Evaluating Station–City Integration Performance in High-Speed Rail Station Areas: An NPI Model and Case Study in the Yangtze River Delta, China

Abstract

Effective station–city integration is crucial for sustainable development around high-speed rail stations. However, research assessing public preferences regarding the aspects of this integration remains limited. We constructed a performance evaluation model for station–city integration in high-speed rail station areas. By considering the high-speed rail station area in the Yangtze River Delta region as a research object, which is located in the metropolitan cities centered on Shanghai, China, we dissected the five dimensions of population, industry, land use, function, and environment into 15 indicators that flow into the three value objectives of attraction–retention–integration (NPI). Subsequently, we systematically analyzed the performance differentiation characteristics of station–city integration in the Yangtze River Delta region’s high-speed rail station areas by employing a multiple regression model to delve into the influence mechanisms affecting the performance differentiation patterns of station–city integration. Our findings indicate the following. (1) Regarding station–city integration performance grade differentiation, a few high-speed rail station areas in the Yangtze River Delta region exhibit a high-efficiency integration level, whereas more areas fall within the higher and general integration levels. (2) Spatially, the station–city integration performance in high-speed rail station areas within the Yangtze River Delta region exhibits a distinct distribution characterized by “high-grade point-block dependence and low-grade concentrated contiguous patches.” (3) The spatial distribution of the five dimensions of station–city integration performance exhibits significant disparities. (4) Regarding the development types of station–city integration performance advantages, efficient integration of stations and cities represents a multidimensional advantageous development type and higher integration falls into the same category. (5) Station–city integration performance results from the comprehensive effects of four factors: government policy inducement, station energy level attraction, station–city relationship adhesion, and urban energy level promotion. This study advances a systematic framework—encompassing performance measurement, mechanistic inquiry, and strategy formulation—for examining station–city integration in HSR station areas. By integrating the perspective of cyclical cumulative development into the node–place model from urban planning and geographical viewpoints, we articulate a new performance model that clarifies critical influencing factors and mechanisms, thus broadening the theoretical scope of HSR station area research. We believe that the NPI evaluation model can provide valuable insights for guiding the integrated development of high-speed rail station areas and enhancing the quality of urban development.

1. Introduction

As a global leader in high-speed rail network construction, China boasts the world’s largest network and cluster of high-speed rails stations [1]. This network primarily caters to the demands for convenient commuting and rapid access within metropolitan areas and urban agglomerations [2,3]. Concurrently, high-speed rail station areas play a pivotal role in fostering the integrated development of transportation, industry, and urban centers [4,5]. In 2019, the State Council (China) issued the “Outline for the Construction of a Transportation Power,” emphasizing the need to develop high-speed rail and other transportation networks in harmony with urban development principles. This plan includes promoting urban integrity, systematization, and growth; fostering hub economies; and facilitating integrated transportation development. By harnessing the potential of high-speed rail station areas as new spatial nodes connecting rail and urban networks, infrastructure services, industrial economic development, population concentration, and urban spatial optimization can be maximized [5,6]. However, various challenges persist, as highlighted in the 2018 “Guiding Opinions on Promoting the Rational Development and Construction of the Areas around High-speed Rail Stations” issued by the National Development and Reform Commission. These challenges include excessive initial scale, high functional positioning, limited development models, and incomplete supporting facilities within domestic high-speed rail station areas. Therefore, there is a pressing need for innovative approaches to leverage the experience of high-speed rail station areas and other transportation elements to propel the development of cities along routes and linked regions. This process entails prioritizing integrated development, transitioning from a focus solely on speed and scale to one emphasizing utilization efficiency in line with the concepts of high-quality development. Furthermore, it is important to mitigate the negative effects of spatial resource wastage.

Most scholars have examined the integration of high-speed rail station areas with their surrounding cities. Practically, Japan offers valuable insights [7,8], particularly through its well-established station–city integration models such as those seen in Tokyo Station and Shibuya Station’s underground commercial streets. These models effectively integrate rail and supporting facility networks, enhancing station area space utilization efficiency and functional diversity [8]. Korean Train eXpress (KTX) station vitality depends on interactions with existing urbanized areas, and those located in urban peripheries typically lack this. Station proximity to central business districts is an important consideration for locating future KTX stations in either mid-size cities or suburban areas, in order to maximize the economic impacts of KTX services [9]. Similarly, cities such as Lille and Amsterdam tailor high-speed rail station area development to urban spatial trends and demands, focusing on strategic location, development positioning, functional formats, and service quality [10]. Today the Spanish HSR network has been operating for over 25 years and has more than five lines and 3240 km. The decision to establish new HSR commuting services should be based on prioritization methodologies according to targets linked to promoting employment. As mentioned earlier, Spain has one of the highest unemployment rates in Europe, and HSR could help reduce regional disparities and generate a more dynamic labor market [11,12]. However, China’s high-speed rail station construction faces challenges such as government emphasis on non-urban hub station planning, considerable distances between stations and cities, prolonged integration costs, shallow resident understanding of station–city integration concepts, and inadequate security systems [13,14]. To address these challenges, efforts must prioritize enhancing land intensification, transportation integration [10], industrial agglomeration, and functional diversity within high-speed rail station areas to meet the multifaceted service needs of urban development [15]. Specifically, high-speed rail station areas in megacities and large cities are often strategically positioned at the junctions between cities within an urban agglomeration. Whether located in peripheral, suburban, or central zones, these stations impact the urban center of gravity [16]. Consequently, they act as key vehicles for refining the internal structure of urban agglomerations and amplifying their external influence and spatial reach. This dynamic fosters the spillover of information and technology from central cities to surrounding areas, stimulating interconnected regional development [17,18].

