Inter-city connections in China: High-speed train vs. inter-city coach

Journal of Transport Geography 82 (2020) 102619

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Journal of Transport Geography journal homepage: www.elsevier.com/locate/jtrangeo

Inter-city connections in China: High-speed train vs. inter-city coach a,b

Jiaoe Wang

a,b,⁎

, Delin Du

, Jie Huang

a,b,⁎

T

a

Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China b University of Chinese Academy of Sciences, Beijing 100049, China

A R T I C LE I N FO

A B S T R A C T

Keywords: High-speed rail Connectivity Inter-city coach Spatial structure Regional transportation planning

High-speed train (HST) and inter-city coach (ICC) have been two important ground transportation modes for travelling between cities in China. They influence inter-city connections significantly. This study uses HST's and ICC's timetable data to construct networks; evaluates city centrality and city-pair connectivity to compare the hierarchical structures. The results show that the HST network shows linear distribution characteristics while ICC network presents regional “core-periphery” structure. Provincial administrative boundaries have an obvious constraint on the ICC network, while the HST community structure follows the railway lines' distribution. Finally, this study illustrates the spatial organization model and gives implications for regional transportation planning.

1. Introduction The geography of inter-city connections or city-pairs can reflect the spatial pattern of urban systems and influence the externalities of cities (Rodrigue et al., 2013). As one of the most important basic conditions for regional development, transport networks are representative physical links that can help to understand the interaction of cities at the global, national, and regional scales (Jiao et al., 2017). Compared with road, air, and conventional railway, high-speed rail is a brand-new transportation mode, which would influence the inter-city flow of passengers. In China, the high-speed rail went through a period of rapid development in recent years and has become the largest network in the world. High-speed rail can generate an unprecedented shrinkage of space and time by greatly shortening inter-city travel time (Spiekermann and Wegener, 1994), which in turn impacts the inter-city travel and changes the market shares of other transportation modes (Vickerman, 1997). For example, 23% of the passenger traffic of Japan's Sanyo Shinkansen line came from air travel, and 16% came from cars and buses (Givoni, 2006). In China, three high-speed rails originating from a provincial capital (Nanning, located in Guangxi in the south of China next to Guangdong) to three cities in the same province (Beihai, Fangchenggang, and Guilin) have been opened since 2013. Subsequently, during the 2014 Spring Festival, Nanning's road passenger traffic volume decreased by 22.1% compared with 2013 (Nanning Municipal Bureau of

Statistics, 2014). Therefore, it is a significant research issue to compare the high-speed rail and other transportation modes, analyze their similarity and diversity, and explore the influence of the high-speed rail. Many studies showed how the high-speed rail affects the spatial interaction of cities (Cheng, 2010; Jiao et al., 2014; Shaw et al., 2014), and in the existing literature, the high-speed rail network is generally compared with the airline network (Wang et al., 2015; Yang et al., 2018c; Zhang et al., 2019). Givoni and Dobruszkes (2013) found that the competition of high-speed rail with air and road transport diverge according to some differences in the factors. Sun et al. (2017) suggested that multimodal transport system, including coaches, is one of the future research agenda on high-speed rail and air transport competition and cooperation. Chang and Lee (2008) also considered road transportation for the analysis of Korean high-speed rail accessibility. Several studies have measured and compared rail and road network and considered the influence of the high-speed rail operation (Kotavaara et al., 2011; Song and Yang, 2016). High-speed rails are usually operated with higher frequencies between city-pairs than traditional trains, and could provide an alternative and competitive transport mode except coaches for passengers. However, few studies conducted a direct comparative investigation for high-speed rail networks and road networks because of the following reasons. First, it is difficult to find a study area where the two developed infrastructure networks are of similar size. Second, a comparison from the perspective of supply has rarely been conducted due to lack of data. Our research attempted to

⁎ Corresponding authors at: Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China. E-mail addresses: [email protected] (J. Wang), [email protected] (D. Du), [email protected] (J. Huang).

https://doi.org/10.1016/j.jtrangeo.2019.102619 Received 28 May 2019; Received in revised form 3 December 2019; Accepted 3 December 2019 0966-6923/ © 2019 Elsevier Ltd. All rights reserved.

