Complexity
Volume 2020 (2020), Article ID 1042506, 22 pages
https://doi.org/10.1155/2020/1042506

Research Article

Analysis of Complex Transportation Network and Its Tourism Utilization Potential: A Case Study of Guizhou Expressways

Sihong Chen,1,2 Jianchao Xi,¹ Menghao Liu,1,2 and Tao Li³

¹Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academic of Sciences, Beijing 100101, China
²College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
³Institute of Finance and Banking, Chinese Academy of Social Sciences, Beijing 100028, China

Correspondence should be addressed to Jianchao Xi; xijc@igsnrr.ac.cn

Received 31 March 2020; Revised 10 May 2020; Accepted 12 May 2020; Published 6 July 2020

Guest Editor: Wen-Ze Yue

Copyright © 2020 Sihong Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Transportation is an example of a typical, open, fluid complex network system. Expressways are one form of complex transportation networks, and expressway service areas serve as infrastructure nodes in the expressway transportation network; hence, their construction has a significant impact on tourism development and utilization. Domestic and foreign studies on complex transportation networks have mostly been conducted from the perspective of railways, air transport, and urban transportation but seldom on expressway transportation networks. This study employed complex network theory, social network analysis, kernel density analysis, and bivariate autocorrelation to characterize the spatial structure of expressway transport networks in terms of geographical centrality. By innovating the coupling of geographical centrality and passenger flow centrality in clustering, the study also quantitatively analyzed the differences between the geographical advantage and actual passenger flow advantage of China’s Guizhou expressway transportation network to analyze the tourism utilization potential of expressway service areas. We found that (1) the geographical centrality of the Guizhou expressway transportation network ranged from −1.28 to 3.33, and its distribution shows a single-core, polyconcentric dispersed spatial structure; (2) the passenger-car flow rate ranged from 15,000 to 3.66 million, and its distribution showed a dual-core, polycentric dispersed structure that is weakly concentric; and (3) there was a positive correlation of 0.22 between the geographical centrality and passenger flow of the Guizhou expressway transportation network, which showed seven cluster types—“high-high,” “moderately high-high,” “low-high,” “moderately low-high,” “high-low,” “moderately high-low,” and “low-low”—for which seven corresponding models of tourism development were proposed. This study broadens the practical application of traffic network complexity research and provides a scientific basis for upgrading and transforming the Guizhou expressway transportation network as well as for developing composite tourism uses for expressway service areas.

1. Introduction

Transportation is one of the six elements of tourism development and the most important intermediary component in the “three-body theory of tourism.” [1] As the main transport corridor for self-driving tourism, expressways play an important role in improving interregional accessibility. With the gradual construction of the “fast forward, slow travel” expressway network, service areas have become part of their infrastructure, and their tourism service functions have expanded and matured. To enable tourists to receive high-quality, convenient, and efficient services, it is necessary to tap the development potential of expressway service areas by reviewing, upgrading, and shaping them into tourist destinations. Doing so will achieve integrated development between expressway service areas and the tourism and leisure industries, which is an important facet of the transportation-tourism relationship.

As the scale of transportation networks expands and their degree of complexity increases, research on transportation network complexity has become an important means to understand the spatial structure and organizational characteristics of transportation networks. A series of network analysis tools, such as multiple centrality assessment (MCA) [2], urban network analysis (UNA) [3], and spatial design network analysis (sDNA) [4], have been proposed, which have facilitated the computation and analysis of the morphological characteristics of complex transportation networks. Western scholars investigating complex transportation networks have mostly explored the centrality distribution of transportation hubs from the perspectives of railway [5, 6], air transport [7–10], and urban transportation [11–13] and have used transportation networks to analyze destination accessibility and the optimization of spatial structures [14, 15]. In contrast, scholars in China have primarily employed transportation network centrality, complexity, and other indicators to study the evolution of the structural characteristics [16] and spatial accessibility [17, 18] of high-speed rail [19], air [20, 21], subway [22, 23], and other traffic networks [24–26].

Transportation networks were first employed in tourism research in the 1920s. Western scholars initially explored them from the perspective of tourism transportation planning [27, 28] followed by an increasing research emphasis on the impact of transportation on tourism demand [29]. The emergence of different transportation modes, such as air transport and high-speed rail, led to changes in the accessibility of tourist destinations [30–33], which affected the spatial behavior and migration patterns of tourists [34–36]. Furthermore, continuous improvements to transportation conditions also resulted in a continual enhancement of tourists’ tourism experience and transportation satisfaction [37]. Meanwhile, environmental [38–40], noise [41, 42], and congestion [43] problems in the transportation network have affected the sustainable development of tourism. In China, as the tourism industry developed, scholars in the 1980s began to explore the relationship between transportation and tourism [44]. The degree of transportation convenience became an important indicator for the development of the tourism industry [45], and changes in transportation facilities also affected the spatial structure of regional tourist destinations [46, 47]. Methods such as social network analysis, analytic hierarchy process, and TOP network were used to quantitatively investigate the relationship between tourism and complex transportation networks [48, 49], and the environmental impact of tourism transportation systems was calculated using ecological footprint, carbon emissions, and other indicators [50]. Furthermore, scenic byways were innovatively defined as tourist attractions for planning and evaluation [51].

As self-driving tourism has developed into an increasingly common phenomenon, expressways have become an indispensable and vital link in tourism development, making it crucial to perform timely complex network analysis on expressways and study their tourism utilization potential. Though several studies have been conducted in China and abroad on the impact of transportation networks on tourism [27–51], research on the relationship between expressway transportation networks and tourism is lacking. Thus, this study examined the expressway transportation network in Guizhou province using complex network theory, social network analysis, kernel density analysis, and bivariate autocorrelation to calculate the spatial clustering of geographical centrality and passenger flow centrality, quantitatively evaluate the difference between geographical advantage and actual passenger flow advantage, and analyze the tourism utilization potential of expressway service areas. Based on the results, seven models of tourism development were proposed, with a view of providing a scientific basis for optimizing the layout of expressway transportation networks and reshaping service areas for tourism.

2. Data and Methods

2.1. Study Area

Guizhou province is located in the inland southwestern region of China at a longitude of 103°36′–109°35′ and a latitude of 24°37′–29°13′ (Figure 1). It covers a total area of 176,200 km², and 92.5% is composed of mountainous and hilly areas including plateaus. Guizhou province is a world-famous mountain tourism destination, also known as the “Mountain Park Province” and the “Summer Capital of China.” Due to its unique mountainous terrain, expressways have become the region’s main transport corridor. The province has 6,450 km of expressways connecting 71 national and provincial scenic areas, and all 5A and most 3A and 4A scenic areas in the province are located within 30 minutes of expressway-connected areas. This has provided the facility guarantee and the tourist source needed for the integrated development of mountain tourism and expressway service areas.

Figure 1: Location of the study area (Guizhou, China).

2.2. Data Source

The first-hand data for this study came from field surveys and investigations of 65 pairs of expressway service areas in Guizhou that were randomly selected by our research team based on the principle of regional averaging. The manager of each expressway service area provided our team with basic information such as the passenger flow rate, deviations in different flow directions, and construction of service areas. Our second-hand data came from the passenger-car flow rate and vehicle registration data at each expressway section for February 3, May 1, June 9, August 1, October 1, and December 3, 2018, which were provided by the Guizhou Provincial Department of Transportation, and two schematic diagrams—one showing the distribution of expressway service areas and parking areas across Guizhou in 2019 and the other showing the expressway operation units across Guizhou in 2017. We also obtained the 2018 Google Earth images with a spatial resolution of 0.6 m, on which ArcGIS vectorization was performed to obtain a map of expressways in Guizhou.

