. This finding has significant policy implications for transportation infrastructure planning and investment.

1. Introduction The landscape of China's transportation network has been constantly shaped by the fast-growing air transportation industry and the continually evolving rail system in the last three decades. The change has been revolutionary in recent years with the introduction of the high-speed rail (HSR) services and the proposed “one belt one road” (OBOR) initiative, which involves heavy investment and planning in transportation infrastructure including HSR, international rail, ports, and airports, with an aim to build better connections between China and the rest of the world. Many Chinese cities are embracing this idea and seizing the opportunity to redesign their long-term planning and investment strategies, in the hope of aligning themselves with the OBOR blueprint and thus attracting more transportation infrastructure investment funds. However, the resources are scarce and limited. It is important first to assess the accessibility and connectivity of the existing transportation network, and to understand the current status that each city plays to make sure that the resources will be put to more efficient use in promoting the connectivity of domestic cities and building a better connected transportation network.

⁎

There is a strong relationship between transportation connectivity and regional economic development. It is a widely held view that as an input into many economic activities including tourism, trade and investment, an efficient transportation system has been an important component in achieving economic development and welfare enhancement. For example, air transportation is particularly important to distant and remote regions where there is no close substitute for this transport mode due to the tyranny of distance. A large volume of literature has reported the causality relationship between transportation infrastructure and local economy (e.g., Li and Qi, 2016, and studies cited therein), and air accessibility has a significant impact on GDP, employment, regional development, and foreign direct investment (FDI) (e.g., Brueckner, 2003; Basile et al., 2006; Zhang, 2012; Banno and Redondi, 2014). Tanaka (2016) notes that despite the advances in information and communication technology that have reduced the barriers to acquiring codified and explicit information on foreign market, in-person meetings are still crucial for building business relationships and managing production activities. Banno and Redondi (2014) argued that the introduction of a new route would lead to a reduction in total transportation costs and facilitate knowledge flow,

Corresponding author at: Sauder School of Business, University of British Columbia, 2053 Main Mall, Vancouver, BC V6T 1Z2, Canada. E-mail addresses: [email protected] (A. Zhang), [email protected] (Y. Zhang).

http://dx.doi.org/10.1016/j.jtrangeo.2017.05.009 Received 18 January 2017; Received in revised form 16 April 2017; Accepted 23 May 2017 0966-6923/ © 2017 Elsevier Ltd. All rights reserved.

Please cite this article as: Zhu, Z., Journal of Transport Geography (2017), http://dx.doi.org/10.1016/j.jtrangeo.2017.05.009

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business opportunities (e.g. if a city is ranked low, there may be opportunities for improvements and investments), and comparing alternatives and choosing the best option. Given that connectivity has been an important performance indicator for an airport or a city (like efficiency and productivity), in this article we attempt to develop a dynamic connectivity measure to capture the aggregated connectivity of multi-transport modes including both air and rail services. Our connectivity measure incorporates both quality and quantity of the connections, which will be used to evaluate the performance of 23 major Chinese cities and 13 international cities. The rest of the article is organized as follows. Section 2 presents the research methodology and data. Section 3 reports the results and discusses, in particular, the domestic city (node) connectivity and route (link) connectivity. The last section contains concluding remarks and policy implications.

thereby increasing the likelihood of FDI exchange between newly connected areas. Cristea (2011) provided evidence for the importance of in-person business meetings in international trade, and pointed out that interactions among trade partners generate relationship capital, which adds bilateral specific value to the traded products. It is thus expected that improved transportation connectivity of a city generates significant economic benefits not only to individual travelers but also to businesses and local economy. In transportation, the concept of connectivity was first introduced to evaluate the importance of an airport in terms of its connection to other airports. Scholars have developed various airport connectivity measures, representing the degree to which an airport is connected to other airports in a given network. This will help policy makers monitor the network performance against that of other airports, airline networks and regions, and design strategies to improve the competitive positons of airports (Burghouwt and Redondi, 2013). Passengers tend to have different preferences over travel time, travel distance and destinations, as well as other travel dimensions. Therefore, different connectivity measures have been developed, taking travel purposes and many other factors into consideration with more detailed and quantitative data to make the measure objective. Burghouwt and Redondi (2013) conducted a good review of the various measures for the air connectivity, including the shortest path length accessibility model (Shaw, 1993; Shaw and Ivy, 1994; Malighetti et al., 2008), the quickest path length accessibility model (Malighetti et al., 2008; Paleari et al., 2010), the weighted number of connections model (WNX) (Burghouwt and de Wit, 2005), and the Netscan connectivity unit (NCU) model (Veldhuis, 1997; Burghouwt and Veldhuis, 2006; Veldhuis and Kroes, 2002; Matsumoto et al., 2008). Boonekamp and Burghouwt (2017) note that more recently developed models incorporate the connection quality, frequency, importance of the destination, etc. However, most of the existing studies only focus on the connectivity of a single transport mode, while in many countries, there exist several main transport modes which complement each other. This is especially so in China where air transportation and rail services are the two dominant transport modes for long-distance travels. Many studies have shown that HSR and air transport are potential substitutes. In the case of China, for example, Fu et al. (2012) indicated that the demand for airline flights between Wuhan and Shanghai/Nanjing/Hangzhou fell sharply following the launching of HSR between these cities in 2009. As a result, airlines cut services between Wuhan and the Yangtze River Delta area by about one-third.1 It is, therefore, necessary to consider both transport models when considering a city's connectivity. A connectivity measure based on any one model alone may not provide accurate and useful information to decision makers. Furthermore, government initiatives such as OBOR will have an impact on the spatial transformation of transport systems in the form of, e.g., connectivity changes. To see such an impact, measuring connectivity is a starting point: Connectivity between two cities shows the convenience of transportation between the city pair and also, reflects how much the two cities rely on (and relate to) each other in an economy, whereas connectivity between a city and all other cities in the network shows the degree of the city's importance in the network. Quantifying, and visualizing the overwhelming connection data with dynamic models will trace the spatial transformation of transport systems, which often is influenced by various government initiatives. They in turn will assist governments for 1) better monitoring and planning of their initiatives; and 2) optimal management and control of the transformation. The implications for management and investors include: facilitating networking and routing decisions, identifying