Theoretical perspectives such as Schütz et al.’s “three development areas” circle structure model [19,20] and Bertolini’s node–place theoretical model [21] offer frameworks for understanding high-speed rail station area dynamics. These models underscore the need to balance transportation node value with urban functional value to achieve coordinated development [22]. Scholars have predominantly drawn upon these theories to analyze high-speed rail station area impacts [23,24] on regional urban systems [25] and urban space development [26,27]. Such areas are recognized for their combined node and site values [28], where development processes must reconcile traffic and urban functional spaces to foster continued growth [22]. Enhanced node value leads to improved accessibility, thereby drawing urban populations and businesses to converge in high-speed rail station areas [25], consequently elevating place value [29]. HSR integration requires improvement in three areas, namely infrastructure supply, connectivity service, and passenger experience [23]. Thus, as urban space construction advances, it compels further improvements in service functions, thereby perpetuating the high-speed rail station area’s role in driving urban space development through the continuous interaction between traffic and urban functions.

In summary, current studies have highlighted the significant impact of integrating high-speed rail station areas with their surrounding cities. However, existing research predominantly focuses on individual or a few representative high-speed rail station areas, leading to qualitative evaluations as a result of the absence of standardized evaluation frameworks and models. Additionally, building upon the seminal node–place model (NPM) by Bertolini (1999) [22], which evaluates development potential through the interplay between a station’s node (transport) and place (urban) qualities, this paper augments the classic two-dimensional framework with a third dimension. This extension marks a significant evolution: early NPM research primarily emphasized walkability and design, whereas our model has broadened the analytical scope to encompass factors such as population, functionality, ecology, and vibrancy. The utility of this enhanced framework has prompted its wide application in the literature. Notably, the Yangtze River Delta region boasts a well-established high-speed rail network [30] characterized by extensive construction, high density, and broad coverage [31]. In 2020, General Secretary Xi Jinping emphasized the importance of “integration” and “high quality” for guiding and advancing the integrated development strategy of the Yangtze River Delta, underscoring their pivotal role in the region’s development theme. Consequently, fostering station cities within the Yangtze River Delta’s high-speed rail station areas holds significant implications for leveraging the role of these areas in driving urban and regional development, unleashing the “positive energy” inherent to the efficient utilization of urban and regional space and ultimately promoting high-quality development across the region. To this end, we constructed a comprehensive evaluation system consisting of 15 specific indicators across five dimensions of population, industry, land use, function, and environment to assess the station–city integration performance of high-speed rail station areas in the Yangtze River Delta region quantitatively. We also conducted a detailed analysis of performance differentiation characteristics based on grade, space, dimension, and type. Building upon this foundation, a multiple regression model incorporating 10 influencing factors for station–city relationships, high-speed rail station energy levels, urban energy levels, and government policy was developed to elucidate the impact and mechanisms of these factors. We aim to provide valuable insights for guiding the integrated development of high-speed rail station areas and enhancing the quality of urban development.

2. Materials and Methods

2.1. Overview of the Study Area

The “Outline of the Yangtze River Delta Regional Integration Development Plan” emphasizes the pivotal role of 27 central cities: Shanghai, Nanjing, Wuxi, Changzhou, Suzhou, Nantong, Yangzhou, Zhenjiang, Yancheng, and Taizhou in Jiangsu Province; Hangzhou, Ningbo, Wenzhou, Huzhou, Jiaxing, Shaoxing, Jinhua, Zhoushan, and Taizhou in Zhejiang Province; and Hefei, Wuhu, Ma’anshan, Tongling, Anqing, Chuzhou, Chizhou, and Xuancheng in Anhui Province. These cities, which cover an area of 225,000 km², face the critical task of catalyzing high-quality development across the Yangtze River Delta region. This study focused on the high-speed rail station areas of the central city within the Yangtze River Delta region. Recognizing significant disparities among stations within the same city due to varying distances, volumes, completion times, and development scales of high-speed rail lines [32], we endeavored to eliminate the influence of specific high-speed rail factors such as line variations and functional differences. Consequently, our investigation comprehensively considered control factors such as high-speed rail trunk lines, new city planning, and passenger transport operational nature. The node cities within the Yangtze River Delta region, which are typified by Shanghai, Nanjing, Hangzhou, and Hefei, primarily rely on trunk lines and intercity railways within four main high-speed railway corridors, namely the Beijing–Shanghai, Coastal, Shanghai–Kunming, and Yangtze River corridors, within the “Eight Vertical and Eight Horizontal” network. These corridors expedite the flow and exchange of people, logistics, and information among cities, propelling social, economic, cultural, and ecological development while facilitating population and economic redistribution (Figure 1).