2.3. Methods

2.3.1. Social Network Analysis

Social network analysis primarily consists of three basic centrality indicators—degree, closeness, and betweenness centrality [52]—which were used to measure the connectivity, accessibility, and intermediation, respectively, of expressway transportation networks. These were applied to the expressway network in Guizhou province through Ucinet (Borgatti, S.P., Everett, M.G., and Freeman, L.C. Released 2002. Ucinet 6 for Windows: Software for Social Network Analysis. Harvard, MA: Analytic Technologies) calculations and then converted using factor analysis in SPSS (IBM Corp. Released 2017. IBM SPSS Statistics 25.0 for Windows. Armonk, NY: IBM Corp.) into geographical centrality, which provided a basis to quantitatively investigate the relationship between geographical centrality and passenger flow centrality.

Degree centrality (hereafter, D) measures the number of direct links to a node and is calculated by the following equation [52]:

In (1), n is the number of nodes and x is the number of links between a given node and other nodes.

Closeness centrality (hereafter, C) measures the average length of the shortest distance between each node pair and is calculated by the following equation [12, 52]:

In (2), is the shortest distance between nodes i and j.

Betweenness centrality (hereafter, B) measures the number of times a node is traversed on the shortest path between other nodes and is calculated by the following equation [12, 52]:

In (3), is the shortest path between nodes a and b, and is the number of times i is traversed on the shortest path between a and b, where .

In the network structure, degree centrality, closeness centrality, and betweenness centrality calculate the convenience, accessibility, and intermediary of network nodes from three perspectives [52]. Any centrality can only reflect one side of the network structure and cannot reflect the overall characteristics of the network space structure. To represent the geographical location features of the network structure in a unified way, this study puts forward the overall features of the geographical location of the network structure reflected by geographical centrality. Geographical centrality refers to the degree of a node’s location center in the whole network, which is a unified indicator of network structure convenience, accessibility, and intermediation based on three network centricity characteristics: degree centrality, closeness centrality, and betweenness centrality. The higher the geographical centrality value, the better the location of the node in the network structure.

Considering that degree centrality, closeness centrality, and betweenness centrality are representative and there is a certain correlation among them that meets the calculation conditions of factor analysis, factor analysis in SPSS was used to calculate the weight index of the three types of centrality. Because the range of values for degree centrality, closeness centrality, and betweenness centrality is quite different, Z-score is used to standardize and obtain normalized values for the three indexes.

Geographical centrality (hereafter, G) is calculated as follows:

In (4)–(7), are the weights of degree centrality, closeness centrality, and betweenness centrality, which can be obtained by factor analysis. , , and are the normalized values for degree centrality, closeness centrality, and betweenness centrality, which can be obtained from a Z-score standardization calculation, where , and are the mean values and , , and are the standard deviations [53].

2.3.2. Kernel Density Analysis

Passenger flow centrality is expressed by the total number of passenger cars passing through the expressway section where the service areas are located. It is an important factor affecting the reception and provision of tourism services by service areas. ArcGIS (Environmental Systems Research Institute (ESRI). Released 2014. ArcGIS 10.2 for Desktop. Redlands, CA: ESRI) was used to input the data on passenger-car flow rates for each expressway section into the vector database, and the kernel density plot of the passenger-car flow rate was computed using spatial kernel density analysis.

The kernel density is calculated as follows [54–56]:

In (8), n is the number of nodes in the service area of the expressway network, h is the bandwidth, and is the deviation between and .

In (9), SD is the standard distance, and is the median distance.

2.3.3. Bivariate Spatial Autocorrelation Analysis

Tourism utilization potential was evaluated using the consistency in the spatial distribution between geographical centrality and passenger flow centrality, which was determined using coupling analysis spatial autocorrelation based on GeoDa (Luc Anselin. Released 2019. GeoDa 1.14 for Windows. Chicago, IL: Luc Anselin). This included both global and local spatial autocorrelation.

The equation for global spatial autocorrelation is given as follows [57]:

In (10), is the spatial weight between i and j, and and are the deviations of attributes i and j from the mean.

The equation for local spatial autocorrelation is given as follows [57, 58]:

In (11), is the value of attribute i at k; is the value of attribute j at l; and and are the variances of attributes k and l.

3. Analysis of Results

3.1. Distribution Characteristics of Geographical Centrality

3.1.1. Degree Centrality

Based on calculations using formula (1), we can see from Figure 2 and Table 1 that the degree of the Guizhou expressway transportation network ranges from 0 to 0.07. Its distribution shows a clear polycentric clustered structure, with the expressway service areas around Guiyang (Area A), Bijie (Area B), and Zunyi (Area C) showing the highest levels of degree centrality and being primarily concentrically distributed. Area A is the primary core region of the overall network with respect to degree and is mainly distributed around Guiyang. The Longdongbao and Yunfeng service areas have the highest number of direct links to other nodes, both with a degree of 0.0726. These are the service areas with the best transportation accessibility and convenience in Guizhou province. Areas B and C are secondary core regions and are distributed around Bijie and Zunyi. The degrees of service areas within these regions, including Hantun, Xiangshui, Jiudongtian, and Shuiyangwan, are 0.05–0.07. Their transportation accessibility and convenience are second only to the Longdongbao and Yunfeng service areas. The degree of 50.4% of service areas ranges between 0.03 and 0.05 and that of 39.20% of service areas ranges between 0.01 and 0.03. These service areas have average transportation accessibility and are considered transitional service areas between the core and peripheral regions. The degrees of the Feigezi, Daxiaojing, Daozhen, and Songtao service areas are only 0.0081, and they have the lowest transportation convenience. These service areas have the fewest direct links with other service areas in the entire expressway transportation network.

Figure 2: Kernel density plot of degree in the Guizhou expressway network.

Table 1: Degree of expressway service areas in Guizhou province.