2. Methodology and data 2.1. Background and the choice of cities for this study Both the airline and rail markets in China are experiencing fast expansion, underpinned by a huge population, rapid economic growth, and policy reforms (e.g., Lei and O'Connell, 2011; Fu et al., 2012; Zhang and Zhang, 2016). In 2015 the length of new railways put into operation totaled 9531 km, of which HSR measured 3306 km. By the end of 2015, the length of China's railways reached 121, 000 km, ranked the second largest rail network in the world after the United States (US), while the HSR system, whose length amounted to 19,000 km, ranked number one (in effect, it was greater than the HSR system of the rest of the world combined). The volumes of passenger and freight traffic delivered by the rail system were also the largest in the world. In 2015, eight new airports were put into use, bringing the total number of China's civil aviation airports to 210. In 2015 China's air transportation industry handled 435.6 million passengers and 6.3 million tons of air cargo, an 11.1% and 5.2% increase from the previous year, respectively. The International Air Transport Association (IATA) forecasts that China will overtake the US as the largest air passenger market by around 2030 as measured by traffic to, from and within a country. Considering the large size of the air and train services data, we only select 23 domestic cities, which include Hong Kong Special Administration Region, and assess their connectivity on the basis of their significance and influence to the surrounding areas. Hong Kong is a leading international hub in terms of air passengers handled and the world's number one measured by international air cargo volume (Wan and Zhang, 2017). The other 22 cities have well-developed multi-modal transport system serving their home province and neighboring provinces: they together accounted for 31.5% of China's GDP (excluding Hong Kong) and 17.2% of its population in 2014.2 Together, these 23 cities and the flight and train links between them form a representative network of all Chinese major cities. The locations of the 23 cities are shown in Fig. 1. A map of China's planned HSR network including completed and planned projects is shown in Fig. 2. In addition, 13 international cities are selected based on the ranking of their air passenger throughput, locations, and the economic and regulatory connections with China.3 These international cities are included to provide the show case of international connectivity. Table 1 explains the rationale of the choice. 2 A city's GDP and population include the statistics of the districts, counties and countylevel cities that this city governs. 3 As discussed in Zhang and Chen (2003), the international airline markets used to be heavily, and unevenly, regulated between China and other countries. Fortunately, after several rounds of liberalization in bilateral air service agreements, by 2016 (our sample year) the markets are fairly liberalized in the 3rd and 4th freedom rights for the sample international cities (Lei et al., 2015) which facilitates our analysis with these cities.

1 See also Wang et al. (2017). For works on HSR, as well as HSR-air transport interactions, in Japan and European countries, see, e.g., Gutierrez (2001), Givoni (2006), Yamaguchi et al. (2008), Dobruszkes (2011), Ha et al. (2011), Behrens and Pels (2012), Givoni and Dobruszkes (2013), Clewlow et al. (2014), Fu et al. (2014), Cheng et al. (2015), Perl and Goetz (2015), and Wan et al. (2016).

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Fig. 1. Locations of selected domestic cities.

Fig. 2. Planned HSR network in China. Adapted from SDPC (2016).

The data for the flight frequency were obtained from the website of FEEYO, managed by the Civil Aviation Resource Net of China. FEEYO is the largest online platform specializing in air passenger service and flight data analysis in China. By cross-checking with other major flight information sources, such as Ctrip, Qunar, and AliTrip, we found that FEEYO's schedule data are the most complete and accurate among those available to us when we started this project. The flight information also contains stops and on-time (punctuality) performance rates. We later doubled checked the flight schedule information in IATA's Airport Intelligence and confirmed the reliability of the FEEYO data. The data

source for the train services is the website of 12306, the only official train service online booking website operated by the Ministry of China Railways. The data were extracted from the two websites on May 11, 2016 for all the routes out of the cities under study. R was used for data screening and calculation. For code-shared flights, we only consider the operating airlines. For air connections, when FEEYO indicates that a flight has an intermediate stop, this flight will be counted as an indirect flight from origin to destination, and a direct flight from origin to the intermediate point, as well as a direct flight from the intermediate point to the final 3

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Table 1 Cities chosen for this study. City

Choice rationale

Beijing, Tianjin

Beijing and Tianjin are both municipalities directly under the central government. The two cities are linked by HSR that allows the intercity travel to be within 30 min. They both have well developed airports and rail systems connecting other parts of China. Dalian and Shenyang are two of the major cities in Northeast China. Both of them are sub-provincial cities, which means that their importance is only slightly lower than Beijing, Tianjin, Shanghai and Chongqing, which are directly controlled by the central government. Both Shenyang and Dalian have well-connected air transport and rail systems. These four cities are major cities in the Yangtze River Delta in East China, the most developed and wealthy area in China. Shanghai is China's economic and financial center. Nanjing, Hangzhou and Ningbo are all sub-provincial cities. They are the leading cities in the Pearl River Delta in South China, another well-developed and wealthy zone in China. Guangzhou and Shenzhen are both sub-provincial cities. Hong Kong is the center of finance, transportation and logistics in Asia. They are respectively the capital cities of Henan Province, Shaanxi Province, Hubei Province and Hunan Province. These cities are linked by HSR. They are also key air transportation hubs in central China. Both cities are important commercial cities in West China with a long history. Chongqing is the largest city in China in terms of the population and land area. Chengdu is the capital city of Sichuan Province, and a sub-provincial city. They are the capital cities of Guizhou Province, Yunnan Province, and Guangxi Province, respectively. Xiamen is a sub-provincial city, a famous tourism city in Southeast China on the west side of Taiwan Straights Urumqi is the capital of Xinjiang Uyghur Autonomous Region. It's the largest city in Xinjiang and the only large city that is close to China's Northwest border. It has close commercial, cultural and transportation links with Central Asia. Yining is a relatively young and small city, while its location is strategically important as it is the gateway to Central Asia and Europe. All of them are important cities in East Asia with strong cultural and economic links with China. These cities are also the main tourism destinations for Chinese tourists. Connectivity of these cities with China shows the passenger travel pattern between China and the neighborhood region of East Asia.a These cities are major international air transportation hubs. Connectivity of these cities with China represents the passenger travel pattern between China and global areas on long-distance routes.

Dalian, Shenyang

Shanghai, Nanjing, Hangzhou, Ningbo Guangzhou, Shenzhen, Hong Kong

Zhengzhou, Xi'an, Wuhan, Changsha Chongqing, Chengdu Guiyang, Kunming, Nanning Xiamen Urumqi, Yining

Seoul, Taipei, Tokyo, Singapore, Osaka, Nagoya

New York, Los Angeles, Atlanta, London, Paris, Frankfurt, Dubai

a We acknowledge that the choice of international cities is still somewhat arbitrary, although we have considered their geographic locations, economic significance and economic ties with China that have been found to be important for transport connectivity (e.g., Gutierrez, 2001; Geurs and Van Wee, 2004). However, one of the main purposes of this paper is to present a new connectivity model. Using a simplified transport network eases the data calculation burden and the simplified network still gives a good representation of the reality.

destination. In our flight data, 655 flights out of 6588 have one intermediate stop.4

which is a standard practice in the literature. Similarly, transfers between trains5 or between air and rail are not considered in this study. This is a realistic assumption as these two practices are not common in China.6 It is understood that without considering these transfers, the connectivity level of a city, especially those cities relying on another hub city, is underestimated. However, we believe that this bias is minor for the network considered in this paper: the underestimation for the connectivity between domestic cities is slight, because all the cities in the domestic network under study are regional hubs and the contribution of indirect connections to the overall connectivity is limited, owing in part to the poor quality of such indirect connections. Fifth, we assume that a rail connection between two major cities has a capacity equal to that of Airbus 380 (A380). Normally the capacity of a train is 2 to 4 times as large as that of an A380. There are 506 seats on a typical A380. A basic unit of China Railway High-speed (CRH), which is the mainstream train type for HSR services, has 494 to 610 seats. A CRH train (i.e., D-train or G-Train)7 between major cities usually operates with 2 units, i.e., 988 to 1220 seats per train. However, as there are multiple stops between the origin city and destination city, and we are unable to obtain the accurate information about the capacity used for transporting people between origin and destination,