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Fig. 1. Analytical framework of city networks.

et al., 2018b) and regional scales (Hou and Li, 2011). Through a study of high-speed rail in Europe, Gutierrez (2001) found that the new highspeed line may reduce accessibility inequality among the cities at the European scale but increase inequality at the national scale. However, Ortega et al. (2012) thought that although high-speed rails make spatial distribution of accessibility more balanced on a macro scale, on a small scale, they may have an equilibrium effect or exhibit polarization. In general, HSTs can result the accessibility tended to intensify in large cities (Jiao et al., 2017) and benefit the core cities more than the peripheral cities (Levinson, 2012). But with the gradual improvement of the high-speed rail network in future, HSTs may promote a more balanced development in China (Jiao et al., 2014). Meanwhile, highway networks have shaped city networks significantly, and some studies have investigated their interactive correlations. Dupuy and Stransky (1996) calculated the hierarchy of cities in the European highway network and analyzed the influence of physical and human geography. At the regional scale, Ke et al. (2017) described macro-spatial patterns and hierarchical structures of city networks in Jiangsu province based on highway traffic flow data and found there are significant administrative constraints in the highway network. Similarity, Chen et al. (2018) found that China's city networks based on highway flows demonstrate strong spatial dependence, and highway networks show the “core-periphery” structure at the regional scale and the provincial administrative governance effect. High-speed rails inevitably have an impact on the original transportation system and inter-city connections. Within ground transportation, some studies have analyzed the comparison and coordination of conventional rail and bus system to optimize the transportation system (Chien and Schonfeld, 1998; Tirachini et al., 2010). Yue et al. (2019) analyzed the city clusters over the transportation network which includes both trains and long-distance buses. Compared with conventional rails, HSTs are operated with high frequencies between city-pairs and that could be compared with inter-city coaches. At the same time,

enrich these aspects, and the characteristics of each network will be valuable in future high-speed rail development and regional transport planning. Therefore, the main objective of this study is to analyze what extent does the configuration of Chinese inter-city connections served by high-speed trains (HSTs) differ from those served by inter-city coaches (ICCs) and reveal the spatial characteristics. We investigated intercity connections based on these two transportation modes in China where the high-speed rail and highway/road networks have been built on a national scale, and compare their performance from the perspective of operations. As many studies have employed the time schedule data to investigate the inter-city connections (Yang et al., 2018b), the current research will use the schedules of HSTs and ICCs to build the networks. This paper is organized as follows. Section 2 summarizes the existing literature, followed by research methods (Section 3). Section 4 compares the similarities and diversities between the two networks. Section 5 explores the spatial interaction and community structures, abstracts the organization models and analyzes the administrative governance effect. Finally, Section 6 concludes.

2. Literature review Within the context of transportation networks, flight, train, and coach are commonly used to study inter-city connections (Chen et al., 2018; Jiao et al., 2017; Wang et al., 2011), and high-speed rail is a relatively new transportation with safe, comfortable, and efficient features, which received lots of attention and influenced the original transportation modes. As mentioned (Jiao et al., 2017; Shaw et al., 2014), high-speed rails generate short-term impacts on accessibility and connectivity, especially for the cities alongside, and show the corridor effect. Thus the construction of high-speed rail has reshaped city network, improved the connection efficiency between cities, and changed spatial organization at international (Gutierrez, 2001), national (Yang 2

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among cities and is employed to evaluate the structure of city networks. Thus, connectivity is commonly evaluated using travel time and transport frequency between cities (Jiao et al., 2017; Yang et al., 2018b). A basic assumption is that the higher is the frequency between cities, the stronger is their connectivity. A network evaluated based on HSTs and ICCs consists of nodes, edges, and the weight of edges. The node is defined as cities that have HST (coach) stations in their municipal districts; the edge is the citypairs with HST (coach) running directly between nodes where passengers could get on/off without a change, and the weight of each edge was the daily frequency of trains (coaches) between city pairs. Two or more HST (coach) stations in any one city are considered as one node (Jiao et al., 2017; Yang et al., 2018b). Following the decomposition method, inter-city connection networks are shown in Fig. 3. In brief, the HST network covers 229 cities and has 6856 city-pairs, while the ICC network consists of 361 cities and has 9208 city-pairs. In Fig. 3, inter-city connections in the ICC network are denser. Consequently, the average geographical distance for train connections is 426.0 km in the HST network, while it is much shorter (291.2 km) in the ICC network. At the city level, train frequency per day ranges from 1 to 1000, which is a smaller range compared to ICC from 1 to 7000. In such a case, average train frequency per city is 164 in the whole network, and the value of ICC is about 544. As the HST network has served less city-pairs, the average frequency per city-pair by HST (23.2) is higher than ICC's (10.7). In addition, another spatial trend is that cities in the eastern region have connections with more trains and coaches per day than other regions.