Name
Degree

Longdongbao
0.0726

Yunfeng
0.0726

Majiang
0.0645

Hantun
0.0565

Xiangshui
0.0565

Jiudongtian
0.0565

Zhijindong
0.0565

Shuiyangwan
0.0565

Huaxi
0.0565

Longchangping
0.0484

Liupanshui
0.0484

Zhijin
0.0484

Qianxi
0.0484

Zunyi
0.0484

Xiazi
0.0484

Baiyun
0.0484

Longgong
0.0484

Yangwu
0.0484

Huangping
0.0484

Sansui
0.0484

Shuijing
0.0403

Jinyinshan
0.0403

Maige
0.0403

Liutang
0.0403

Xifeng Wenquan
0.0403

Huoshipo
0.0403

Yongle
0.0403

Dongtun
0.0403

Changerying
0.0403

Huishui
0.0403

Shangbao
0.0403

Longgang
0.0403

Sinan
0.0403

Dashaba
0.0403

Yuqing
0.0403

Baiyangping
0.0403

Shibing
0.0403

Baojing
0.0403

Sankeshu
0.0403

Weining
0.0323

Dewu
0.0323

Jichangping
0.0323

Yanjiao
0.0323

Xixi
0.0323

Bijie Xinhua
0.0323

Jinsha
0.0323

Renhuai
0.0323

Hailong
0.0323

Wujiang
0.0323

Kaiyang
0.0323

Jiuchang
0.0323

Qingyan
0.0323

Qinglong
0.0323

Liuguan
0.0323

Danxia
0.0323

Baotian
0.0323

Ziyun
0.0323

Anlong
0.0323

Qingshuijiang
0.0323

Tianxingqiao
0.0323

Guiding Tianfu
0.0323

Mouzhudong
0.0323

Weng’annan
0.0323

Weng’anxi
0.0323

Weng’andong
0.0323

Weng’anbei
0.0323

Fuxing
0.0323

Fuyang
0.0323

Huaqiao
0.0323

Yangchang
0.0323

Kuanchang
0.0323

Xingren
0.0323

Hanlianhua
0.0242

Faer
0.0242

Lezhi
0.0242

Liuguanghe
0.0242

Xishui
0.0242

Zunyi Xiangyang
0.0242

Tongzi
0.0242

Shangji Wujiang
0.0242

Guanling
0.0242

Hongguo
0.0242

Wusha
0.0242

Zhenfeng
0.0242

Kanbian
0.0242

Wangmo
0.0242

Sanglang
0.0242

Changshun
0.0242

Haohuahong
0.0242

Xinzhai
0.0242

Libo
0.0242

Jiuqian
0.0242

Sandong
0.0242

Xinmin
0.0242

Luoxiang
0.0242

Liping
0.0242

Fuquan
0.0242

Meitandong
0.0242

Meitan
0.0242

Zhengan
0.0242

Guanzhou
0.0242

Dewang
0.0242

Tongren Xiangyang
0.0242

Wenquan
0.0242

Tongren
0.0242

Yina
0.0161

Wanglong
0.0161

Tuanjie
0.0161

Liangcun
0.0161

Yata
0.0161

Bianyang
0.0161

Pingtang
0.0161

Sige
0.0161

Yueliangshan
0.0161

Zhaoxing
0.0161

Feilonghu
0.0161

Xihe
0.0161

Panxin
0.0161

Chadian
0.0161

Jinping
0.0161

Qiandongnan Xinhua
0.0161

Feigezi
0.0081

Daxiaojing
0.0081

Daozhen
0.0081

Songtao
0.0081

The degree of the expressway transportation network in Guizhou province shows a polycentric distribution, with the three high-value areas (Guiyang, Bijie, and Zunyi) forming the core from which the degree gradually declines as we move outward to the periphery. The degree of Guiyang is far higher than that of Bijie and that of Zunyi; therefore, the core transportation hub is mainly located in the Guiyang urban area.

3.1.2. Closeness Centrality

Based on calculations using formula (2), we can see from Figure 3 and Table 2 that the closeness of the Guizhou expressway transportation network ranges between 9 and 25. Its distribution shows a “core-periphery-margin” spatial structure. Area A is the core region with high values of closeness and is mainly located in the south of Guiyang and northwest of Qiannan. The closeness of the Longdongbao, Yunfeng, Yongle, Huoshipo, Huaxi, and Baiyun service areas is all above 20. Among them, the closeness of the Longdongbao service area is 24.2661; thus, this is the service area with the shortest average time needed to reach all other service areas within the network. Areas B and C are located at Zunyi and Weng’an and are small areas with relatively high closeness. The closeness of the Xiazi, Zunyi, Weng’anxi, and Weng’andong service areas ranges between 17 and 20, indicating that the paths to other service areas and the average time taken are relatively short. Regions that are far away from Areas A, B, and C but have high kernel densities include the areas around Bijie City, Qianxi County, and Sansui County, where the closeness of the service areas varies from 15 to 20. The closeness of the Weining, Sanglang, and Libo service areas is less than 15, indicating that their geographical locations are relatively marginal, where the average path to other service areas and the average time taken are comparatively long. The Songtao, Liangcun, and Feigezi service areas have the lowest closeness level, ranging between 11 and 9 and, accordingly, are the most remote service areas for travel.

Figure 3: Kernel density plot of closeness in the Guizhou expressway network.

Table 2: Closeness of expressway service areas in Guizhou province.

Name
Closeness

Longdongbao
24.2661

Yunfeng
23.3522

Yongle
23.0056

Huoshipo
22.7523

Huaxi
22.7106

Baiyun
22.1429

Kaiyang
21.9858

Zhijindong
21.9081

Guiding Tianfu
21.7926

Huangping
21.7544

Zhijin
21.6405

Mouzhudong
21.6405

Huishui
21.6028

Qingyan
21.4533

Longgang
21.4162

Jiuchang
21.3793

Maige
21.3425

Longgong
21.1244

Yangwu
20.8754

Qianxi
20.7705

Xifeng Wenquan
20.7358

Dongtun
20.5298

Majiang
20.5298

Liuguanghe
20.2946

Shangji Wujiang
19.8400

Liutang
19.6513

Wujiang
19.5893

Qingshuijiang
19.5893

Fuquan
19.4969

Shangbao
19.4662

Shuiyangwan
19.2547

Yuqing
19.2248

Lezhi
19.1654

Xiazi
19.1063

Weng’anxi
19.0476

Changshun
18.8450

Xingren
18.7311

Sankeshu
18.6747

Zunyi
18.6186

Shibing
18.6186

Yanjiao
18.4799

Weng’annan
18.4524

Haohuahong
18.4250

Guanling
18.3704

Ziyun
18.1287

Weng’andong
17.9971

Kanbian
17.9191

Xixi
17.8674

Jinsha
17.8674

Dashaba
17.6638

Renhuai
17.5887

Tianxingqiao
17.5637

Meitan
17.5637

Jiudongtian
17.3913

Weng’anbei
17.3913

Hailong
17.3184

Liupanshui
17.2943

Yangchang
17.2702

Huaqiao
17.1034

Bijie Xinhua
16.9399

Baiyangping
16.7794

Hantun
16.6443

Xiangshui
16.6443

Xinzhai
16.5997

Sinan
16.5775

Baojing
16.5554

Fuxing
16.5333

Pingtang
16.4238

Sansui
16.4021

Kuanchang
16.3804

Wenquan
16.2516

Qinglong
16.2304

Tongzi
16.2092

Dewu
16.1880

Zhenfeng
16.0207

Dewang
15.8771

Bianyang
15.8568

Feilonghu
15.8365

Meitandong
15.7560

Longchangping
15.5975

Wangmo
15.5975

Shuijing
15.5388

Jinyinshan
15.5388

Tongren Xiangyang
15.3276

Sige
15.1961

Tuanjie
15.1589

Faer
15.1220

Sandong
15.1220

Fuyang
15.1035

Guanzhou
15.0485

Weining
14.8681

Sanglang
14.6919

Libo
14.6054

Chadian
14.4860

Changerying
14.3519

Liuguan
14.3353

Jinping
14.3187

Xihe
14.0113

Hanlianhua
13.9483

Yata
13.8393

Tongren
13.7168

Jichangping
13.6414

Xinmin
13.4490

Jiuqian
13.3909

Yina
13.3765

Zhengan
13.3333

Zunyi Xiangyang
13.2905

Danxia
13.0115

Baotian
12.9436

Hongguo
12.8898

Daxiaojing
12.8232

Anlong
12.7835

Qiandongnan Xinhua
12.7703

Wusha
12.7049

Luoxiang
12.1450

Panxin
12.0976

Yueliangshan
11.9923

Xishui
11.7983

Wanglong
11.7759

Daozhen
11.7759

Liping
11.6105

Zhaoxing
11.0912

Songtao
10.8014

Liangcun
10.5802

Feigezi
9.5753

The high values of closeness in the Guizhou expressway transportation network are primarily distributed around Guiyang city, where the closeness levels are far higher than those of other regions. Thus, we can conclude that the route from Guiyang to all other service areas is the shortest and takes the least time on average, which indicates that Guiyang occupies a core position in the expressway transportation network of the entire Guizhou province.