2.2. Assumptions made for constructing our connectivity model Any connectivity measure involves some realistic assumptions so as to simplify the model such as the maximum transit time and maximum number of steps allowed (Burghouwt and Redondi, 2013; Boonekamp and Burghouwt, 2017). The following assumptions are made in constructing our connectivity measure. Frist, we only consider the passengers traveling by air and by train although intercity passenger transport modes include means of air, rail, highway, and water. However, for long-distance travel, the vast majority of Chinese passengers use air and train services. Also due to the unavailability of data for highway and water transportation, these transportation means are not included in our connectivity measure. Second, we only consider a network formed by the 23 domestic cities and 13 international cities, meaning that only connections between the 23 domestic cities and connections between the 23 domestic cities and the 13 international cities are considered. Connections between international cities are not considered. Third, for the schedules of flights and trains, it is assumed that they are cyclical with a cycle of one week. It is recognized that there are frequent delays in air transportation. However, due to the unavailability of flight delay data, flights and trains are all considered to depart/arrive on time as scheduled. Fourth, for indirect travel by air, we only consider scheduled transfers noted in the database of FEEYO. Transfers that are arranged by passengers themselves (so-called “self-connecting”) are not counted,

5 Rail-rail transfers (especially for K-train services with a speed not more than 120 km/ h) is unusual for the following two reasons. Firstly, purchasing indirect ticket bundles is not supported by the official platform. Tickets must be bought separately for every section. Secondly, end-to-end services are available for most city pairs as long-haul transportation is the major concern for China Rail. Short-haul end-to-end trains are only applied to a few routes, like Beijing-Tianjin, Shanghai-Nanjing, etc. However, with the extension of the HSR network, more and more time-sensitive passengers choose to travel to their destinations via HSR with one or two transfers even though the direct K-train services are available between the origin and destination. This change should be considered in future research. Without considering transfers between trains, rail connectivity of some cities especially those such as Wuhan and Zhengzhou will be underestimated. 6 In particular, the unpopularity of air-rail transfers is due largely to the lack of integration of the two transport modes (Chen and Lin, 2016; Xia and Zhang, 2016b). 7 D-train operates at a speed between 200 and 250 h while the maximum speed of HSR train (G-train) is usually at 300 km/h.

4 Our results suggest that flights with one stop account for 9.94% of the total flights counted by flight number, 8.96% of the total flights counted by frequency, 5.87% of the total air connectivity considering quality discount of aircraft size and travel length. The importance of indirect connectivity for one specific route is significantly correlated with the natural logarithm connectivity level of the route, with p-value = 0.000 and Pearson correlation = −0.765. Indirect connections are more important for weakly connected routes, although its impact on the overall connectivity is negligible.

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We first retrieved the connection data from the websites of FEEYO and 12306, including flight number/train number, aircraft type, origin city, destination city, scheduled departure time, and scheduled arrival time. Next, we assign discount factors, ranging between 0 and 1, to every connection. The discount factors for air connections include A capacity discount (denoted as DxyaC), travel time discount (DxyaT ), and velocity discount (DijV). The discount factors for train connections are T travel time discount (DxybT ), and velocity discount. C Dxya is used to normalize the effect of different sizes of the aircraft on the capacity of the connection. A flight using aircraft A380 and a flight using aircraft A320 provide different levels of capacity. DxyaC is calculated based on the aircraft type used in the connection and the number of seats offered, as shown in Eq. (1) below. For flight a from city x to city y, DxyaC is the square root of the ratio of the number of seats on this flight to the number of seats of the largest aircraft. This function form allows routes with higher frequency to have higher connectivity, ceteris paribus. That is that a flight with 200 seats generates smaller connectivity than two flights with 100 seats each. When flight a operates with the biggest aircraft, the capacity discount for flight a is 1. The capacity discount is 1 for all train connections as the capacity of train connections between two cites is assumed to be equivalent as an A380.

it is reasonable to assume that half of the G- or D-train capacity, equivalent to the capacity of A380, is allocated to the origin and destination cities.8 Other main types of trains operating in China such as K-train9 usually operate with 16 cars, with 110 to 128 seats per car, that is, 1760 to 2048 seats per train. As these types of trains have more stops between two cities, we still assume that the capacity used between the two cities is equivalent to that of an A380. Finally, it should be noted that prices are not considered in the model, which means that G-trains (more expensive but faster) are always preferred to K-trains (cheaper but slower). In reality, passengers will not always choose the faster connection. Their utility may be likely influenced by factors such as the prices. 2.3. Dynamic weighed model measuring connectivity As reviewed in Burghouwt and Redondi (2013), the shortest path length accessibility model uses the average number of steps needed to reach any other airport in the network to measure the connectivity. It only considers the steps, but ignores all other factors that could affect the accessibility, such as flight duration and ticket availability, etc. Also, it only considers the shortest path, while in fact, the other feasible paths also contribute to the connectivity of that route. In this article, we use travel time instead of steps as the major indicator. The quickest path length accessibility model uses travel time needed to reach other cities to measure the connectivity. Similar to the shortest path length accessibility model, it only considers the best connection option from origin to destination while ignoring the second-best options. Also, it uses the throughput of the airport to measure the airport's connectivity instead of the actual flow traffic on a particular route. Without measuring the route level capacity, this model does not capture the bilateral connectivity between cities. In the model proposed in this article, apart from using time as an important indicator to represent the quality of the connection, all connections as well as route level capacity from origin to destination are considered. The weighted number of connections model (WNX) weighs each connection by its quality in terms of transfer and detour time. The WNX has considered the disadvantage of additional time for transfer for a connection, which is also included in our model. However, the WNX does not account for the different capacity sizes for the connections and the relative time quality of non-stop connections for the same route, which will be considered in our model. The Netscan connectivity unit (NCU) model has included discount for the actual time of a connection compared with the theoretical direct flight time. However, the theoretical direct flight time is very roughly estimated, which may not reflect the true minimum travel time for most of the cases. In this article, we use the best connection of the route as the dynamic benchmark which varies for every route. It enables us to capture a more realistic state of the connection. The measure result evolves dynamically when a better connection is built for one route. More specifically, a dynamic weighted model (DWM) is developed in this study to quantify the quality of a connection. This model is an extension of the NCU model. The NCU model only considers one dimension of quality for flights, which is time, while our DWM includes capacity as an additional dimension measuring connectivity quality. It is also the first time that the NCU model is applied to measure train connectivity. A connection is converted to the dynamic weighed connectivity unit (DWCU) by applying various discount factors which quantify different dimensions of its quality. The construction of the prototype of DWM and calculation of numeric results are as follows.