both of HSTs and ICCs are focused on short and middle distance, but a lot of traditional trains cover long distance because they could be operated overnight. In general, HSTs have an advantage with respect to road transport in middle distances (DeRus and Inglada, 1997), and buses and cars are their main competitors for short journeys (Levinson et al., 1997; Vickerman, 1997; Zheng and Kahn, 2013). Based on the autoregressive distributed lag modeling approach, Chou and Shen (2018) found high-speed rail has a negative effect on the intercity bus transport market in the long term, but a positive effect in the short term. Compared with HSTs, the highway market share generally show obvious advantage within 200 km (Cheng, 2010; Takatsu, 2011). These are mainly based on the investigation of model analysis or surveys, and we will try to test the empirical conclusions based on the actual frequency of HSTs and ICCs. And so far, few studies have compared the networks' structures and investigated the interaction between HSTs and ICCs, which may also reshape inter-city connections. Therefore, this paper will attempt to fill in the research gap. In a broader view of literature, inter-city connections have been widely investigated in fields of city network and urban system. As shown in Fig. 1, many scholars have investigated inter-city connections by using the corporation, social networks, tele-information contacts, advanced producer service linkages, as well as transport networks to examine city networks' structures, urban functions, and relations at various spatial scales (Alderson and Beckfield, 2004; Choi et al., 2006; Derudder and Witlox, 2005; Hu et al., 2019; Mahutga et al., 2010; Malecki, 2002; Martinus and Sigler, 2017). These studies offered various empirical observations which facilitate theoretical advances in the study of the city-network. To some extent, the comparison of HST and ICC networks can be a sub-section in the study of inter-city connection, only from the perspective of transport geography. With this work, we may illustrate how technological improvement affect conventional inter-city travel modes and hence reconfiguration of inter-city connections.

3.3. Evaluation of city centrality and city-pair connectivity To describe the hierarchical structure and explore the similarities and diversities of HST and ICC networks, we used the measures of city centrality and city-pair connectivity, as proposed by Limtanakool et al. (2007). City centrality can be measured by the indicator PI (Point Indice) denoting the importance of cities in the network:

3. Research methods 3.1. Data processing

PIi = This study uses timetable data to reflect the connections between cities and then builds the transport networks based on these connections. Timetable data of ICCs are obtained from the Xinxin Travel Network website (http://www.cncn.com), which includes timetable data for all Chinese cities, and HSTs' data come from the China Railway Customer Service Center (http://www.12306.cn/mormhweb/). We collect data on rail lines with trains prefixed with G, C, and D because these trains are usually identified as the high-speed trains in China (Jiao et al., 2014; Yang, et al., 2018b). Using these datasets, the average number of coaches or trains between cities per day is calculated, and we acquire the daily frequency for each inter-city connection by coach or train. The data were collected in May 2018. The study area comprises 361 cities in mainland China, including 4 municipalities, 333 prefecture-level cities, and 24 county-level cities administrated by province. If there are multiple train (coach) stations in a city, all trains (coaches) originating or ending at those stations are merged. Hence, all these trains (coaches) are regarded as trains originating or ending at the city node.

Ti

(

n ∑ j=1

Tj / n

)

(1)

where Ti and Tj are the total number of trains (or coaches) associated with city i and city j. n is the number of cities covered by the transportation. Cities with PIi > 1 are considered dominant, because they are more important than the average of the other cities in the network. City-pair connectivity can be measured by the indicator RSL (Relative Strength of Links) reflecting the strength of inter-city connections:

RSLij =

tij n

n

∑i = 1 ∑ j = 1, j ≠ i tij

(2)

where tij is the total number of trains (or coaches) travelling between city i and city j, and i ≠ j. Since some RSLij values will be rather small, to clearly understand their strength values, the RSLij value is multiplied by 1000 (Derudder and Witlox, 2009). The higher the value of RSLij, the higher the status of the city-pair in the network. 3.4. Community detection

3.2. Network generation The community detection measure the global and local agglomeration characteristics of networks (Ke et al., 2017). This method has been used widely to explore city network structures. Most cities can be classified into groups of densely connected cities, with cities belonging to different groups being connected sparsely. Based on the HSTs and ICCs, the community structure can reflect the influence of the two modes on the spatial economic connection between cities and reveal the structural features of the two networks. Community detection is based

China has a nationwide HST network as well as a highway network (Fig. 2). At the end of 2018, the operating mileage of high-speed rails exceeded 29,000 km, and the highway mileage was > 142,500 km. So far, the highway network has covered all Chinese provinces, and there are 854 HST stations in mainland China, distributed in the majority of provinces except Ningxia and Tibet. Connectivity reflects the external relations and spatial interaction 3

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Fig. 2. High-speed rails and highways in mainland China. Note: Data on high-speed railways in mainland China, that is the lines operated by the high-speed trains prefixed with G, C, and D, were obtained in May 2018 and on highways in January 2017. The “four vertical and four horizontal” high-speed rail corridors are adapted from the National Development and Reform Commission (2008).