3.1.3. Betweenness Centrality

Based on calculations using formula (3), we can see from Figure 4 and Table 3 that the betweenness of the Guizhou expressway transportation network varies from 0 to 35. Its distribution shows a polycentric dispersed structure that is weakly concentric, where the overall layout is relatively concentrated, but the high-value areas are relatively scattered. Areas A, B, C, and D form the core areas of betweenness. Area A has the highest betweenness and is spatially located in the south of Guiyang city. In Area A, the betweenness of the Longdongbao service area is the highest at 34.8013, indicating that it is located on the greatest number of paths between pairs of service areas and is a “necessary path” for passenger flow between service areas. The betweenness of other service areas is also relatively high: for example, the betweenness of the Yunfeng, Yongle, and Guiding Tianfu service areas ranges between 10 and 20; thus, they occupy a core position. Areas B, C, and D are smaller areas concentrated around the Qianxi-Zhijin-Longgong region, distributed along the Zunyi-Xiazi route, and centered on Huangping, respectively. Their betweenness levels range from 10 to 25, forming small-scale “core-periphery” concentric structures. However, there are no transitional zones between these areas, and the betweenness drops to a low level in the intermediate areas—for example, that of the Maige service area is only 1.8884. This reflects the weak connectivity of the service areas in the intermediate concentric layers. As the Yina, Wanglong, Feigezi, and Tongzi service areas are not located between any node pairs, their betweenness is 0, which indicates that they are located in the marginal regions of the expressways connecting Guizhou with the surrounding provinces.

Figure 4: Kernel density plot of betweenness in the Guizhou expressway network.

Table 3: Betweenness of expressway service areas in Guizhou province.

Name
Betweenness

Longdongbao
34.8013

Huangping
22.3045

Yunfeng
20.1288

Yongle
18.6511

Guiding Tianfu
13.1280

Qianxi
11.7287

Shuiyangwan
11.3840

Longgong
11.3290

Xiazi
10.8608

Zhijindong
10.6430

Yuqing
10.5387

Tianxingqiao
9.4155

Meitan
9.3063

Renhuai
9.2840

Shibing
8.8271

Yangwu
8.8057

Liutang
8.5227

Tuanjie
7.8023

Lezhi
7.6282

Huishui
7.4428

Kaiyang
7.2087

Qingshuijiang
7.0535

Liupanshui
6.9696

Longgang
6.8019

Guanling
6.6514

Sinan
6.2995

Zunyi Xiangyang
6.2943

Zhijin
5.9361

Dashaba
5.8976

Qinglong
5.8694

Fuxing
5.6472

Huaxi
5.5953

Majiang
5.5557

Jiudongtian
5.3775

Huoshipo
5.0972

Shangbao
5.0533

Shangji Wujiang
4.8513

Ziyun
4.8175

Sige
4.8009

Xifeng Wenquan
4.7644

Jinsha
4.6453

Kuanchang
4.6282

Bijie Xinhua
4.3175

Kanbian
4.0857

Yanjiao
4.0442

Baiyun
3.8784

Changerying
3.8274

Xinmin
3.7398

Wujiang
3.5778

Tongren
3.5464

Xingren
3.5093

Sansui
3.4839

Haohuahong
3.3451

Zhenfeng
3.3341

Jinping
3.2033

Xishui
3.1996

Wangmo
3.1000

Tongren Xiangyang
3.0211

Weng’anxi
3.0122

Mouzhudong
2.9682

Meitandong
2.9068

Sankeshu
2.8848

Faer
2.7247

Xixi
2.7239

Huaqiao
2.6556

Jiuchang
2.4818

Liuguanghe
2.4729

Fuyang
2.4458

Liuguan
2.2368

Changshun
2.1933

Hantun
2.1835

Xiangshui
2.1835

Xinzhai
2.1815

Weng’anbei
2.1618

Chadian
2.0497

Fuquan
2.0174

Zhengan
1.9616

Bianyang
1.9473

Weining
1.9296

Yangchang
1.9258

Luoxiang
1.8949

Maige
1.8884

Qiandongnan Xinhua
1.8308

Baiyangping
1.7890

Sanglang
1.7886

Sandong
1.7691

Weng’andong
1.7586

Dewang
1.7172

Jichangping
1.6521

Liangcun
1.6129

Panxin
1.6129

Zunyi
1.6079

Weng’annan
1.4250

Longchangping
1.4026

Feilonghu
1.1876

Dewu
1.1677

Jiuqian
1.1269

Xihe
1.0631

Libo
1.0068

Liping
0.8896

Yata
0.7684

Danxia
0.7096

Baotian
0.6217

Hanlianhua
0.6210

Anlong
0.5825

Baojing
0.5414

Qingyan
0.4819

Dongtun
0.3863

Wenquan
0.3309

Yueliangshan
0.2732

Hailong
0.1672

Yina
0.0000

Shuijing
0.0000

Jinyinshan
0.0000

Wanglong
0.0000

Feigezi
0.0000

Tongzi
0.0000

Hongguo
0.0000

Wusha
0.0000

Daxiaojing
0.0000

Pingtang
0.0000

Zhaoxing
0.0000

Daozhen
0.0000

Guanzhou
0.0000

Songtao
0.0000

The core region with respect to betweenness in the Guizhou expressway transportation network shows a clear trend of dispersion and is centered around Guiyang, Zhijin, Zunyi, and Huangping. The service areas in these regions occupy a core position and are connected to a relatively large number of other service areas. However, upon expanding the scope, we observe a sharp drop in the effective “intermediary” connective function of the service areas, with the formation of large, discontinuous intermediate regions.

3.1.4. Geographical Centrality

Based on calculations using formulas (4)–(7), the result of the Kaiser–Meyer–Olkin test is 0.715 and the Bartlett test significance value is 0.000, which is below the significance level of 0.05 and is suitable for factor analysis. The weight coefficients calculated using factor analysis for degree, closeness, and betweenness are 0.388, 0.394, and 0.374, respectively. We can see from Figure 5 and Table 4 that the geographical centrality of the Guizhou expressway transportation network ranges from −1.28 to 3.33. Its distribution shows a single-core, polyconcentric dispersed spatial structure.

Figure 5: Kernel density plot of geographical centrality in the Guizhou expressway network.

Table 4: Geographical centrality of expressway service areas in Guizhou province.