C = Dxya

Seatxya max Seatxya x , y, a

(1)

The travel time discount is to normalize the effect of different travel length within a single travel mode. A four-hour connection from Shanghai to Beijing provides a better accessibility level than an 8-hour connection on the same route. Passengers usually select connections based on the travel length (in the sense of total transportation costs). However, the travel time discount is calculated based on the adjusted travel length instead of the actual travel length of a connection. The adjusted travel length is made up of travel duration, transfer time, and extra time needed at airport/station. Even though in some cities, the distance of the HSR station from the city center is similar to that of the airport, air passengers normally need to spend more time at airports because they need to arrive early to go through more procedures, such as check-in, baggage drop, security check, etc. They also need to spend extra time at destination airport to pick up baggage. Therefore, 1.5 h is added to all air connections as most airlines require passengers to report to the check-in counter 2 h before departure.10 Furthermore, it is well recognized that the time spent at a transfer stop is more uncomfortable to passengers than the time spent in a moving plane because of the risk of losing baggage or missing connecting flight and the inconvenience of physical movements to get to a different gate (Burghouwt and de Wit, 2005; Burghouwt and Veldhuis, 2006; Burghouwt and Redondi, 2013). Therefore, the value of time for time spent at transit airports is higher for passengers. In this research, we assume an extra penalty of 1 h for each stop that a flight makes to represent the inconvenience of flights with intermediate stops. The shortest adjusted travel length by air from city i to city j is denoted as tA ij . The shortest adjusted travel length by train from city i to city j is denoted as tTij. The calculations of tAij and tTij are shown in Eqs. (2) and A T (3), respectively. The travel time discount (DxyaT , DxybT ) is calculated by comparing the adjusted travel length of a flight (txyaA) or a train (txybT) with the shortest adjusted travel length of that mode (tijT, tijA), as shown in Eqs. (4) and (5). The travel time discount is 1 for the connection on a route with the shortest adjusted travel length. A tijA = min txya x = i, y = j

8

We understand that the assumption of the capacity for trains between two cites is again somewhat arbitrary due to the lack of data. We hope that when the ticket preallocation data becomes available in the future, the capacity of trains will be better estimated. 9 K-train is slower than D-train and HSR train, with the maximum speed being around 120 km/h.

(2)

10 This means that we do not consider each individual city's actual locations of their airport and train station. Future research should account for this for a more accurate connectivity measure.

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A

T Dxya =

T

T Dxyb =

Connectivityx =

(3)

x = i, y = j

tijA A txya

j=x

tijT (5)

The velocity discount (DijV) is to normalize the difference of the connection quality between different routes. The velocity discount is necessary to reveal the actual connectivity level. When the connection time for two cities of 10 km apart is the same as the connection time for two cities of 100 km apart, the latter has a better connection. The velocity discount is calculated by comparing the route's highest average speed with the global highest average speed, as shown in Eq. (6):

DijV =

3. Results and analysis

The proposed DWM considers multiple transport modes. The connectivity of a city shows its status in the city network. A city with a high connectivity represents a high level of importance in the network. Naturally, a city with a high connectivity is more likely to serve as a transit hub. The domestic city global (DCG) connectivity is calculated by aggregating the connectivity of all the routes connecting the city with all other cities in our domestic city network and the international city network. The DCG connectivity shows the comprehensive connectivity level for a city. The ranking of the 23 domestic cities in terms of the DCG connectivity is shown in Table 2 and Fig. 3. Shanghai, Beijing, and Guangzhou, China's first-tier cities in terms of income, are the three most important nodes in our domestic city network. They are evenly distributed in North, East and South China along the east coast line. Shanghai beats Beijing by 3.45% in rail connectivity and 10.79% in air connectivity. Shanghai and Beijing exhibit a huge advantage over Guangzhou. Shanghai's rail connectivity is only 11.74% higher than Guangzhou, while Shanghai performs 96.71% better than Guangzhou in air connectivity. This is fully expected, because Guangzhou Airport faces intensive air competition from airports in Hong Kong and Shenzhen. In the Pearl River Delta region, within a circle of 140 km diameter, three airports have very close connectivity levels (1752.54 for Guangzhou, 1963.49 for Hong Kong, and 1483.31 for Shenzhen), while Beijing and Shanghai have achieved outstanding performance in air connectivity compared with the nearby airports in their respective regions (3447.44 for Shanghai, but only 1073.84 for Hangzhou and 778.86 for Nanjing; 3111.79 for Beijing, but only 652.46 for Tianjin). Hong Kong has a historical dominant advantage in international connectivity, and Shenzhen is adjacent to Hong Kong, which gives Shenzhen a small advantage in being a transit hub for domestic passengers heading for Hong Kong. Shenzhen and Hong Kong, two strong competitors, have undoubtedly constrained the growth of air transport in Guangzhou. Wuhan, Nanjing, Changsha, Hangzhou, Shenzhen, and Zhengzhou are in the second echelon in terms of the overall DCG connectivity. All these cities are connected by high-speed rail with each other and with other major cities. As a result, a noticeable feature for these cities is that rail connectivity accounts for a higher percentage than air in their overall connectivity. All of them are key transport hubs in the HSR network. Wuhan is at the junction of Beijing-Guangzhou HSR and Shanghai-Chengdu HSR, while the Beijing-Guangzhou HSR and Shanghai-Kunming HSR meet at Changsha. Wuhan is only 1.5 h away from Changsha by HSR and 3–4 h away from Guangzhou. Nanjing is the intersection of the Beijing-Shanghai HSR and Shanghai-Chengdu HSR. Hangzhou has extensive HSR links to Shanghai, Nanjing, Ningbo, Kunming, etc. The HSR lines have connected Shenzhen to Guangzhou and Xiamen, and more HSR lines under construction. Zhengzhou is the capital city of Henan Province which is traditionally regarded as the geographical center of China (a position similar to

(6)

The single-mode DWCU is calculated by taking the product of all discount factors and the frequency of connections per time unit (day, week, or year). As shown in Eq. (7), the single-mode DWCU from city i to city j by flight a is calculated first. The single-mode DWCU from city i to city j by air transportation (ConnectivityijAir) is calculated by aggregating the single-mode DWCU of all flights from city i to city j. Similarly, in Eq. (8), the single-mode DWCU from city i to city j by train b (ConnectivityijTrain) is calculated first. The single-mode DWCU from city i to city j by rail is the aggregate of the single-mode DWCU of all trains from city i to city j.