Furthermore, in 2018, the state issued the “Guiding Opinions on Promoting the Rational Development and Construction of the Areas Around High-speed Rail Stations,” emphasizing the importance of focusing on development within a 2 km radius around new high-speed rail stations in the initial stages, while also allowing for appropriate control and reservation of long-term development space [32,33]. Choosing a unified observation range facilitates better control over variables [34]. In summary, this study selects 26 high-speed rail stations within the core urban agglomeration of the Yangtze River Delta region—including but not limited to Shanghai Hongqiao, Nanjing South, Suzhou North, Hangzhou East, Hefei South, and Jiaxing South stations—as its research subjects. The study area for each station is defined as the surrounding area within a 3 km radius buffer (Figure 1). It is noteworthy that Zhoushan City is excluded from the analysis as it had not yet launched HSR services during the research period.

2.2. Study Methods

2.2.1. Construction of a Performance Evaluation Model for Station–City Integration in High-Speed Rail Station Areas

The development of high-speed rail station areas introduces a novel and competitive spatial framework for China’s large urban agglomerations, establishing a new structural foundation for future city growth [23,29]. Station–city integration primarily hinges on the symbiotic relationship between high-speed rail station areas and urban centers [30]. Serving as hubs for transportation, industry, and urban life, these areas possess dual significance as both nodes and places. Striking a balance between their node–place functions and service functions fosters their evolution into vibrant urban cores [29]. In 2018, the National Development and Reform Commission (China) issued the “Guiding Opinions on Promoting the Rational Development and Construction of Areas Around High-speed Rail Stations”, advocating for high-speed rail station areas to align with the principles of new urbanization by prioritizing the goal of ensuring that people are “attracted, retained, and integrated” (Figure 2).

High-speed rail station area development can evolve into pivotal urban functional centers that can help residents “live well” [31,32,33]. This notion of “living well” primarily arises from the cyclic accumulation of population, industry, land, and other elements within high-speed rail station areas (Figure 2).

This ongoing process fosters interactions between the station area’s nodes and the value of place functions [31], improving transportation convenience, public service capacity, and overall livability within the area, including transportation connectivity and the development of public service systems [34,35,36,37]. We focus on the perspective of achieving the three value objectives of “attraction, retention, and integration” within high-speed rail station areas and we identified population (λ_a), industry (λ_b), land use (λ_c), function (λ_d), and environment (λ_e) as the five principal dimensions for analyzing station–city integration performance (

Table 1. Performance evaluation system for station–city integration in high-speed rail station areas.