Name
Geographical centrality

Longdongbao
3.3296

Yunfeng
2.1029

Huangping
2.0033

Yongle
1.8517

Guiding Tianfu
1.2610

Zhijindong
1.1585

Longgong
1.0921

Qianxi
1.0799

Shuiyangwan
0.8953

Huaxi
0.8710

Yangwu
0.8700

Kaiyang
0.8338

Huishui
0.8297

Xiazi
0.8133

Huoshipo
0.7897

Yuqing
0.7788

Longgang
0.7584

Zhijin
0.7438

Liutang
0.6768

Baiyun
0.6478

Majiang
0.6296

Shibing
0.5756

Qingshuijiang
0.5333

Xifeng Wenquan
0.5214

Lezhi
0.5017

Mouzhudong
0.4695

Tianxingqiao
0.4690

Renhuai
0.4620

Meitan
0.4364

Jiuchang
0.4010

Shangbao
0.3905

Maige
0.3756

Shangji Wujiang
0.3716

Guanling
0.3316

Liupanshui
0.2989

Wujiang
0.2687

Qingyan
0.2577

Liuguanghe
0.2454

Dashaba
0.2376

Jiudongtian
0.2137

Ziyun
0.1871

Yanjiao
0.1706

Dongtun
0.1634

Weng’anxi
0.1604

Xingren
0.1602

Jinsha
0.1426

Sinan
0.1373

Sankeshu
0.1301

Fuquan
0.1146

Haohuahong
0.0865

Kanbian
0.0819

Fuxing
0.0581

Zunyi
0.0504

Changshun
0.0495

Qinglong
0.0385

Tuanjie
0.0080

Bijie Xinhua
0.0059

Xixi
−0.0037

Weng’annan
−0.0321

Kuanchang
−0.0379

Weng’andong
−0.0615

Sansui
−0.0739

Huaqiao
−0.1009

Weng’anbei
−0.1038

Xiangshui
−0.1194

Hantun
−0.1194

Yangchang
−0.1364

Baiyangping
−0.1816

Zhenfeng
−0.2039

Sige
−0.2159

Xinzhai
−0.2219

Hailong
−0.2644

Meitandong
−0.2684

Wangmo
−0.2727

Baojing
−0.3036

Zunyi Xiangyang
−0.3076

Tongren Xiangyang
−0.3113

Changerying
−0.3189

Dewu
−0.3244

Longchangping
−0.3292

Dewang
−0.3443

Bianyang
−0.3535

Fuyang
−0.3578

Faer
−0.3586

Wenquan
−0.4047

Feilonghu
−0.4137

Weining
−0.4254

Sandong
−0.4313

Pingtang
−0.4334

Tongzi
−0.4350

Jinping
−0.4432

Tongren
−0.4653

Liuguan
−0.4662

Shuijing
−0.4672

Jinyinshan
−0.4672

Sanglang
−0.4816

Xinmin
−0.4829

Chadian
−0.5108

Libo
−0.5516

Guanzhou
−0.5748

Jichangping
−0.5943

Zhengan
−0.6321

Xihe
−0.6431

Hanlianhua
−0.6601

Yata
−0.6862

Jiuqian
−0.6887

Xishui
−0.7228

Qiandongnan Xinhua
−0.7342

Danxia
−0.7419

Baotian
−0.7568

Anlong
−0.7791

Luoxiang
−0.7804

Yina
−0.8005

Panxin
−0.8318

Hongguo
−0.8349

Wusha
−0.8571

Daxiaojing
−0.8914

Liping
−0.9213

Yueliangshan
−0.9464

Wanglong
−0.9933

Liangcun
−1.0146

Daozhen
−1.0175

Zhaoxing
−1.0758

Songtao
−1.1349

Feigezi
−1.2826

Area A is the core area of geographical centrality and is mainly concentrated in six municipal districts in the south of Guiyang city. Within this area, the geographical centrality of the Longdongbao, Yongle, and Guiding Tianfu service areas ranges between 1 and 3.33, while that of the Huaxi, Qingyan, Huoshipo, Baiyun, and Jiuchang service areas is all greater than 0. These service areas have highly convenient transportation conditions and complete transportation infrastructure, enabling them to occupy the most central geographical location in the Guizhou expressway transportation network. Areas B and C are the central regions of geographical centrality. Area B is centered around the Yunfeng service area and includes the Longgong, Yangwu, Qianxi, and Zhijin service areas, with the geographic centrality ranging from 0.74 to 2.11. However, a small low-value area is formed to the south of the Maige service area due to its lack of expressways in the north-south direction. Area C is centered around the Huangping service area, which has a geographical centrality of 2.0032, indicating that it has a high level of transportation convenience and accessibility. However, its long distance from Guiyang city resulted in its off-center position as a geographically central location. The geographical centrality of the transitional zones beyond Areas B and C ranges from 0 to 1, indicating that these areas have slightly inadequate transportation convenience, accessibility, and intermediation but are not considered remote locations; they account for 39.20% of all service areas. Service areas with geographical centrality ranging between −0.7 and 0 account for 38.40% and are marginal regions in the network with an average geographical location for transportation. Service areas with a geographical centrality of less than −0.7 account for 16% of all areas and are located in the outlying areas of the overall transportation network. These areas include the Yueliangshan, Wanglong, Liangcun, Daozhen, Zhaoxing, Songtao, and Feigezi service areas, which have poor geographical locations for transportation.

The core region with respect to the geographical centrality of the Guizhou expressway transportation network is concentrated in the southern urban area of Guiyang city, where its geographical location has the greatest advantages of centrality—high connectivity, convenience, and accessibility. This is followed by the central region, which is mainly distributed in the periphery of the core region and spreads in the north-south and east-west directions along the main transport corridors; this region has a superior geographical location and infrastructure for transportation as well as high accessibility. The transitional zones are located in the periphery of the central region and have less adequate transportation convenience, accessibility, and intermediation compared to the central region. The marginal regions have average transportation connectivity, while the outlying regions have poor geographical locations for transportation.

3.2. Distribution Characteristics of Passenger Flow Centrality

Based on calculations using formulas (8) and (9), as shown in Table 5 and Figure 6, the passenger flow rate of all sections in the Guizhou expressway network ranges between 15,000 and 3.66 million. Its spatial layout primarily exhibits a dual-core, polycentric dispersed structure that is weakly concentric and relatively dispersed. Zunyi city (Area A) is the cluster region with the highest passenger flow rate in the Guizhou expressway transportation network. Here, the Zunyi service area has the highest passenger-car flow rate at 3.66 million followed by the Xiazi service area at 2.43 million. These service areas primarily receive tourists coming from Chongqing and Sichuan, forming the core region with the largest area with high flow rates. Guiyang city (Area B) attracts a large number of passenger-car transfers by virtue of its large population flow and convenient ring roads. Within this region, the passenger-car flow rate of the Longdongbao service area is 2.37 million, which is second only to the Zunyi and Xiazi service areas and a core region with high flow rates. The two major core regions are connected to each other by north-south expressways. Among them, the Wujiang, Kaiyang, and Shangji Wujiang service areas that are closely connected to Zunyi city have an average passenger-car flow rate of around 1.82 million, whereas that of the Jiuchang, Yongle, Baiyun, and Huoshipo service areas around Longdongbao gradually decreases to 1.4 million.

Table 5: Passenger-car flow rates of expressway service areas in Guizhou province.