frequencies in both networks is < 10%, reflecting that the two transportation modes are mainly concentrated in the short and medium distances. This is consistent with the estimation based on the modeling study (Wang, 2009). Evidence based on the actual passenger flow data (Yang et al., 2018c) also shows that the flows of HST mainly concentrate within 1100 km. As shown in Fig. 4, the similar distribution tendency of frequencies indicates the fierce competitive relationships between these two transport modes. Many existing researches have showed the impact of the market of highway after the operation of HST (DeRus and Inglada, 1997; Cheng, 2010; Zheng and Kahn, 2013), especially in short and middle distance.

on the modularity proposed by Newman and Girvan (2004), written as follows:

Q=

1 2m

∑ ⎛Aij ⎜

ij

⎝

−

k i kj ⎞ ⎟

2m ⎠

δ (Ci , Cj ) (3)

where Aij represents the number of ICCs or HSTs between city i and city j, ki represents the total number of ICCs or HSTs in the networks attached to city i, Ci is the community to which city i is assigned, and m is the maximum number of connections that may exist in the network, the δ function δ(u, v) is 1 if u = v and 0 otherwise (Blondel et al., 2008). The value of Q range from 0 to 1, and the higher the value, the stronger the community structure. After Newman and Girvan (2004), many scholars have proposed some improved methods, among which Fast Unfolding Algorithm (FUA) by Blondel et al. (2008) is one of the methods with excellent performance (Lancichinetti and Fortunato, 2009). It is a multistep technique based on the local optimization. Thus this study uses FUA to explore the community structures and local spatial characteristics in the two networks. To evaluate the importance of different communities in the networks, we use PageRank to assess the connection status of all nodes. The sum of the PageRank values of the nodes in each community can reflect the community's external connectivity. Communities with higher external connectivity have higher status.

4.2. Similarity 2: spatial agglomeration and preferential attachment All cities and city-pairs in the HST and ICC networks are divided into five levels respectively according to the city centrality and city-pair connectivity (Figs. 5 and 6). Cities in the first four levels have more frequencies than the average and can be considered as dominant transport hubs in the network (Yang et al., 2018c). Those cities in the first four levels of the two networks not only occupy about one-third of all cities but also account for about 70% of the frequencies respectively. With only < 10% city-pairs in the first four levels, they account for nearly 50% of the frequencies in both two networks. These reflect that the frequencies of HST and ICC are mainly dominated in some hub cities and distributed along their trunk city-pairs. Furthermore, a comparison of cities and city-pairs in first and second levels, demonstrates that administrative governance and economic development play important roles. In those cities, six out of seven HST cities (6/7) and eight out of thirteen ICC cities (8/13) are provincial capitals or municipalities as shown in Fig. 5. For city-pairs in the first two levels (Fig. 6), they are mainly distributed in the Yangtze River Delta region in the HST network, but in the Pearl River Delta region in the ICC network. Both the two regions have dense population

4. Network characteristics and geographic features 4.1. Similarity 1: restriction by distance decay As ground transportation modes, HSTs and ICCs are constrained by geographical proximity and show the restriction by distance decay in space. The proportion of frequencies by HST or ICC is shown by the distance intervals in Fig. 4. In both the HST and ICC networks, the frequency proportion increases within 200 km and decreases gradually thereafter. When the distance exceeds 1000 km, the accumulation of 4

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Fig. 3. The high-speed train and inter-city coach networks.

exponential decay than the HST network, which is similar with the estimation based on the actual passenger flow (Wang, 2009). In Fig. 4, when the distance is < 200 km, the ICC proportion is higher than that in the HST network, and the ICC accumulative rate is > 55%. The proportion of HST network gradually exceeds that of the ICC network after the distance of 200 km and the difference is larger and larger with the distance increase. This is consistent with the existing research that cars and buses generally have advantages at short distances while high-

and well developed economy. These indicate that the two modes of transportation are inclined to serve in the areas with well development. 4.3. Diversity 1: ICC network is connected more densely and internally Much evidence shows that ICC connections are more internally dense. First, ICC service covers more cities and city-pairs than HST does (Fig. 3). Second, the proportion of the ICC network shows a steeper 5

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Fig. 4. Distance distributions of the high-speed train and inter-city coach networks.