∑

A

C T Frequencyxya × Dxya × Dxya

(7)

x = i, y = j

ConnectivityijTrain =

∑ x = i, y = j

T

T Frequencyxyb × Dxyb

(8)

The final DWCU, which combines the DWCU of multi-modes, is calculated by taking weighed sum of the single-mode DWCUs. The reason for taking a weighed sum is that the DWCU of different modes has been calculated against different standards of travel time. The weight brings different modes to the same frame of reference. The weight is calculated by comparing the shortest travel time within different modes, which ensures a uniform discounting standard in travel time. As shown in Eqs. (9) and (10), when air transportation has shorter adjusted travel length than rail transportation on a route, the weight of the air mode (WijAir) will be 1. The weight of the rail mode (WijTrain) will be accordingly smaller than 1.

WijAir =

WijTrain =

⎛ tijT ⎞ min ⎜⎜ A , 1⎟⎟ ⎝ tij ⎠ ⎛t A ⎞ ij min ⎜⎜ T , 1⎟⎟ ⎝ tij ⎠

(12)

3.1. Domestic city connectivity

distanceij min (tijT , tijA) ⎛ distance ⎞ ij ⎟⎟ max ⎜⎜ i, j ⎝ min (tijT , tijA ) ⎠

ConnectivityijAir =

i=x

In our DWM, we assume that all the discount factors and weights are quasi-concave functions, that is, we assume a diminishing marginal effect. For example, we consider the connectivity of a flight with 400 seats contributes less than 4 times that of a flight with 100 seats does. All the discount factors are calculated by square-rooting the ratio, because the square root keeps the relative magnitude when a quality dimension is referred to and compared with for more than once, e.g., travel time. The use of square root avoids repeated connectivity scaling down when taking the product of mode weighing factor, travel time discount, and velocity discount.

(4)

T txyb

∑ Connectivityij + ∑ Connectivityij

(9)

(10)

The aggregated route DWCU shows the performance of one route, and of the level of connectivity between an origin-destination pair. The multi-mode DWCU from city i to city j can be calculated with Eq. (11):

Connectivityij = DijV × (WijAir × ConnectivityijAir + WijTrain × ConnectivityijTrain ) (11) The aggregation of the DWCU from and to one city shows the connectivity level between the city and the rest of the cities in the network. It also shows the importance of the city (node) in the network. When cities have multiple airports, connectivity is aggregated for all airports. The multi-mode DWCU of city x is calculated with Eq. (12): 6

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Table 2 Domestic city global (DCG) connectivity values of a city. Ranking

City

DCG connectivity

Air

Rail

Air percentage

Rail percentage

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Shanghai Beijing Guangzhou Wuhan Nanjing Changsha Hangzhou Shenzhen Zhengzhou Tianjin Hong Kong Xian Chengdu Shenyang Chongqing Xiamen Kunming Ningbo Guiyang Dalian Nanning Urumqi Yining

7324.283 6859.250 5222.059 4231.772 4173.083 4139.328 4030.799 3934.420 3419.829 2924.357 2396.591 2190.101 2012.041 2004.061 1941.790 1615.640 1573.660 1527.691 1316.484 1232.488 985.908 923.469 48.945

3447.444 3111.790 1752.545 649.492 778.859 599.265 1073.837 1483.306 638.527 652.460 1963.487 1112.199 1310.900 688.329 1123.267 901.225 1321.971 255.286 479.285 731.376 418.296 752.963 45.895

3876.839 3747.460 3469.514 3582.280 3394.224 3540.063 2956.962 2451.114 2781.302 2271.897 433.104 1077.903 701.141 1315.731 818.522 714.415 251.689 1272.405 837.199 501.112 567.612 170.506 3.050

47.07% 45.37% 33.56% 15.35% 18.66% 14.48% 26.64% 37.70% 18.67% 22.31% 81.93% 50.78% 65.15% 34.35% 57.85% 55.78% 84.01% 16.71% 36.41% 59.34% 42.43% 81.54% 93.77%

52.93% 54.63% 66.44% 84.65% 81.34% 85.52% 73.36% 62.30% 81.33% 77.69% 18.07% 49.22% 34.85% 65.65% 42.15% 44.22% 15.99% 83.29% 63.59% 40.66% 57.57% 18.46% 6.23%

tributes little to Hong Kong's overall connectivity. There are only 12 trains from Hong Kong to Guangzhou every day. Trains from Hong Kong to Shanghai and Beijing are operated for leisure trips, with one train every two days. Therefore, air is the major transport mode connecting Hong Kong to other parts of China. For Kunming, the dependence on air is mainly due to its welldeveloped tourism and a lack of HSR operations. Its home province, Yunnan, is geographically large and mountainous. Air transport links to other parts of China and Southeast Asia are fairly developed. Kunming serves as an air transit hub and gateway to many scenic spots in its home province for passengers from other provinces. Due to the longdistance between Yunnan and some wealthy provinces in East and South China, tourists usually use air services when traveling Yunnan. The HSR connecting Shanghai and Guiyang has been extended to Kunming and the HSR will be in operation in early 2017. It will then take only nine hours from Kunming to Shanghai. There should be a significant rise in Kunming's connectivity by then. Similar to Kunming, Urumqi is far away from China's major cities. Urumqi is located in the

Kansas City in the US). Zhengzhou is an inland transport hub with two major trunk railway lines, Longhai Railway from east to west, and Beijing-Guangzhou from north to south meeting here. BeijingGuangzhou and Shanghai-Xi'an HSR lines also meet at Zhengzhou and both have been in operation. Several HSR lines linking ZhengzhouChongqing, Zhengzhou-Hefei, Zhengzhou-Taiyuan, Zhengzhou-Jinan and Zhengzhou-Lanzhou are being constructed or soon to be constructed (Wang et al., 2017). Eventually, Zhengzhou will be a crisscross base of an HSR network. Two cargo-rail lines to Europe cross through Zhengzhou with one going north to Russia and the other going west through Kazakhstan. As China's HSR network continues to expand, it is expected that the rail connectivity of these second-echelon cities will be even higher in the near future. Hong Kong, Kunming, and Urumqi are the three cities that predominantly rely on air transportation. Due to historical reason, Hong Kong's rail system is relatively independent from mainland China. Although Hong Kong is connected to Shenzhen by subway, and to Guangzhou, Beijing, and Shanghai by regular rail services, rail con-

100.00%

7200 6800 6400 6000 5600 5200 4800 4400 4000 3600 3200 2800 2400 2000 1600 1200 800 400 0

90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00%

Air

Rail

Rail Percent

Fig. 3. Domestic city global (DCG) connectivity ranking.

