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.

Transportation is the most basic element in the development of tourism, with expressways being the main channel for tourism transportation in the era of driving tours. The relationship between transportation and tourism development has always been one of the hot topics in tourism geography study. Since the expressway service area is the main node of tourist service facilities in the expressway transportation network, we conducted a field research on 125 service areas and 177 tourist attractions in Guizhou Province, and obtained relevant data. By using the centrality measures for social network analysis, and combining the kernel density analysis with bivariate spatial autocorrelation analysis, this paper explores the centrality characteristics of expressway transportation network, the spatial distribution regularity of tourist attractions, and the spatial coupling between them. The main study conclusions are as follows: (1) The expressway service areas in Guizhou Province gradually decrease from a single core to the periphery. (2) The spatial distribution of tourist attractions presents a weak circular structure with multiple cores in a scattered manner, and the regional differences are apparent. (3) There is positive spatial correlation between the expressway networks and tourist attractions. The spatial coupling between the expressway transportation network and tourist attractions can be divided into four types of clusters: high–high cluster, high–low cluster, low–high cluster, and low–low cluster, with high–high and low–low clusters as the main types. The high–high cluster is the area with the best coupling effect and the most mature tourism. The high–low and low–high clusters have a mismatch between transportation and tourism resources. For such clusters, the spatial coupling can be realized by changing the functions of the service area or improving the quality of the tourist attraction. They have great development potential. The low–low cluster is far away from tourist attractions and markets, thus it has a small value of tourism development, and it is the critical area for future improvement. The conclusion of this study can provide a theoretical basis and scientific suggestions for promoting integrated and coordinated sustainable development of transportation and tourism.