Target Level	System Level	Indicator Level	Weighting	Access to and Calculation of Indicators
Station–City Integration Performance Evaluation	Population (λa)	Customer Attraction (μa-1)	+0.159	Annual passenger traffic at high-speed rail stations [38].
		Number of inhabitants (μa-2)	+0.078	Total number of households in the residential area of the high-speed rail station area [38].
		Residential density (μa-3)	+0.083	Number of dwellings within the high-speed rail station area/Total site area of the high-speed rail station area [32,38].
	Industrial (λb)	Density of businesses (μb-1)	+0.013	Number of businesses in the high-speed rail station area/total land area of the high-speed rail station area.
		Industrial diversity (μb-2)	+0.069	$Divi=1-{\displaystyle \sum _{n=1}^{N_i}{S}_{i,n}^2}$ Ni is the number of industry types in region i, Si,n is the ratio of persons employed in industry category n in region i among all persons employed in the region. When all employed persons belong to the same category, the index achieves a minimum value. The greater the HHI, the greater the degree of diversity [34].
		Industry circle agglomeration (μb-3)	+0.064	$z_n={\displaystyle \frac{X_n}{{\displaystyle \sum _{n=1}^3{X}_n}}}/{\displaystyle \frac{S_n}{{\displaystyle \sum _{n=1}^3{S}_n}}}$, $Z_n={\displaystyle \sum _{n=1}^3{z}_n}$ Xn denotes the number of firms in the nth buffer; the 3 km perimeter around the high-speed rail station i is taken as the research object; the high-speed rail station area is divided into three circles according to the radius of the high-speed rail station which is less than 1000 m (the first buffer), 1000 m~2000 m (the second buffer), and 2000 m~3000 m (the third buffer); Sn denotes the area of the nth buffer; and Zn represents the circle agglomeration structure of industries through the sum of industry indices in the three buffer zones [32].
	Land use (λc)	Density of road network (μc-1)	+0.049	$D={L_i/M}_i$ D is road network density (km/km2), L is the length of the road (km) of area i, and M is the area of the high-speed rail station area i (km2) [35].
		Station site mix (μc-2)	+0.017	$H=-{\displaystyle \sum _{i=1}^n{p}_i}\ln p_i$ H indicates the functional mixture of land, pi is the ratio of the area of functional land use in category i to the total land use in the high-speed rail station area, and n is the total number of land use categories within the station area [33,38].
		Station accessibility (μc-3)	+0.077	$NQPDA(x)={\displaystyle \sum _{y\in Rx}\frac{p(y)}{d_{\theta }(x,y)}}$ NQPDA(x) is the accessibility and p(y) is the weight of node y within the search radius R. In continuous space analysis, p(y) is determined in proportion to the radius and the length of the section, p(y) ∈ [0, 1]. In discrete space analysis, p(y) takes on a value of zero or one. dθ (x, y) is the shortest topological distance from node x to node y [39,40,41].
	Functional (λd)	Public transportation links (μd-1)	+0.055	$A={\displaystyle \sum (a_n\times w)}$ an is the number of trips of the nth transportation connection mode, and w represents weight [4].
		Public service coverage (μd-2)	+0.087	Number of public services accessible within 5 min (walking/transit) of each residential subdivision within the high-speed rail station area [10].
		Cost of living (μd-3)	−0.012	Ratio of average residential price of used versus new homes to average household income in the high-speed rail station area [42]
	Environmental (λe)	Management environment(μe-1)	+0.012	Assigning values in accordance with the planning positioning of the high-speed rail station area in the territorial spatial pattern of the “14th Five-Year Plan” of each city (where the core area is assigned a value of one, the development pole and sub-nucleus is assigned a value of 0.6, the non-core functional area is assigned a value of 0.4, and the non-functional area is assigned a value of 0.2) [15].
		Ecological environment(μe-2)	+0.146	Analyzing the supply and demand of ecological facilities in the high-speed rail station area using a two-step search method (walking) with 500 m as the search threshold [43].
		Cultural environment (μe-3)	+0.079	Analyzing the supply and demand of cultural facilities in the high-speed rail station area using a two-step search method (walking) with 500 m as the search threshold [44,45].

Note: “+” indicates a positive indicator and “−” indicates a negative indicator.

). The entropy value method was employed to assign weights to each index and the TOPSIS evaluation method was applied to measure station–city integration performance within the Yangtze River Delta region quantitatively.

This study employs a three-stage methodology to assess station–city integration performance in high-speed rail station areas, ensuring scientific rigor and reproducibility. Step 1: Indicator Standardization. Raw indicators were standardized using the min–max normalization method to construct a judgment matrix, eliminating disparities in measurement units, scales, and directional effects (positive or negative). Indicators were classified into two categories: Positive indicators, where higher values reflect better performance, including passenger flow, residential population, residential density, industrial agglomeration, industrial diversity, enterprise density, transportation accessibility, road network density, land use mix, public transport connectivity, public service coverage, and management, cultural, and ecological environments. Negative indicators, where higher values denote poorer performance, such as residential cost. Step 2: Entropy-Based Weighting. The entropy weight method was applied to determine indicator weights. This objective approach assigns greater weight to indicators exhibiting higher variability across samples, as greater divergence implies greater informational content and importance in the evaluation. Step 3: Entropy-Weighted TOPSIS Evaluation. Performance scores were calculated using the entropy-weighted TOPSIS method. This technique measures the relative closeness of each station area’s indicator profile to the ideal solution (optimal performance) and its distance from the negative-ideal solution (poorest performance) based on Euclidean distance. The results provide a precise quantification of station–city integration performance for HSR stations in the Yangtze River Delta region.

2.2.2. Multiple Regression Model of Station–City Integration Performance in High-Speed Rail Station Areas

Influenced by the presence of high-speed rail stations, the integration between stations and surrounding cities and regions is shaped by the confluence of advantages stemming from human mobility, industrial activity, and information exchange facilitated by the station, coupled with the locational, economic, and policy advantages offered by the city and region hosting a station. Drawing upon findings from existing research elucidating the interplay between stations and cities [46,47,48,49,50], the energy level of high-speed rail stations [51,52,53,54,55,56], city energy levels [26], and government policies [57], we constructed a multiple regression model to analyze the mechanisms influencing the spatial differentiation of station–city integration performance within high-speed rail station areas (

Table 2. Description of variables and expected impacts.