7

Rail Percentage

DCG Connectivity Units

DCG Connectivity Ranking

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connectivity only. Using connectivity as the weight of edges and cities as nodes, we can obtain the network graph with Gephi shown in Fig. 5. It can be seen that Beijing-Tianjin, Guangzhou-Shenzhen, and Shanghai-HangzhouNanjing-Ningbo, have formed tightly connected clusters, respectively. We turn now to the domestic city domestic (DCD) connectivity, which is calculated by aggregating route connectivity of all the routes connecting the city with other domestic cities. The DCD connectivity represents a city's domestic radiation. Similarly, the domestic city international (DCI) connectivity only calculates the connectivity of international routes, and shows the international accessibility of a domestic city. The rankings of the DCD and DCI connectivity are presented in Figs. 6 and 7, respectively. When considering DCD connectivity only, the ranking of Hong Kong drops substantially. This is not surprising as Hong Kong has an extensive international air network. Its international connectivity accounts for 46.42% of its DCG connectivity, while this figure is only 12.14% for Shanghai. When considering DCI connectivity only, Hong Kong ranks the 1st with a huge lead over other cities. Shanghai, Beijing, and Guangzhou are in the 2nd, 3rd, and 4th places, respectively (see the blue bars in Fig. 7). All other cities' international connectivity is at a much lower level compared with the top four. However, Hong Kong is normally regarded as an international city rather than a domestic city by most Chinese. When we exclude Hong Kong from our domestic network, the DCI connectivity ranking changes significantly (see the red bars in Fig. 7). Shenzhen rises to number 3 from number 5. Xiamen rises from 11 to 9. Hangzhou rises from 12 to 6. Nanjing rises from 13 to 10. Chongqing rises from 15 to 12. Wuhan rises from 17 to 14. Ningbo rises from 21 to 18. These changes indicate that these cities have good connections with Hong Kong. As Hong Kong is well connected to many international airports, these cities could use Hong Kong as a transit hub to increase their indirect connectivity to the outside world (Wan and Zhang, 2017).

Table 3 Domestic city global (DCG) connectivity rankings with different modes. City

Air DCG ranking

Rail DCG ranking

Combined DCG ranking

Shanghai Beijing Hong Kong Guangzhou Shenzhen Kunming Chengdu Chongqing Xi'an Hangzhou Xiamen Nanjing Urumqi Dalian Shenyang Tianjin Wuhan Zhengzhou Changsha Guiyang Nanning Ningbo Yining

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

1 2 20 5 9 21 17 15 13 7 16 6 22 19 11 10 3 8 4 14 18 12 23

1 2 11 3 8 17 13 15 12 7 16 5 22 20 14 10 4 9 6 19 21 18 23

The underlined numbers show that a city's air DCG ranking and its rail DCG ranking can be substantially different.

far northwest of China. A train trip between Urumqi and Beijing takes about 30–39 h. Air is a natural choice for many travelers. For the reasons mentioned above, when only air connectivity is considered, Hong Kong ranks the 3rd out of 23 (see Table 3 and Fig. 4). However, it ranks the 20th place in terms of rail DCG connectivity, and the 11th for the combined modes. Kunming ranks the 6th place in air, 21st in rail, and 17th for the combined DCG connectivity. As noted earlier, Kunming did not have HSR until early 2017. In contrast, Wuhan and Changsha are located in Central China with an extensive HSR network. The well-developed HSR system has given them the 4th and 6th places for the combined DCG connectivity, respectively. However, they come in the 17th and 19th, respectively, when measured by air

3.2. International city connectivity An international city domestic (ICD) connectivity is calculated by aggregating the connectivity of all the routes connecting this international city to China's domestic cities. The ranking of the 13 international cities in ICD connectivity is presented in Fig. 8. When considering Hong Kong as a domestic city, Seoul is the

Figure 4 DCG connectivity ranking changes considering different modes 25

Ranking

20

15

10

5

0

Rank_Air

Rank_Rail

Rank_Agg

Fig. 4. Domestic city global (DCG) connectivity ranking with transport modes.

8

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Fig. 5. Graph network showing the clusters of Beijing & Tianjin, Guangzhou & Shenzhen, Shanghai & Hangzhou & Nanjing & Ningbo.

100.00% 90.00%

70.00% 60.00% 50.00% 40.00% 30.00%

Rail Percentage

80.00%

20.00%

Air

YINING

URUMQI

DALIAN

NANNING

NINGBO

GUIYANG

XIAMEN

KUNMING

SHENYANG

CHONGQING

XIAN Rail

CHENGDU

TIANJIN

HONG KONG

SHENZHEN

ZHENGZHOU

CHANGSHA

HANGZHOU

WUHAN

NANJING

BEIJING

GUANGZHOU

10.00% SHANGHAI

DCD Connectivity unit

DCD Connectivity Ranking 6500 6000 5500 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0

0.00%

Rail Percent

Fig. 6. Domestic city domestic (DCD) connectivity ranking.

Domesc City Internaonal Connecvity Ranking 1400 1200 1000 800 600 400 200 0

HK Domesc

HK Internaonal

Fig. 7. Domestic city international (DCI) connectivity ranking. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)

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International City Domestic Connectivity Ranking 1300.00 1200.00 1100.00 1000.00 900.00 800.00 700.00 600.00 500.00 400.00 300.00 200.00 100.00 0.00

ICD Connectivity(HK Domestic)

ICD Connectivity(HK Int'l)

Fig. 8. International city domestic (ICD) connectivity ranking. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)

International City Domestic Connectivity Structure Analysis 100.00%

650

90.00% 520

80.00% 70.00%

390

60.00% 50.00%

260

40.00% 30.00%

130

20.00% 10.00%

0

0.00%

Beijing

Shanghai

Guangzhou

Hong Kong

All

BSGH%

Fig. 9. International city domestic (ICD) connectivity structure analysis.

is not counted as a Chinese domestic city. In contrast, Paris, Frankfurt, Seoul, Tokyo and Osaka have better connections with mainland China in the international network as the changes in the size of the blue and red bars are not dramatic. Our results suggest that the connections between the international cities and China's domestic network are highly concentrated on a few cities, namely, Hong Kong, Shanghai, Beijing, and Guangzhou. As shown in Fig. 9, the cumulative ICD connectivity with Beijing, Shanghai, Guangzhou and Hong Kong (BSGH) accounts for at least 59% of the overall ICD connectivity for every international city. It even accounts for more than 95% for New York, London, and Atlanta.

international city that is best connected with China's domestic network, followed by Tokyo, Taipei and Singapore (see the blue bars in Fig. 8).11 Each week, there are 1447 flights connecting Taipei to China's domestic network, and 1255 flights between Tokyo and China. However, after applying the discount factors, Taipei's ICD connectivity is 12.46% lower than that of Tokyo. This is because flights connecting Taipei to mainland China have to detour around the Taiwan Strait middle line, which results in a significant increase in flight distance, especially for short-haul flights. As the direct distance is used to calculate the velocity discount, the detour implies a smaller value of this discount factor. In fact, the average velocity discount for routes connecting Taipei to China's domestic network is 0.601, while the average velocity discount for all international routes is 0.749. When Hong Kong is regarded as an international city, it is the absolute winner measured by ICD connectivity, beating all other international cities (see the red bars in Fig. 8). The connectivity values for New York and London drop by 50.31% and 51.10%, respectively, which means that about half of New York and London's connections with China are via Hong Kong. Taipei, Atlanta and Dubai's connection with China also heavily rely on Hong Kong. The destination of 41% (595 weekly) of the flights from Taipei to China is Hong Kong, resulting in the ranking of Taipei being overtaken by Singapore when Hong Kong

Table 4 Ranking of city pairs by flight frequency.