according to schedule data. As shown in Fig. 7, nodes are cities with both HSR station(s) and ICC station(s), and links composed of city pairs served by direct HSTs and ICCs. Based on the schedule data collected, we obtain 229 cities and 3335 city pairs served by HSTs and ICCs simultaneously. The overlap mainly concentrates in East China and indicates that overlapping network is corresponding to the populous region with developed economy. This suggests that both HST and ICC operators are more likely to allocate service in relatively well-developed areas, and these areas need to deal with HSR and highway competition. Besides, the frequency in the overlapping network account for 54.0% and 76.6% of their total HSTs and ICCs networks, respectively. As for the top 10 city-pairs in HSTs network and ICCs network (Table 1), the RSL values of most are higher than their values in another network. However, comparing the RSL values for HSTs and ICCs in the whole overlapping network, it can be found that most city-pairs have higher RSL values for HSTs (71.7%), while only a few of them are with higher RSL for ICCs (28.3%). From the perspective of the whole overlapping network, it could be concluded that when passengers could choose HST or ICC service for travelling between a city-pair, it is more likely that the HSTs will have advantages on frequency rather than ICC.

speed rail has a competitive advantage for middle distances (Cheng, 2010; Takatsu, 2011; Zheng and Kahn, 2013; Vickerman, 1997). Last but not least, > 80% of city-pairs in first four levels in ICC network are connections among cities located in the same province, which also shows that administrative boundaries have stronger restrictive influence. In contrast, > 50% of city-pairs in first four levels in HST network across different provinces. This also indicates that the ICC service is constrained to a shorter distance. 4.4. Diversity 2: corridor pattern in HST network and core-periphery structure in ICC network The HST network has improved the connectivity of cities along the trunk high-speed rail lines (Jiao et al., 2017; Shaw et al., 2014). In HST network, city-pairs in the first four levels have obvious linear distribution characteristics (Fig. 6(a)) and > 70% of these city-pairs are distributed along the “four vertical and four horizontal” system of the national rail network in China, especially for Beijing–Shanghai and Beijing-Guangzhou high-speed rails. In the top 20 city-pairs based on RSL, 90% city-pairs are distributed along the two trunk high-speed rails. On the other side, city-pairs in the first four levels in ICC network show the regional “core-periphery” distribution characteristic and locally form several star-like structures (Fig. 6(b)), which is significantly different from the HST network. For example, in Guangdong province, city-pairs in the ICC network are mainly centered in Guangzhou and Shenzhen, and > 60% of the coaches are operated between the two cities or connected to them. In the Beijing metropolitan area, the neighbor cities tend to establish more interactions with Beijing rather than other cities and present a radial distribution. Similarly, around some regional centers in China such as Wuhan, Chengdu, Chongqing, Shenyang, and Nanning, the ICC network also show the “core-periphery” structure within the province scale.

5.2. Community structures Community structure could measure the agglomeration characteristics of networks. Using the method in Section 3.4, community structures can be found in both the HST and ICC networks, and their modularity is 0.427 and 0.631, respectively. All cities in the HST (ICC) network can be divided into 9 (13) community groups (Fig. 8). We rank each community in both the two networks using its PageRank value from the largest to the smallest. Transportation promotes economic development as much as developed economy facilitates transportation. Thus, areas with economic development may have higher PageRank value. Comparison of two networks' community structures identifies Community 1 as located around the Yangtze River Delta region. As this region is a major economic center in China, the community's HST and ICC networks are connected internally well. Meanwhile, communities in the two networks are affected by geographic proximity, as inter-city connections on ground transportation significantly rely on physical networks. For instance, affected by their geographical isolation, the HST networks in Inner Mongolia (Community 9) and Hainan (Community 7) are divided into two separate groups. Hainan province (Community 11) in the ICC network also follow this tendency. Moreover, some regions have similar socioeconomic statuses, natural conditions, and local cultural backgrounds.

5. Spatial organization and governance effect 5.1. Spatial interaction From the perspective of personal travel, the alternative among various modes of transportation has been affected by the spatial pattern of the competitive network. Here, we investigated the competition between HSTs and ICCs in their overlapping network, which is similar to the method in previous studies (Wang et al., 2015; Yang et al., 2018a). The overlapping network consists of cities with at least an HSR station and a coach station and city-pairs with direct HSTs and ICCs, 6

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Fig. 5. City centrality of the high-speed train and inter-city coach networks. Note: Cities in the HST and ICC networks are divided into five levels with the critical values of 1 times, 2 times, 4 times and 6 times of the average of PI.

example, Community 1 mainly comprises cities along the BeijingShanghai, Jinan-Qingdao, and Shanghai-Wuhan high-speed rails. In ICC network, the scope of the communities is obviously constrained by the provincial boundaries, similar with the analysis by Chen et al. (2018). Some communities are located within a single province or are composed of most cities in the province. For example, Community 11

In such cases, community groups may be identified within several adjacent provinces. However, the spatial characteristics of communities in the two networks have significant difference. Communities in the HST network follows the spatial pattern of rail lines, which is similar to the hierarchical system according to its city-pair connectivity in Section 4. For

7

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Fig. 6. City-pair connectivity of the high-speed train and inter-city coach networks. Note: Considering the classification of Jenks natural breaks method, city-pairs are mainly divided into five levels based on the values of RSL.

organization model of HSTs and ICCs, as shown in Fig. 9. Three models can be captured: (a) ICC network without HSTs, (b) Early stage of HST network construction, and (c) Regional integration of two transportation modes, reflecting the evolutionary patterns of HST and ICC networks and spatial variations from the west to east in China. In regions where high-speed rail has not yet developed, ICCs play a

consists of all cities in Hainan province and Community 13 comprises most of the cities in Xinjiang province.