Primary Variables	Binary Variables	Explanation of Variables and Assignment of Values	Expected Impact
Station–city relationship	High-speed rail station location (LOCATION)	Straight-line distance between the high-speed rail station and city center/built-up area of the city [54].	+
	External accessibility (ACCESSIBILITY)	Measuring the external accessibility of 26 cities in the Yangtze River Delta Region under the conditions of high-speed railways using the time-cost raster methodology [3].	+
Site energy level	Length of commissioning (TIMES)	Average daily number of shuttle buses sent from high-speed rail stations (Times/day).	+
	High-speed rail station grades (LEVEL_STATION)	Hub level variables are compiled from a combination of the “Approved Measures for the Grade of National Railway Stations,” “Medium- and Long-Term Plan for the National Railway (2016–2030),” and almanacs of various transportation bureaus, as well as the passenger flow of the stations, frequency of departures, and number of platform surfaces at the stations.	+
	High-speed rail platform size (SIZE)	Number of platform surfaces and strands completed at high-speed rail stations.	+
Urban energy level	City level (LEVEL_CITY)	According to the State Council’s Circular on the Adjustment of the Standards for the Division of City Scale, cities are divided into five classes, namely super-cities, mega-cities, large cities, medium-sized cities, and small cities, with values of 5, 4, 3, 2, and 1, respectively.	+
	Urbanization level (RATE)	Urban population/total population (%).	+
	Percentage of tertiary sector (PROPORTION)	Value added of the tertiary sector as a percentage of gross urban product (%).	+
	GDP	GDP of 26 Cities in the Yangtze River Delta Region in 2022 (Billion Yuan).	+
Government policy	Diversity of station area policies (DIVERSITY)	Station area policy multiplicity = positioning class factor + planning function factor [15] + coefficient of complexity of policy-oriented industries + coefficient of major government investment projects.	+

Note: “+” indicates a positive indicator.

). The model is defined as follows:

$$\begin{array}{c}SCI=\alpha +LOCATION{\beta }_1+ACCESSIBILITY{\beta }_2+TIMES{\beta }_3\\ +LEVEL\_STATION{\beta }_4+\dots +DIVERSITY{\beta }_{10}+\epsilon \end{array}$$

(1)

where SCI represents the station–city integration performance, α denotes the intercept term, and β₁, β₂, …, β₁₀ represent the explanatory variables to be examined. These variables mainly encompass the location of the high-speed rail station, its accessibility from outside the city, operation time, station level, platform size, city level, urbanization level, proportion of tertiary production, GDP, and policy diversity. Additionally, ε represents error terms.

The influence of the ten independent factors on the Station–City Integration Index (SCI) is analyzed through a multiple regression model. These factors, categorized into four dimensions, directly affect SCI through the following causal pathways: Station–City Relationship: Measured by station location and accessibility. Proximity to the city center determines the development mode (e.g., urban renewal or new town development), directly shaping integration potential. Accessibility, calculated via the time-cost raster method, determines the area’s attractiveness to passengers and enterprises, directly influencing its evolution into a functional agglomeration center. Station Energy Level: Represented by operation time, station grade, and platform size. These metrics directly quantify a station’s capacity to attract passenger and economic flows, which in turn drives the agglomeration and development intensity of the station area. City Energy Level: Assessed by city administrative level, urbanization rate, proportion of tertiary industry, and GDP. A higher city energy level accelerates factor agglomeration and urban spatial expansion through the “siphoning effect” of the HSR station, providing direct momentum for station–area integration. Government Policy: Captured by policy diversity. A greater diversity of pre-development policies directly facilitates integration by mobilizing managerial and capital resources to attract subsequent urban and industrial activities to the station area. This model explicitly tests the strength and mechanisms through which these ten factors directly impact the SCI value in the Yangtze River Delta region.

2.3. Data Sources and Processing

The graphical data utilized in this study primarily originated from the relevant vector administrative boundaries of the Yangtze River Delta region, which were extracted from the 1:4 million Chinese basic geographic information dataset provided by the National Basic Geographic Information Center (China). The precise coordinates of high-speed rail stations were primarily determined using the BD09 coordinate picking system, followed by input and adjustment of coordinates using ArcGIS to ensure accuracy in location determination. Additionally, data from the “1:4 million basic elements version” and the “1:4 million highway traffic version” maps provided by the State Bureau of Surveying and Mapping were incorporated to depict water systems and road traffic, including high-speed rail lines and urban traffic lines, within the Yangtze River Delta region.

Regarding enterprise data, the database of registered industrial and commercial enterprises in 26 major high-speed rail station areas across 26 cities as of October of 2023 was primarily sourced from the national enterprise credit information system (https://www.gsxt.gov.cn/ accessed on 1 October 2023) and enterprise verification platform (https://www.qcc.com/ accessed on 1 October 2023). The databases categorize large, medium, and small industrial and commercial enterprises according to the national economic industry classification (GB/T 4754-2017) [58]. Analysis revealed that the industry types within the enterprise databases align with the middle categories of the national economic industry classification. Additionally, enterprise address information was integrated for coordinate positioning purposes.