Name
Flow

Zunyi
3,661,081

Xiazi
2,431,688

Longdongbao
2,369,429

Wujiang
1,892,692

Kaiyang
1,791,420

Shangji Wujiang
1,790,814

Jiuchang
1,692,232

Yongle
1,473,084

Baiyun
1,362,797

Majiang
1,283,252

Hailong
1,143,308

Sinan
1,118,963

Qinglong
1,085,028

Huoshipo
1,075,324

Longgong
1,065,735

Fuquan
1,022,454

Shuiyangwan
986,808

Guanling
962,574

Huishui
901,144

Liupanshui
869,839

Shangbao
825,564

Mouzhudong
791,746

Huangping
789,166

Xinzhai
763,012

Jiudongtian
728,219

Renhuai
702,210

Qianxi
701,471

Tongren
685,608

Wusha
668,470

Anlong
637,520

Zhenfeng
606,424

Xifeng Wenquan
601,833

Xiangshui
592,971

Fuxing
587,794

Jinsha
585,045

Dewang
578,103

Guiding Tianfu
555,510

Tuanjie
546,301

Tongren Xiangyang
543,816

Zunyi Xiangyang
517,868

Ziyun
511,282

Longchangping
494,850

Changshun
483,479

Changerying
480,507

Yuqing
471,780

Bijie Xinhua
462,451

Tongzi
443,813

Meitan
437,170

Sankeshu
423,887

Dashaba
418,023

Libo
413,940

Zhijindong
403,976

Yata
379,674

Hanlianhua
362,812

Haohuahong
347,559

Maige
331,908

Chadian
322,606

Yanjiao
312,802

Zhijin
309,954

Jinyinshan
302,366

Liping
297,774

Baotian
287,978

Weng’annan
287,713

Xingren
285,228

Yangchang
272,377

Liutang
270,751

Wanglong
268,958

Guanzhou
268,172

Lezhi
256,067

Sansui
253,625

Weng’andong
253,225

Liuguanghe
252,862

Panxin
251,448

Baojing
249,026

Qiandongnan Xinhua
237,566

Faer
235,737

Huaqiao
231,525

Baiyangping
226,532

Yunfeng
222,684

Liuguan
218,770

Bianyang
208,504

Dongtun
203,993

Yangwu
201,454

Huaxi
196,867

Shuijing
194,059

Weining
186,973

Jichangping
178,594

Kuanchang
177,358

Jinping
177,358

Hantun
177,178

Danxia
167,605

Luoxiang
164,756

Meitandong
161,945

Wenquan
154,567

Xixi
150,893

Weng’anxi
141,313

Tianxingqiao
125,982

Weng’anbei
125,768

Qingyan
124,219

Qingshuijiang
112,417

Wangmo
109,460

Shibing
108,028

Sanglang
100,589

Dewu
96,270

Yina
90,703

Fuyang
87,313

Feilonghu
80,973

Xishui
80,198

Liangcun
80,198

Zhaoxing
74,444

Longgang
67,238

Songtao
66,075

Daxiaojing
61,475

Xinmin
58,972

Zhengan
54,161

Xihe
53,562

Sige
49,865

Hongguo
49,457

Pingtang
45,500

Yueliangshan
43,302

Kanbian
40,921

Feigezi
40,099

Sandong
35,431

Jiuqian
28,868

Daozhen
14,934

Figure 6: Kernel density plot of passenger-car flow rates in the Guizhou expressway network.

The “polycenters” are primarily distributed in Guiding (Area C), Xingyi (Area D), Sinan (Area E), and Bijie (Area F). Among them, the Guiding Tianfu, Mouzhudong, Fuquan, and Majiang service areas in Guiding County have an average passenger-car flow rate of about 900,000, receiving passenger cars mostly from Guiyang city. The Wusha, Changerying, and Anlong service areas in Xingyi city experience an average passenger-car flow rate of about 600,000 and receive passenger cars mostly from Yunnan. The Sinan service area in Sinan County has a passenger-car flow rate of up to 1.12 million, primarily from merging passenger cars from Hunan and Chongqing. The Longchangping and Xiangshui service areas in Bijie city experience an average passenger-car flow rate of 540,000, and they generally receive passenger cars from Yunnan and Sichuan.

The average passenger-car flow rate of median-value regions is about 650,000, which follows a zonal distribution along the expressway transport corridors. These regions are concentrated along the peripheries of the Lanhai, Hangrui, Hukun, and Shankun expressways. Other service areas beyond the regions above, such as the Daozhen, Yina, Xinmin, and Daxiaojing service areas, have an average passenger-car flow rate of about 100,000. In particular, areas such as southeast Qiandongnan, southwest Qiannan, southeast Qianxinan, northwest Bijie, and north Zunyi have low passenger-car flow rates, thus constituting the marginal regions.

The spatial distribution of passenger-car flow rates in the Guizhou expressway network is characterized by Zunyi city and Guiyang city as its dual-core, Guiding, Xingyi, Sinan, and Bijie as its secondary centers, and distribution along its transport corridors. However, the low passenger-car flow rates in the margins of the province are also highly significant.

3.3. Analysis of Tourism Utilization Potential of Expressway Service Areas

After calculations using formulas (10) and (11), based on the geographical centrality and passenger flow centrality, a coordinate plot for 125 geographical-passenger flow centrality pairs was created (Figure 7). As shown in Figure 8, there is a positive spatial correlation between the distribution of the geographical centrality and passenger flow centrality of expressway service areas. The bivariate global autocorrelation coefficient is 0.22, which indicates that expressway service areas tend to be built in areas with high passenger flow to a certain extent, thus providing transportation service facilities with high accessibility. Furthermore, the more complete the development of transportation conditions, the greater the passenger flow these areas will attract. To test whether Moran’s I was significant, a Monte Carlo simulation was used in GeoDa. The value was equal to 0.001, indicating that spatial autocorrelation is significant at 99.9% confidence interval.

Figure 7: Distribution of the bivariate local cluster of expressway service areas in Guizhou province.

(a)

(b)

Figure 8: Moran’s I scatter plot and test plot.

The passenger-car flow rate is indicative of the potential tourist flow rate, and the accessibility of the transportation network will affect tourists’ travel willingness and behaviors. Therefore, in Moran’s I scatter plot, values along the geographical centrality y-axis >1, >0 and <1, >−1 and <0, and <−1 are defined as high, moderately high, moderately low, and low geographical centrality, respectively; values along the passenger flow centrality x-axis >0 and <0 are defined as high and low passenger flow centrality, respectively. As the difference between “low geographical centrality-low passenger flow centrality” and “moderately low geographical centrality-low passenger flow centrality” was of limited practical significance, these were merged into “low geographical centrality-low passenger flow centrality” for the analysis. This produced seven major categories, as shown in Table 6.

Table 6: Cluster types of geographical centrality-passenger flow centrality and tourism development models of expressway service areas in Guizhou province.