5.3. Organization models Based on the above analyses, this section summarizes the spatial 8

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Fig. 7. The overlapping network of the high-speed trains and inter-city coaches.

where most cities tend to connect to Lhasa or Urumqi, the respective provincial capitals. The construction of transportation corridors is significant for HST development (Givoni, 2006; Campos and de Rus, 2009; Perl and Goetz, 2015). Following this strategy, core cities (usually provincial capital cities) are connected with HST lines. In China, the HST network has covered most provincial capital cities except for Yinchuan and Lhasa. With the development of HST corridors, some inter-city connections

very important role in the daily travel of residents. This ICC network mainly forms a “core-periphery” structure centered on the provincial capital (Copus, 2001). As shown in Fig. 9(a), trunks have been built between the core city and sub-core/periphery cities, and some branch links exist between two periphery cities. Due to the restriction of the distance decay and provincial administrative boundaries, connections across provinces are few. This spatial organization can be seen in Tibet (Community 5) and Xinjiang (Community 13) in the ICC network, 9

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explained by their own administrative models. In China, the National Railway Administration (NRA) is responsible for rail development plans and policies, and the NRA gradually designed and contributed to “the four vertical and four horizontal high-speed rail corridors” across the whole country. Following this guidance, the China Railway Corporation is in charge of construction and operation of rail lines and its 18 local rail corporations are the main body of the rail industry and have an important influence on the HST network. Therefore, the scope of communities in the HST network is similar with the local rail corporations' spatial jurisdiction (Wang and Jing, 2017), and the spatial structure in the HST network mainly shows the distribution along trunk high-speed rails. For ICCs, most coaches rely on freeways to operate, and some coaches may choose other roads. In China, freeways include two parts. One part is national freeway which is financed and maintained by the central government. The other part is local freeway, which is planned, financed, constructed and operated by the provincial government. Also, the local freeway is usually extended from the provincial capitals to other cities in the province with developed economy, so does the ICCs. In terms of operation, regardless of state-owned or private, ICC corporations all need to apply permission from the corresponding management departments if they want to obtain passenger transport qualifications and develop new routes. Thus the ICC routes are approved and supervised by the provincial transport administrative department. Although the corporations have certain autonomy in pricing strategies, ICC fares still needs to follow regulations and be supervised by the local transport management departments. Once again, > 50% city-pairs within the provinces in China are < 200 km, which is similar with the advantage distance of ICCs in section 4.1. Therefore, the community structure is constrained by provincial administrative boundaries.

Table 1 Comparision of RSL values of top 10 city-pairs in the overlapping network. Rank

Top 10 in high-speed trains network

Top 10 in inter-city coaches network

City-pair

RSL(HSTs/ ICCs)

City-pair

RSL(ICCs/ HSTs)

1

Nanjing−Wuxi

4.36/0.71

7.94/2.99

2 3

Shanghai-Nanjing Shanghai-Suzhou

4.16/1.03 3.72/2.24

4

Nanjing-Changzhou

3.64/1.04

5 6 7 8

Nanjing-Suzhou Shanghai-Wuxi Wuxi-Changzhou GuangzhouShenzhen Shanghai-Changzhou Suzhou-Wuxi

3.55/1.53 3.24/0.69 3.13/0.55 2.99/7.94

GuangzhouShenzhen Xiamen-Zhangzhou GuangzhouDongguan ShenzhenDongguan Guangzhou-Foshan Shenzhen-Huizhou Guangzhou-Zhuhai Xiamen-Quanzhou Shenzhen-Foshan GuangzhouZhongshan