11 When a city has multiple airports, the connectivity measures the aggregate of all airports.

10

City A

City B

Weekly flights

Shanghai Shanghai Shanghai Shanghai Shanghai Beijing Beijing Beijing Shanghai Shanghai

Beijing Shenzhen Hong Kong Guangzhou Kunming Hong Kong Chengdu Shenzhen Chengdu Xi'an

793 751 695 576 527 523 485 465 450 437

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Table 5. Shenzhen-Hong Kong holds the advantage over other city-pairs in rail connections. The subway system connects Shenzhen and Hong Kong at Lok Ma Chau Station and Lo Wu Station, with trains arriving/ departing every 8–12 min. However, other city-pairs are connected by HSR or intercity trains rather than subways. The route connectivity measured by the aggregated DWCU gives a ranking list for 40 domestic routes in China (see Table 6) that differs greatly from those in Tables 4 and 5. Shenzhen-Hong Kong ranks the 17th with the measure of DWCU, mainly due to the low speed of subway between the two cities and the extra time spent on immigration and customs clearance. Kunming-Shanghai ranks the 42nd with DWCU instead of the 5th in terms of the air frequency. The drop in ranking is largely due to the absence of HSR and the high percentage of indirect flights. In fact, 31.88% of the flights connecting Kunming and Shanghai are indirect flights, while the indirect flights only account for 10.27% in the whole domestic network. Beijing-Tianjin has the same train frequency as Shanghai-Hangzhou, while Beijing-Tianjin's connectivity is 11.12% higher than the latter. This is because only 65 out of the 332 trains between Beijing and Tianjin are K-trains (low speed trains), while 97 of the 332 trains between Shanghai and Hangzhou are K-trains. Among all the city pairs, Shanghai-Nanjing has the highest connectivity, mainly due to the significant contribution from the HSR services between the two cities. Beijing-Shanghai is the best-connected pair by air. It appears that the connectivity between city-pairs follows the Pareto principle quite well. It can be seen from Fig. 10 that the top 10% routes contribute more than 50% of the total network connectivity. The first 30% routes contribute more than 80% of the total network connectivity. Looking closely at the top-ranking routes, we can find some similarities. As shown in Fig. 11, the top routes benefit greatly from train connections. Train services contribute more than 70% of the route DWCU for 26 out of the top-40 routes. This trend is more obvious among the top-20 routes, with train contributing more than 80% of the route DWCU for 19 of them. The only exception is Shanghai-Beijing, with train contributing 48.68%. However, considering Shanghai-Beijing is the longest route among the top-20 (the direct distance is 1178 km), the role that train plays is still significant. Although there are K- or D-trains operating on these top-20 routes, the vast majority of the train services are provided by the HSR, especially during the daytime. Although the HSR has been well recognized as the dominant transport mode only for short- and medium-haul routes with a distance below 700 km (e.g., Yamaguchi et al., 2008; Adler et al., 2010; Yang and Zhang, 2012; Fu et al., 2014; Wan et al., 2016; Xia and Zhang, 2016a), with people building in confidence in its safety and punctuality, the HSR has become a better and dominant option against air for some medium- and long-haul routes up to 1300 km. For example, rail transport contributes 83.08% of the connectivity for GuangzhouZhengzhou, whose direct distance is 1294 km. It contributes 80.97% of the connectivity for Beijing-Wuhan, with direct distance being 1133 km, and 89.27% of the connectivity for Beijing-Nanjing whose direct distance is 981 km. Therefore, when studying the connectivity of a route, it is necessary to add rail to the passenger transportation network, especially the HSR, which allows a better understanding of how cities are connected. As one anonymous referee correctly pointed out, the relative level of ticket price, passengers' time value and income are the factors that may have made HSR more attractive than air in the case of China. The published fare for G-train is around 0.46 yuan/km and the fare for Dtrain is 0.3 yuan/km (Sina, 2017). For airlines, the published airfare is 0.75 yuan/km. By offering various discounts, domestic airlines' actual yield has been around 0.55 in recent years. For example, China Southern's yield was 0.58 yuan/km in 2014, and 0.55 in 2015 in the domestic market. Therefore, the HSR services are still relatively cheaper. Furthermore, Wang et al. (2017) report the HSR ticket prices

Table 5 Ranking of city pairs measured by train frequency. City A

City B

Weekly trains

Shenzhen Shanghai Guangzhou Shanghai Beijing Guangzhou Wuhan Nanjing Zhengzhou Hangzhou

Hong Kong Nanjing Shenzhen Hangzhou Tianjin Changsha Changsha Hangzhou Wuhan Ningbo

3486 3374 2611 2324 2324 2002 1946 1414 1400 1337

3.3. Route connectivity The information of the connectivity of the routes can help disclose the main travel channels in the domestic city network, just like the arteries in the human body. The top 10 city-pairs with the most connecting flights are listed in Table 4. When considering the raw connections, Shanghai-Beijing takes the lead. The top ten city-pairs with the most connecting trains are listed in

Table 6 Route connectivity ranking. Rank

City pair

DWCU

Air

Rail

Air%

Rail%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Shanghai-Nanjing Beijing-Tianjin Guangzhou-Shenzhen Shanghai-Hangzhou Guangzhou-Changsha Wuhan-Changsha Beijing-Shanghai Zhengzhou-Wuhan Guangzhou-Wuhan Beijing-Zhengzhou Hangzhou-Ningbo Nanjing-Hangzhou Beijing-Nanjing Shenyang-Dalian Beijing-Wuhan Zhengzhou-Changsha Shenzhen-Hong Kong Zhengzhou-Xi'an Shenyang-Tianjin Shenzhen-Changsha Shanghai-Shenzhen Beijing-Changsha Beijing-Shenyang Beijing-Guangzhou Tianjin-Nanjing Shanghai-Tianjin Shanghai-Guangzhou Chengdu-Chongqing Beijing-Xi'an GuangzhouZhengzhou Shanghai-Hong Kong Beijing-Shenzhen Changsha-Hangzhou Shanghai-Wuhan Beijing-Hangzhou Beijing-Chengdu Beijing-Hong Kong Shanghai-Xiamen Guangzhou-Nanning Shanghai-Changsha

1343.04 1029.79 996.42 926.70 888.92 808.04 625.89 599.17 591.27 559.98 517.39 513.51 494.31 412.75 402.85 394.88 383.86 360.10 353.17 331.13 317.04 311.76 305.59 293.35 284.90 281.34 280.61 274.66 273.28 271.51

2.96 0.00 0.00 0.00 13.30 0.00 321.23 0.00 50.50 16.36 0.00 0.00 53.05 0.00 76.64 0.00 0.00 0.00 3.61 8.15 264.61 95.14 61.02 199.36 1.85 79.17 226.66 0.00 145.14 45.95