Cluster type
Expressway service area
Tourism development model

High-high
Qianxi, Jinsha, Wujiang, Kaiyang, Xifeng Wenquan, Jiuchang, Baiyun, Huoshipo, Yongle, Longdongbao, Longgong, Huishui, Guiding Tianfu, Mouzhudong, Fuquan, Majiang
Transportation hub-type service areas

Moderately high-high
Xiangshui, Jiudongtian, Renhuai, Hailong, Shuiyangwan, Zunyi, Xiazi, Shangji Wujiang, Guanling, Ziyun, Shangbao, Huangping
Tourist attraction relying-type service areas

Low-high
Zunyi Xiangyang, Tuanjie, Wusha, Xinzhai, Tongren Xiangyang, Tongren
Tourism gateway-type service areas

Moderately low-high
Longchangping, Liupanshui, Qinglong, Zhenfeng, Anlong, Fuxing, Sinan, Dewang
Secondary gateway-type service areas

High-low
Lezhi, Zhijin, Zhijindong, Xixi, Maige, Liuguanghe, Liutang, Huaxi, Qingyan, Yunfeng, Dongtun, Yangwu, Changshun, Haohuahong, Weng’annan, Weng’anxi, Longgang, Weng’andong, Weng’anbei, Feilonghu, Xingren
Urban leisure-type service areas

Moderately high-low
Yanjiao, Bijie Xinhua, Pingtang, Qingshuijiang, Meitandong, Huaqiao, Yangchang, Yuqing, Baiyangping, Shibing, Wenquan, Baojing, Sankeshu
Rural leisure-type service areas

Low-low
Yina, Weining, Hanlianhua, Shuijing, Dewu, Faer, Jichangping, Jinyinshan, Hantun, Wanglong, Xishui, Liangcun, Feigezi, Tongzi, Liuguan, Hongguo, Danxia, Baotian, Changerying, Kanbian, Wangmo, Yata, Sanglang, Bianyang, Daxiaojing, Tianxingqiao, Libo, Jiuqian, Sandong, Sige, Yueliangshan, Xinmin, Luoxiang, Zhaoxing, Liping, Meitan, Xihe, Zhengan, Daozhen, Fuyang, Guanzhou, Dashaba, Songtao, Panxin, Chadian, Sansui, Kuanchang, Jinping, Qiandongnan Xinhua
Community co-construction and sharing-type service areas

3.3.1. High-High Cluster

Service areas in the “high-high” cluster type are geographically located at the center of the expressway transportation network. These service areas have complete expressway transportation facilities, high transportation convenience, accessibility, and intermediation, as well as a high number of core scenic areas and large passenger-car flow rate. This type of the service area is generally distributed around Guiyang city, as well as in Anshun (Longgong), Bijie (Jinsha and Qianxi), Qiannan (Guiding Tianfu, Mouzhudong, and Fuquan), and Qiandongnan (Majiang). They account for 13.6% of all service areas, with an average geographical centrality of about 0.90 and an average passenger-car flow rate of up to 1.17 million. Service areas in the “high-high” cluster type have the greatest potential for tourism utilization and can rely on major transportation hubs and passenger-car flow rates to enhance the focus of the transportation hub-type service areas.

3.3.2. Moderately High-High Cluster

Service areas in the “moderately high-high” cluster type are supported by high-quality scenic areas and large passenger flow, but their geographical locations are not sufficiently central, and they do not have adequate transportation convenience, accessibility, and intermediation. Hence, the geographical centrality of these service areas is slightly lower than that of the “high-high” cluster type. These service areas are mainly distributed in Zunyi, as well as Bijie (Xiangshui and Jiudongtian), Anshun (Guanling and Ziyun), Qiannan (Shangbao), and Qiandongnan (Huangping). They account for 9.6% of all service areas, with an average geographical centrality of about 0.44 and an average passenger-car flow rate of up to 1.26 million, far exceeding those of the “high-high” cluster type. The rapid development of red tourism in such service areas, including Zunyi, has resulted in large passenger flows from Chongqing, Sichuan, and Hunan. Their potential for tourism utilization is also high, and these regions can rely on the concentrated transfer of large passenger flows. Therefore, the construction of service areas relying on tourist attraction can be improved.

3.3.3. Low-High Cluster

Service areas in the “low-high” cluster type have extremely marginal geographical locations. They are typically distributed at the intersections between Guizhou and other provinces but have high passenger-car flow rates. For example, the Tongren, Xiangyang, Tuanjie, Wusha, and Xinzhai service areas account for 4.8% of all service areas and have an average geographical centrality of about −0.36, but their average passenger-car flow rate is 620,000. Although such service areas are not the core nodes in terms of geographical location, they are situated at crucial gateway positions in Guizhou province, receiving a large volume of tourists from other provinces. These areas have great potential for tourism utilization and development and can serve as “image windows” for external publicity. Therefore, they can be improved by the construction of tourism gateway-type service areas.

3.3.4. Moderately Low-High Cluster

Service areas in the “moderately low-high” cluster type are similar to those in the “low-high” cluster type in that they have high passenger flow but are located at relatively marginal areas in the overall network. For example, the Tongren area (Dewang, Sinan, and Fuxing) receives tourists coming from Dewang and Xiangyang; the Bijie area (Longchangping) receives tourists coming from northwestern Yunnan; and Qianxinan (Anlong, Qinglong, and Zhenfeng) receives Yunnan tourists coming from Wusha. These service areas account for 6.4%, with an average geographical centrality of about −0.14 and an average passenger-car flow rate of 750,000. These types of service areas are located at internal connections with “low-high” service areas and function as secondary gateways for Guizhou province. They can be upgraded through the construction of secondary gateway-type service areas.

3.3.5. High-Low Cluster

Service areas in the “high-low” cluster type have low passenger flow but are geographically situated at core nodes in the overall transportation network. They are generally distributed in the west of Guiyang city, northeast of Anshun city, west of Bijie, north of Qiannan, and west of Qiandongnan. These service areas account for 16.8%, with an average geographical centrality of about 0.41 and an average passenger-car flow rate of 240,000. This type of service area, such as Huaxi and Qingyan, experiences a high level of infrastructure construction but an extremely low passenger flow due to insufficient attractiveness. However, given their proximity to city suburbs and the leisure needs of large urban populations, they can be improved via the construction of urban leisure-type service areas.

3.3.6. Moderately High-Low Cluster

Service areas in the “moderately high-low” cluster type have low passenger flows and insufficiently central geographical locations. They are mostly distributed in northwestern Qiandongnan, as well as Qiannan (Qingshuijiang and Pingtang), Tongren (Huaqian), Zunyi (Meitandong and Yuqing), Bijie (Xinhua), and Liupanshui (Yanjiao). These service areas account for 10.4%, with an average geographical centrality of about 0.03 and an average passenger-car flow rate of 240,000. They are situated neither on the periphery of cities nor at the intersections between provincial borders. To improve, they can be linked with the surrounding villages through the construction of rural leisure-type service areas.

3.3.7. Low-Low Cluster

Service areas in the “low-low” cluster type have extremely low passenger flows and are located in the marginal zones, which are distributed in the northwest of Bijie, north of Zunyi, west of Liupanshui, southeast of Qianxinan, south of Qiannan, northwest of Qiandongnan, and west of Tongren. These service areas account for 39.2%, with an average geographical centrality of about −0.54 and an average passenger-car flow rate of 170,000, indicating their low tourism utilization potential. In such service areas, the construction of comprehensive community service centers and agricultural product trade show centers can be undertaken to develop and upgrade them as community co-construction and sharing-type service areas.