Regarding other data sources, we mainly utilized point of interest data and enterprise locations, which were predominantly obtained through analysis and operations on the AutoNavi Map basic data platform. Population, housing, housing prices, and related data were primarily gathered from real estate trading platforms such as Anjuke and Lianjia. Land use data, including land use types and built-up areas, were primarily obtained through ENVI Landsat 4-5 imagery, while proximity data derived from processed remote sensing images such as 8TM and computed using the spatial syntax software SDNA1.0 were primarily sourced from the statistical yearbooks of provinces and cities from 2008 to 2023, the China Urban Statistical Yearbook, and China Urban Construction Statistical Yearbook.

3. Results

3.1. Characteristics of Grade Differentiation

According to the relative proximity value of the station–city integration performance evaluation, Yangtze River Delta high-speed rail station areas were divided into four levels (Figure 3) using the natural discontinuity point method: highly efficient integration (0.620~0.663), more efficient integration (0.353~0.619), general integration (0.187~0.352), and inefficient integration (0.081~0.186).

Few high-speed rail station areas exhibited high-efficiency integration, whereas more station areas were assigned to the higher- and general-integration groups. The count of high-speed rail station areas across the levels of high efficiency, more efficiency, and general integration exhibits a progressive increase from high to low. Additionally, a significant number of high-speed rail station areas were observed in the inefficient integration stage.

3.2. Spatial Differentiation Characteristics

The 26 cities exhibiting high station–city integration performance in the Yangtze River Delta region are predominantly major “polar core” cities such as Ningbo, Hangzhou, Shanghai, Nanjing, and Hefei (Figure 3). Notably, the station–city integration performance of the high-speed rail station areas in these cities exhibits distinct distribution characteristics of “high-grade point and block dependence, and low-grade concentration and contiguous areas.” Specifically, high-performance areas such as Ningbo Station and Hangzhou East Railway Station are dispersed in a point-like pattern, as are stations at the next level down such as Shanghai Hongqiao Station and Jinhua Station. In contrast, stations such as Hefei South Railway Station and Wuhu Station, as well as Nanjing South Railway Station and Zhenjiang Station, exhibit a block-like distribution. Areas with average to low performance levels such as Ma’anshan East Station, Anqing Station, and Suzhou North Railway Station exhibit a concentrated and contiguous distribution pattern (Figure 3a).

From a spatial perspective, the overall station–city integration performance of high-speed rail station areas in the Yangtze River Delta region exhibits a trend of “high in the east and west, low in the middle,” and “high in the south and low in the north” (Figure 3b).

3.3. Dimensional Differentiation Characteristics

Analyzing the spatial differentiation characteristics of station–city integration (Figure 4) reveals several patterns. (1) Population integration exhibits a high-level “sporadic distribution” characterized by both high-level “clump dependence” and a general-to-low-efficiency level “concentrated and contiguous” distribution pattern. (2) The spatial distribution of industrial integration performance reveals distinct patterns of “agglomeration” for the top two grades, a clustered distribution for the general grade, and sporadic dispersion for the lowest grade (Figure 4b). (3) The spatial distribution of land use integration dimensions exhibits a point-block distribution for the higher two grades with a spatial pattern of sporadic scattering of the lower two grades (Figure 4c). (4) The spatial distribution of functional fusion dimensions as a whole exhibits a pattern of dot-block interweaving for the top two levels, alongside concentrated and contiguous patches for the bottom two levels (Figure 4d). (5) The spatial distribution of environmental fusion dimensions exhibits a point-block distribution pattern among high-speed rail station areas with the top two integration grades, a sporadic pattern among stations with general integration performance, and a “strip-like” distribution among stations with low integration performance (Figure 4e).

3.4. Type Differentiation Characteristics

To quantify the multidimensional spatial multifunctionality of high-speed rail station areas, we employed a dominant advantage combination model encompassing five dimensions of population, industry, land use, function, and environment. This model was constructed based on a spatial evaluation and classification system for station–city integration performance. Initial identification of spatial functions across dimensions such as function and environment enabled the quantitative calculation of 15 specific indicators. Each dimension’s value was then summarized and the dominant dimension advantage type of each high-speed rail station area was identified by comparing the magnitudes of the dimension values. Observations revealed four development advantage types for high-speed rail station areas in the Yangtze River Delta region: multidimensional advantage, two-dimensional advantage, single-dimensional advantage, and no advantage. These types form a matrix alongside the four station–city integration performance levels described in the previous subsections (Figure 5).

First, Ningbo Railway Station and Hangzhou East Railway Station exhibit multidimensional advantageous development for station–city integration. Ningbo Railway Station showcases efficient integration across the dimensions of population, land use, environment, and function, with the industrial dimension achieving the next level of integration.

Second, the six high-speed rail station areas of Zhenjiang Station, Nanjing South Railway Station, Shanghai Hongqiao Station, Jinhua Station, Hefei South Railway Station, and Wuhu Station exhibit a high level of station–city integration with multidimensional advantageous development.

