Classifying and Evaluating High-Speed Rail Stations Based on the Node–Place–Connectivity Model: A Case Study of the Chengdu–Chongqing Urban Agglomeration in China

The high-speed rail (HSR) network expands worldwide, especially in China. Many HSR stations are newly built, and the surrounding areas have become vital city development areas. However, the lack of adequate integration between transportation and land use can be seen in many HSR station areas. Evaluating and classifying HSR stations from this perspective can provide a valuable reference for planning, management, and investment in HSR station areas. In previous studies, the node?place model used for evaluation and classification could not assess intracity accessibility. This study introduces the intracity accessibility index and proposes an extended node?place?connectivity (NPC) model and application framework to evaluate and categorize HSR stations in the Chengdu?Chongqing urban agglomeration in China. The original node?place model and the NPC model were compared and analyzed. The results showed that the HSR stations in the Chengdu?Chongqing urban agglomeration are classified into six clusters. The NPC model could identify clusters of stations with poor intracity accessibility and analyze the relationship between intracity accessibility, intercity accessibility, and the place of HSR stations. This facilitates a more precise and comprehensive evaluation and classification. In addition, a distinct spatial differentiation is evident in the node, place, and connectivity performance of HSR stations in the Chengdu?Chongqing urban agglomeration. The node, place, and connectivity performance of HSR stations is significantly related to cities? demographics and economy, as well as the stations? location and opening time. An assessment and classification of HSR stations based on the NPC model can provide a more comprehensive basis for HSR station area planning and decision-making. High-speed rail (HSR) stations serve as urban external transportation hubs; their surrounding areas are often vital development areas. This study developed an improved node?place?connectivity model and application framework to evaluate and classify high-speed rail stations in the Chengdu?Chongqing urban agglomeration in China. The model improves upon the original model by identifying station clusters that are poorly connected to their host cities, and the evaluation and classification results are more refined and comprehensive. In addition, the study found that high-speed rail stations in the Chengdu?Chongqing urban agglomeration can be classified into six clusters. The place performance of high-speed rail stations is correlated with intercity and intracity accessibility, the size of the cities? economy and population, and the stations? location and opening time. Upgraded high-speed rail stations have more potential for development into transit-oriented developments than newly built HSR stations. This research result can be used as a theoretical basis for the location selection, investment optimization, and development planning of high-speed rail station areas.