4. Conclusions and Discussion

4.1. Conclusions

This study performed a comprehensive analysis of the complexity of the expressway transportation network in Guizhou province, which enabled us to understand the differences between its geographical advantage and actual passenger flow advantage and allowed us to explore the tourism utilization potential of expressway service areas. Our main conclusions are as follows:

(1)The geographical centrality of the Guizhou expressway transportation network generally ranges between −1.28 and 3.33, and its distribution shows a single-core polyconcentric dispersed spatial structure. The core region is located in the south of Guiyang city, where the geographical centrality is concentrated between 1 and 3.33, and the area had the greatest advantage with respect to centrality. The central regions are distributed in Anshun and Bijie, where the geographical centrality is concentrated between 0.72 and 2.11, and the areas had superior transportation conditions and infrastructure. Other areas are located in the marginal zones of Guizhou province, where the geographical centrality is less than 0, and transportation conditions are poor.(2)The passenger-car flow rate of the Guizhou expressway transportation network mainly ranges between 15,000 and 3.66 million, and its distribution shows a dual-core, polycentric dispersed structure that is weakly concentric. Zunyi and Guiyang are the dual cores of the passenger flow network, with passenger-car flow rates of up to 2.37–3.66 million. Guiding, Xingyi, Sinan, and Bijie are the secondary centers, with an average passenger-car flow rate of about 800,000. The median-value regions had an average passenger-car flow rate of about 650,000, while the marginal regions had an average passenger-car flow rate of about 10,000.(3)On the whole, the geographical centrality indicator and passenger flow indicator of the Guizhou expressway transportation network show a positive correlation of 0.22. This implies that expressway service areas tend to be constructed in areas with dense passenger flows, and core geographical locations have greater passenger flows. Based on the differences between the geographical advantage and actual passenger flow advantage in the expressway transportation network, the tourism utilization potential of the expressway service areas could be divided into seven types. Service areas in the “high-high” cluster type around Guiyang city should focus on their future development as transportation hub-type service areas, while those in the “moderately high-high” cluster type near Zunyi city should focus on their development as service areas relying on tourist attraction. Service areas in the “moderately low-high” and “low-high” cluster types should develop themselves as tourism gateways. Service areas in the “high-low” cluster type around Guiyang city should be developed as urban leisure-type service areas, while “moderately high-low” cluster-type areas should develop their rural leisure-type services. Finally, service areas in the “low-low” cluster type should design their development as community co-construction and sharing-type service areas.

4.2. Discussion

Expressways are an important part of the complex transportation network system. Due to the different geographic locations and passenger-car flow conditions of each node in an expressway network, the functions undertaken in the complex transportation network system are different, forming a complex network relationship with different divisions of labor. The complexity of expressway transportation networks is mainly reflected in the following three aspects: (1) the complexity of connectivity elements—expressways connect a variety of spatial elements such as cities, villages, and attractions; (2) the complexity of network structures, mainly including traffic connectivity, accessibility, intermediation, and other spatial complexity features; (3) the complexity of actual passenger flow—passenger flow trends will lead to expressway transportation network nodes having specific functions, increasing the complexity of expressway transportation networks.

Studies have been conducted to analyze the spatial structure characteristics of complex networks such as railways, airlines, and urban transportation using indicators such as degree centrality, closeness centrality, and betweenness centrality and to delineate the hierarchical structure and group structure of node centrality in transportation network systems [59]. In this study, from the less researched expressway perspective, the complex spatial features of the expressway transportation network were innovatively subsumed into two indicators of geographical centrality and passenger flow centrality, and coupled clustering was used to quantitatively analyze the difference between the geographic advantage and passenger flow advantage of the expressway transportation network. The study found that the most geographically advantaged nodes of the transportation network are often located at the center of the entire network, with important cities at the center, relying on expressway transportation corridors to expand into the surrounding central towns and core scenic areas. However, the large flow of tourists between the core city and the core scenic area has resulted in a severe polarization of passenger flows in the geographic center. The geographically disadvantaged nodes are often distributed in the peripheral areas of the overall network, relying on the input of other provincial passenger flows and radiation into the internal network to drive the surrounding towns, villages, and scenic areas where there are huge differences in passenger flow.

Finally, this study put forward seven types of expressway service area tourism utilization modes in the Guizhou expressway network (Figure 9), and suggestions are as follows:

(1)Transportation hub-type service areas: as a transportation hub, these should focus on promoting the construction of regional tourism distribution centers.(2)Service areas relying on tourism attraction: these service areas should have close relationships with the surrounding tourist attractions, focusing on the core tourist attractions nearby so as to realize the integrated linkage development of the service area and those tourist attractions.(3)Tourism gateway-type service areas: these service areas are location adjacent to service areas at the junction of provinces and cities and should focus on shaping the local tourism “image window” and providing information consulting services.(4)Secondary gateway-type service areas: development of these service areas will mean taking tourism gateway-type service areas and connecting them with other service areas in the province internally, which should strengthen traffic utilization services and tourism information services, such as real-time transportation, urban display, and other tourism services.(5)Urban leisure-type service areas: based on urban leisure needs, the construction of comprehensive service facilities such as large-scale stores and professional markets should be promoted, and these should be developed into urban leisure areas.(6)Rural leisure-type service areas: to develop these types of services areas, by relying on the characteristic agricultural resources around the service area, the surrounding villages should be linked, rural tourism activities should be carried out, and urban rural leisure tourism destinations should be built.(7)Community co-construction and sharing-type service areas: the development of the service area in conjunction with the surrounding ethnic villages should be promoted, and the service area should be made a comprehensive service center for local communities, an exhibition center for the trade of agricultural products, and an exhibition center for the cultural heritage of ethnic minorities.

Figure 9: Seven types of development models for expressway service areas.

4.2.1. Significance

Research on complex transportation networks combines complexity science with transportation science, and expressways are a form of complex transportation networks. Therefore, this study is significant because

(1)Domestic and foreign studies on complex transportation networks have mostly been conducted from the perspective of railways [46], air transportation [33], and urban transportation [24] but seldom on expressway transportation networks. Hence, this study enriches the knowledge on complex transportation networks.(2)This study broadens the scope for the practical application of complexity research on transportation networks by analyzing the spatial structural characteristics of a transportation network and innovatively performing the coupled clustering of geographical centrality and passenger flow centrality to quantitatively analyze the differences between geographical advantage and actual passenger flow advantage.(3)A comprehensive indicator was calculated using factor analysis to uniformly characterize the transportation convenience, accessibility, and intermediation of expressway service areas using geographical centrality. This approach enabled us to avoid problems caused by scattered indicators’ inability to holistically reflect the spatial structural characteristics of transportation networks.(4)This study proposed seven development models that have practical significance in guiding the upgrade and transformation of service areas for optimizing the layout of the expressway transportation network and promoting the composite use of expressway service areas for tourism.

4.2.2. Limitations

In this study, complex network theory, social network analysis, kernel density analysis, and bivariate autocorrelation were employed to analyze the complexity of the Guizhou expressway transportation network and the tourism utilization potential of expressway service areas.

Due to limitations of the data, we cannot deduce the influence of the evolution of the Guizhou expressway traffic network in terms of its tourism utilization potential across time. We merely discuss its static structure characteristics. In the future, however, we will carry out dynamic evolution and simulation prediction research on the complex traffic network and its tourism utilization potential. In addition, this study still leaves room for additional improvement in the understanding of the complexity of transportation networks. It does not take into consideration the impact of aviation, railway, urban transportation, and other transportation networks on tourism utilization potential nor does it account for the impact of urban and rural self-attraction. In the future, we will focus on the impact of the interaction of multiple transportation elements and spatial elements on the tourism utilization potential, as well as complex transportation. The comprehensive influence of the communication network structure on the development orientation of the spatial structure of the tourism destination is the key issue to be explored in future research.

Data Availability

The prior studies are cited at relevant places within the text as references [1–59]. The degree, closeness, betweenness, geographical centrality data, and passenger-car flow rates of expressway service areas in Guizhou province used to support the findings of this study are included within the paper.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This study was supported by the National Natural Science Foundation of China, “Simulation Study on the Spatial Evolution Model and System Optimization of Tourism Urbanization” (no. 41671151).

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