Third, six high-speed rail station areas, namely Suzhou North Railway Station, Anqing Station, Ma’anshan East Railway Station, Yancheng Station, Xuancheng Station, and Wenzhou South Railway Station, exhibit two-dimensional advantageous development for station–city integration. In contrast, Wuxi East Railway Station, Changzhou North Railway Station, and Chizhou Station exhibit only single-dimensional advantageous development.

Fourth, among the high-speed rail station areas experiencing inefficient station–city integration development, only Chuzhou Station exhibits one-dimensional advantageous integrated development. In contrast, eight high-speed rail station areas, namely Shaoxing North Railway Station, Jiaxing South Railway Station, Nantong West Railway Station, Yangzhou East Railway Station, Tongling Station, Taizhou Railway Station, Huzhou Railway Station, and Taizhou West Railway Station, exhibit non-advantageous station–city development.

4. Dominant Factors and Influence Mechanisms

4.1. Dominant Factors

To analyze the driving mechanisms behind the performance differentiation of station–city integration in high-speed rail station areas within the Yangtze River Delta region comprehensively, we constructed a multiple regression model. Emphasis was placed on addressing multicollinearity issues in the selection of independent variables, ensuring that the maximum variance inflation factor (VIF) of the constructed model is less than 10 (

Table 3. Results of multiple regression analysis of station–city integration performance of high-speed rail station areas in the Yangtze River Delta region.

Independent Variable	Non-Standardized Coefficient		Standardized Coefficient	t	p	VIF
	B	Standard Error	Beta
Constant	−0.823	0.629	-	−1.308	0.209	-
Station–city relationship
High-speed rail station location	0.038 **	0.007	0.718	5.057	0.000 **	2.250
External accessibility	1.629	1.519	0.118	1.072	0.300	1.355
Site energy level
Length of commissioning	−0.000	0.012	−0.007	−0.030	0.977	6.695
High-speed rail station grades	0.135 *	0.061	0.600	2.212	0.042 *	8.194
High-speed rail platform size	−0.000	0.004	−0.019	-0.072	0.943	7.554
Urban energy level
GDP	−0.018	0.069	−0.060	−0.258	0.799	6.086
City level	0.001 *	0.000	−0.421	−2.844	0.012 *	2.445
Urbanization level	−0.046	0.064	−0.087	−0.722	0.481	1.632
Government policy
Diversity of station area policies	0.271 **	0.083	0.379	3.270	0.005 **	1.503
R2	0.857
Adjustment R2	0.776
F	F (9, 16) = 10.619, p = 0.000

Note: *, ** indicate 5%, 1% significance levels, respectively.

). This analysis confirmed the absence of significant multicollinearity problems among the model’s independent variables. To ensure the robustness of the findings, we conducted a series of checks by substituting key variables and incorporating additional control variables into the regression model. The results confirm that the influence of core factors—including high-speed rail station location, station grade, city level, and the diversity of station area policies—on the Station–City Integration Index remains statistically significant and stable in both the direction and magnitude of their coefficients.

Notably, factors such as the station–city location index, station level, city level, and policy diversity exhibit a substantial positive impact on the performance evaluation value. In contrast, variables such as the external accessibility of the high-speed rail station, operation time, platform size, GDP, and urbanization level exhibit no significant influence (Table 3), suggesting their limited impact. The regression coefficients of the station–city location index, station level, city level, and policy diversity were calculated as 0.038, 0.135, 0.001, and 0.271, respectively. This indicates that for every one-unit increase in the station–city location index, station grade, city level, and policy diversity, the performance values of station–city integration in high-speed rail station areas increase by 0.038, 0.135, 0.001, and 0.271, respectively.

4.2. Influence Mechanisms

In this subsection, we provide an in-depth examination of how each influence factor contributes to the performance of station–city integration within high-speed rail station areas (Figure 6).

(1) Diverse and tailored government policies serve as the primary catalyst for fostering integration between high-speed rail station areas and cities. The multiple regression model indicates a substantial influence of policy pluralism (0.271), suggesting that a more favorable government policy environment within high-speed rail station areas correlates with enhanced station–city integration.

(2) Enhancing the energy level of high-speed rail stations amplifies the allure of station–city integration within high-speed rail station areas. The transportation capacity of stations is fundamental for their emergence as pivotal functional zones within cities [59], exerting a significant impact on station–city integration levels across the Yangtze River Delta region. High-speed rail stations occupying elevated positions within the rail network facilitate the swift circulation of economic elements such as capital, technology, and labor between station areas [59,60], thereby reinforcing connectivity among urban high-speed rail station areas and propelling them to serve as key nodes in regional city networks [61]. As the station-level network system continues to evolve, high-speed rail station areas in small- and medium-sized cities are poised to enhance their capacity to attract population, industry, and other factors within the Yangtze River Delta region.