1340.09 1029.79 996.42 926.70 875.61 808.04 304.66 599.17 540.76 543.62 517.39 513.51 441.27 412.75 326.21 394.88 383.86 360.10 349.56 322.98 52.44 216.62 244.57 93.99 283.05 202.18 53.95 274.66 128.13 225.56

0.22% 0.00% 0.00% 0.00% 1.50% 0.00% 51.32% 0.00% 8.54% 2.92% 0.00% 0.00% 10.73% 0.00% 19.03% 0.00% 0.00% 0.00% 1.02% 2.46% 83.46% 30.52% 19.97% 67.96% 0.65% 28.14% 80.77% 0.00% 53.11% 16.92%

99.78% 100.00% 100.00% 100.00% 98.50% 100.00% 48.68% 100.00% 91.46% 97.08% 100.00% 100.00% 89.27% 100.00% 80.97% 100.00% 100.00% 100.00% 98.98% 97.54% 16.54% 69.48% 80.03% 32.04% 99.35% 71.86% 19.23% 100.00% 46.89% 83.08%

262.69 261.09 251.19 246.75 239.44 237.46 235.67 235.18 234.74 233.02

260.76 211.91 17.51 78.57 130.67 213.13 233.50 126.40 14.56 73.42

1.92 49.18 233.68 168.18 108.77 24.33 2.17 108.78 220.18 159.60

99.27% 81.16% 6.97% 31.84% 54.57% 89.75% 99.08% 53.75% 6.20% 31.51%

0.73% 18.84% 93.03% 68.16% 45.43% 10.25% 0.92% 46.25% 93.80% 68.49%

31 32 33 34 35 36 37 38 39 40

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Fig. 10. Route connectivity distribution.

Fig. 11. Route connectivity ranking and train proportion.

highest connectivity level, leading in both rail and air connectivity. Hong Kong, Kunming, and Urumqi are the three cities that predominantly rely on air transportation whose contribution to the connectivity exceeds 80%. Beijing-Tianjin, Guangzhou-Shenzhen, and ShanghaiHangzhou-Nanjing-Ningbo, have formed tightly connected clusters, respectively, as revealed by the graph network. This research also suggests that the connections between the international cities and China's domestic network are highly concentrated on a few cities, namely, Hong Kong, Shanghai, Beijing, and Guangzhou. To build better connections with the rest of the world and implement the OBOR strategy, other cities will need to improve their international air connectivity. Shanghai-Nanjing has been found to be the best-connected city-pair, mainly due to the significant contribution from the HSR services. Beijing-Shanghai is the best-connected pair by air. Seoul is the best connected international city in term of its transportation links with China. Our study also shows that train connections have made considerable contributions to the route connectivity. The contribution from train services is more than 80% for 19 of the 20 top-ranking domestic routes. Although most studies suggest that the HSR is the dominant transport mode only for short- and medium-haul routes with distance below 700 km, this research has found that the HSR in China has become a better and dominant option against air for routes up to

among China, Japan and Europe for routes of comparable distance. The HSR ticket prices in China are much cheaper that its counterparts in Japan and Europe. In addition, flight delays have become a norm at the mega-airports. Taken together, it may not be very surprising that HSR has been a preferred choice, even on some longer routes: HSR in China is still competitive against air transportation on some long-haul routes with distance even up to 1300 km, as compared to other countries where HSR is the dominant transport mode only on short-haul and medium-haul routes with distance below 700 km. 4. Concluding remarks This paper has developed a DWM to measure and compare the network performance of 23 major cities in China and 13 major international cities that are of economic/location significance. The DWM takes into account the connections by both flights and trains. Apart from the quantities, three aspects of the connection quality, namely, capacity, travel time, and velocity, are incorporated into the model to account for the utility function of travelers. To our best knowledge, this is the first study that uses such a dynamic model to consider both quality and quantity of the connections of two transport modes. Among the 23 cities selected, Shanghai is revealed to have the 12

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1300 km. This finding has significant policy implications for transportation infrastructure planning and investment in China. The multi-modal connectivity information is valuable to airport and HSR planning. In 2017, the Director of the Civil Aviation Administration of China (CAAC) announced that some flights from the second- and third-tier cities to Beijing will be shifted to Tianjin and Shijiazhuang in the future and these airports will be linked to Beijing via HSR. Also in 2017, National Development and Reform Commission and the CAAC jointly issued a notice on national civil aviation airport network planning in which constructing new airports in West China and integrating airports with other transport modes such as HSR were top priorities. These moves have shown that the Chinese government understands the importance of coordination of the development of airports and HSR. Although these two modes are substitutable in general, they could possibly complement each other (e.g., Givoni and Banister, 2006; Jiang and Zhang, 2014; Román and Martín, 2014; Xia and Zhang, 2016b; Wang et al., 2017). Understanding the overall connectivity of a city/region is key to implement these proposals and move forward the OBOR agenda. However, this research only considers 23 important domestic cities. In future research, the DWM developed in this paper can be used to examine a larger network by including more domestic cities, and of course more international cities, which will reduce the bias of the connectivity measure resulting from the small sample size. Indirect connections arranged by passengers may also need to be incorporated into the model for a more complete analysis of the connectivity. The DWM can also be used to assess the possible vulnerability and resilience of intercity passenger transportation network and thereby develop an emergency response strategy for possible transportation accidents, or national transportation safety and security planning.12 Moreover, our DWM can be extended and applied in freight transportation research, which will produce useful results for the transportation and logistics industry. Finally, transport geographers and other researchers are interested in how contextual factors such as the particularities of the urban, economic and institutional system shape transportation system outcomes in general, and connectivity in particular (e.g., Gutierrez, 2001; Geurs and Van Wee, 2004; Lin, 2012; Matsumoto et al., 2016; Wu et al., 2016; Zhang et al., 2017). For example, Zhang et al. (2017) attempt to identify the underlying drivers (e.g. urban, economic and institutional system) of the variation in Chinese airport connectivity over the 2005–2016 period. We consider that incorporating our multimodal approach with the underlying drivers would be a natural and interesting extension, but perhaps beyond the scope of the present paper.

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Acknowledgement We are very grateful to three anonymous referees and the Associate Editor (Peter Hall) whose detailed and thoughtful comments have led to a large improvement of the paper. We also thank Guillaume Burghouwt, Yaap de Wit, Jun-yeop Lee, Kevin Li, Becky Loo, and participants at the seminar at the University of Hong Kong, Kyoto University, Hiroshima University, the World Aviation Hub Conference (Incheon), the 5th European Aviation Conference (Amsterdam), and the “One Belt, One Road” Conference at RMIT (Melbourne) for helpful comments. Partial financial support from the UBC Centre for Transport Studies (to Zhenran Zhu) and the Social Sciences and Humanities Research Council of Canada (435-2015-0845) (to Anming Zhang) is gratefully acknowledged. A. Zhang would like to thank Shanghai Advanced Institute of Finance, Shanghai Jiao Tong University, for hospitality as a visiting professor when the paper was revised.

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We thank an anonymous referee for providing this insightful point.

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