4.08/0.04 4.04/1.16

9 10

2.93/0.46 2.88/0.73

6.17/1.10 5.53/2.21 5.35/2.05 4.77/2.47 4.39/0.95 4.36/0.92 4.22/1.70

between the core and sub-core/periphery cities may be strengthened by ICCs, while some may be weakened especially for the links along highspeed rails. This is consistent with the “corridor effect” of accessibility (Shaw et al., 2014). However, for other periphery cities, ICCs are still an important carrier for their residents' travel, as high-speed rail has not been built there yet. For example, in Chengdu-Chongqing region (Community 8 in the ICC network), the HST connection between Chengdu and Chongqing is of high frequency and belongs to the third level. However, the inter-city connections of other cities to the cores (i.e. Chengdu and Chongqing) still rely on travel by ICCs due to underdevelopment of the high-speed rail. With rapid HST development, the comprehensive network and convenient services have been extended, along with the appearance of polycentric systems and regional integration (Limtanakool et al., 2007). In such cases, cities with adequate HST services may transform into subcore cities (even new cores), connecting to other cities more frequently. Conversely, some cities may degrade if they do not see enough HST development between inter-city connections. According to categories of links, trunks are usually HST links because the market of ICCs in long distance links is squeezed by HSTs. Some inter-city connections are linked by HST and ICC simultaneously because of large traffic demand. Moreover, inter-city connections by coaches basically maintain the “core-periphery” structure. Still, for short-distance journeys, travelers may choose to take ICCs. This indicates that the decrease of the ICC market share is caused by the decline in long-distance journeys. Nevertheless, ICCs still play an irreplaceable role in connecting cities without trains. This study uses HSTs and ICCs in the Yangtze River Delta region to better illustrate the stage of regional integration (Fig. 9(c)). The region is one of the most developed regions in China from the perspective of economic growth, population aggregation, and transportation connectivity. The HSTs and ICCs have been identified in Community 1 (Fig. 8). Various trunks by HST can be found between the core (Hangzhou, Nanjing, and Shanghai) and sub-core (Hefei, Ningbo, Suzhou, and Wuxi) cities, and they all hold high frequency. Most of these city-pairs belong to the second level in the HST network (Fig. 6). Other inter-connections by coaches take the role of branches linking to the HST network or ICC trunks. For example, without HST service, Zhoushan forms an ICC trunk with the sub-core Ningbo, and the citypair belongs to the second level in the ICC network.

6. Conclusions and discussion Using timetable data, we conducted a comparative analysis of intercity connections by HST and ICC in China. Overall, the two modes of transportation are suitable to depict and express urban relationships in different spatial scales and administrative divisions. We found that HSTs have a great influence on ICC network as a brand-new transportation mode. Natural condition, layout, geographical distance, and administration system are important factors affecting the structure and organization of ground transportation network. Similarly, both the HST and ICC networks were restricted by distance decay and geographic proximity, and they present obvious community structures. But for the spatial structure, the HST network was more likely driven by the National Railway Administration (Wang and Jing, 2017) and it showed the corridor effect at the national level (Shaw et al., 2014), which was consistent with the “four vertical and four horizontal” corridor planning in China. Diversely, the structure of the ICC network is mainly affected by provincial governance and departments, and it has a star-like topology following the core-periphery structure. This is consistent with the current research based on single or multiple modes of transportation (Chen et al., 2018; Jiao et al., 2017; Yang et al., 2018c). Based on the comparison, one advantage of HSTs is that they serve middle-distance travel between core cities with important administrative or well economic status, while one advantage of ICCs is that they are more suitable for short-distance inter-city connections, which is consistent with the existing research (Cheng, 2010; Takatsu, 2011). For example, the city-pair between Shanghai and Nanjing (two center cities) is about 300 km. Although the fare for the standard seat of HSTs (about 135 RMB) between them is 1.5 times that of ICCs (about 90 RMB), the RSL value in HST network is much higher than that in ICC network. It indicated that HSTs may be more popular between Shanghai and Nanjing. One different story can be seen along the city-pair between Guangzhou and Dongguan which is only about 60 km, the RSL value in ICC network is higher than that in HST network and the fare of

5.4. Administrative governance effect Different spatial characteristics in the HST and ICC networks can be 10

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Fig. 8. Community structure of the high-speed train and inter-city coach networks.

some cities, especially those without HST, the coach service can take the role of feeders connecting to HST lines. These findings revealed the similarity and diversity between HSTs and ICCs, and compared the spatial interaction and organization models of the two networks. Also, this paper offered some policy implications. ICCs in areas without high-speed-rail lines can be feeders to HSTs in the regional transport planning, which is useful for coordinating the two transportation modes. Still, China's high-speed rail is in the process of

HSTs (about 45 RMB) is also about 1.5 times that of ICCs (about 30 RMB). Indeed, the market share of ICCs in the middle distance travel may decrease with the high-speed rail development (DeRus and Inglada, 1997; Vickerman, 1997). More importantly, the two modes of transportation gradually integrate and form different spatial organization at different stages. In the overlapping network, most city-pairs have a greater advantage for HSTs than that for ICCs. However, ICCs still play an irreplaceable role in the regional transportation system. In