(3) The symbiotic relationship between a station and city serves as the linchpin for station–city integration within high-speed rail station areas. Results from the multiple regression model reveal that the influence of station area location distance on station–city integration in high-speed rail station areas is 0.038, suggesting that a closer proximity of high-speed rail station areas to a city’s central area correlates with higher degrees of station–city integration.

(4) The enhancement in urban vitality serves as an impetus for the integration between high-speed rail station areas and cities. The multiple regression model results indicate that the higher the urban tier of a high-speed rail station area, the greater the degree of station–city integration, with an influence factor of 0.001. This suggests that high-tier cities possess ample labor, capital, infrastructure, and other resources, thereby exerting a stronger impetus for population and enterprise migration to high-speed rail station areas (Figure 6).

5. Conclusions and Discussion

(1) Regarding station–city integration grade differentiation, there are fewer high-speed rail station areas in the Yangtze River Delta region at a highly efficient integration level, with more areas at the next two integration levels. There are also several high-speed rail station areas in the inefficient integration stage. (2) Regarding the spatial differentiation characteristics of station–city integration performance, Yangtze River Delta high-speed railway station areas exhibit a distinct distribution pattern of “high-grade point-block dependence and low-grade concentrated contiguous patches” along the Shanghai-Nanjing and Ning’an high-speed rail corridors. (3) Considering the spatial differentiation characteristics of station–city integration performance, significant differences exist in the spatial distributions across the five dimensions considered in this study. (4) Concerning the development types of station–city integration performance advantages, highly efficient integrated high-speed rail station areas exhibit multidimensional advantageous station–city integrated development. Areas with the next level of integration also exhibit multidimensional advantages, whereas general integrated areas lean toward two-dimensional and single-dimensional advantageous station–city integrated development. Inefficient integrated areas mostly align with non-advantageous station–city integrated development, excluding Chuzhou station.

Examining the influence mechanisms of station–city integration performance revealed the following: (1) Diversified and differentiated government policies serve as the driving force behind the integration of high-speed rail station areas and cities. (2) Enhancing the station level augments the appeal of station–city integration in high-speed rail station areas, with transportation capacity represented by the station level serving as a prerequisite for an area to become a significant functional zone within a city. (3) Close station–city relationships act as an adhesive force in the integration of high-speed rail station areas, effectively reducing traffic costs and overcoming time and space barriers between station areas and cities. (4) Improvement in the urban energy level serves as the driving force behind station–city integration in high-speed rail station areas by attracting a broader spectrum of people and capital aggregation.

Our study holds significant practical implications for advancing the development and construction of high-speed rail station areas, bolstering their pivotal role in optimizing urban spatial structure, propelling urban population and economic growth, and reshaping the pattern of urban agglomeration to achieve integrated development between urban areas and high-speed rail station areas. A comparison with international case studies reveals both convergent validations and the distinct theoretical contribution of this research. Practically, the emphasis on functional diversity and spatial efficiency in Japanese models and the critical importance of linkage to urban cores in the Korean KTX experience strongly corroborate our findings that integrated infrastructure and strategic location are fundamental to avoiding underutilized “ghost towns.” Similarly, the tailored development approaches in Lille and Amsterdam align with our conclusion that successful integration requires context-specific policies. However, our study moves beyond these valuable but context-specific practical insights by offering a unified theoretical and methodological framework. While the Spanish case highlights overarching goals like employment, our research provides a scalable model to operationalize such goals. The proposed “attraction–retention–integration” framework, with its 15-indicator system, systematically delineates the developmental trajectory of station areas. This transcends descriptive case studies by offering a diagnostic tool to quantify performance, analyze influencing mechanisms, and inform targeted policy interventions—thereby enriching the node–place theory and providing a generalizable approach for planning sustainable station–city symbiosis in China and beyond.

The theoretical framework and corresponding metric system developed in this study provide a novel, process-oriented lens and a replicable method for quantifying station–city integration performance, moving beyond static assessments. However, the findings and their interpretation must be considered within the context of certain limitations. Primarily, the reliance on cross-sectional data, while suitable for an initial spatial analysis, inherently limits causal inference and cannot capture the temporal dynamics of integration. Furthermore, the “attraction–retention–integration” framework, though designed to distill core developmental mechanisms, may simplify complex urban dynamics, and the absence of stakeholder perspectives limits the granularity of our analysis.

In light of these considerations, future research will pursue several directions. First, we will expand the geographical scope beyond the Yangtze River Delta region to include station areas within other major metropolitan contexts to test the framework’s generalizability. Second, a longitudinal research design is essential to trace causal pathways and understand the evolution of integration forces over time. Third, future work will incorporate primary data, including stakeholder interviews and surveys, to validate the metrics and enrich the model with qualitative insights. Finally, a critical avenue is the deeper integration of macro-level factors, such as long-term governance mechanisms and environmental sustainability metrics, to strengthen the framework’s connection to broader TOD and high-speed rail debates and its utility for guiding sustainable urban policy.