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Fig. 9. Main organization model of high-speed trains and inter-city coaches. Note: In Chengdu-Chongqing and Yangtze River Delta regions, city-pairs in the first four levels were selected from Fig. 6 for explict presentation. organizational patterns of city networks in China: A highway passenger flow perspective. J. Geogr. Sci. 28 (4), 477–494. https://doi.org/10.1007/s11442-0181485-x. Cheng, Y.H., 2010. High-speed rail in Taiwan: New experience and issues for future development. Transp. Policy 17 (2), 51–63. https://doi.org/10.1016/j.tranpol.2009.10. 009. Chien, S., Schonfeld, P., 1998. Joint optimization of a rail transit line and its feeder bus system. J. Adv. Transp. 32 (3), 253–284. https://doi.org/10.1002/atr.5670320302. Choi, J.H., Barnett, G.A., Chon, B.S., 2006. Comparing world city networks: a network analysis of Internet backbone and air transport intercity linkages. Glob. Networks 6 (1), 81–99. https://doi.org/10.1111/j.1471-0374.2006.00134.x. Chou, C.C., Shen, C.W., 2018. An exploration of the competitive relationship between intercity transport systems. Transp. Plan. Technol. 41 (2), 186–197. https://doi.org/ 10.1080/03081060.2018.1407526. Copus, A.K., 2001. From core-periphery to polycentric development: concepts of spatial and aspatial peripherality. Eur. Plan. Stud. 9 (4), 539–552. https://doi.org/10.1080/ 09654310123647. Derudder, B., Witlox, F., 2005. An appraisal of the use of airline data in assessing the world city network: a research note on data. Urban Stud. 42 (13), 2371–2388. https://doi.org/10.1080/00420980500379503. Derudder, B., Witlox, F., 2009. The impact of progressive liberalization on the spatiality of airline networks: a measurement framework based on the assessment of hierarchical differentiation. J. Transp. Geogr. 17 (4), 276–284. https://doi.org/10.1016/ j.jtrangeo.2009.02.001. DeRus, G., Inglada, V., 1997. Cost-benefit analysis of the high-speed train in Spain. Ann. Reg. Sci. 31 (2), 175–188. https://doi.org/10.1007/s001680050044. Dupuy, G., Stransky, V., 1996. Cities and highway networks in Europe. J. Transp. Geogr. 4 (2), 107–121. https://doi.org/10.1016/0966-6923(96)00004-X. Givoni, M., 2006. Development and impact of the modern high-speed train: A review. Transp. Rev. 26 (5), 593–611. https://doi.org/10.1080/01441640600589319. Givoni, M., Dobruszkes, F., 2013. A review of ex-post evidence for mode substitution and induced demand following the introduction of high-speed rail. Transp. Rev. 33 (6), 720–742. Gutierrez, J., 2001. Location, economic potential and daily accessibility: an analysis of the accessibility impact of the high-speed line Madrid–Barcelona–French border. J. Transp. Geogr. 9 (4), 229–242. https://doi.org/10.1016/S0966-6923(01)00017-5. Hou, Q., Li, S., 2011. Transport infrastructure development and changing spatial

construction and development, which will inevitably reshape the existing inter-city connections (Jiao et al., 2014). In such a case, the organizational relationship between the two modes can be used as a reference to coordinate the resource allocation of ICCs and HSTs in time. Some limitations in this paper are necessary to mention. Because of the restrictions of data and method, we use timetable data to build the networks based on frequencies, which may be some different from the results based on capacity, namely the number of seats. We mainly focus on topological characteristics and the impact of HSTs on the ICC network, and the cooperation between the two transportation modes are still worth studying in future. Also, future research could consider more transportation modes to fully reflect the inter-city connections. Acknowledgements This research was financially supported by the National Natural Science Foundation of China Grants No. 41722103 and 41771134. References Alderson, A.S., Beckfield, J., 2004. Power and position in the world city system. Am. J. Sociol. 109 (4), 811–851. https://doi.org/10.1086/378930. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E., 2008. Fast unfolding of communities in large networks. J. Stat. Mech. 2008 (10), P10008. https://doi.org/10. 1088/1742-5468/2008/10/P10008. Campos, J., De Rus, G., 2009. Some stylized facts about high-speed rail: A review of HSR experiences around the world. Transp. Policy 16 (1), 19–28. https://doi.org/10. 1016/j.tranpol.2009.02.008. Chang, J.S., Lee, J.H., 2008. Accessibility analysis of Korean high-speed rail: A case study of the Seoul metropolitan area. Transp. Rev. 28 (1), 87–103. Chen, W., Liu, W., Ke, W., Wang, N., 2018. Understanding spatial structures and

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