Urban Renewal Strategy Guided by Rail Transit Development Based on the “Node–Place–Revenue” Model: Case Study of Shenyang Metro Line 1

Abstract

Under the backdrop of urban renewal, harmonizing transit-oriented development (TOD) with urban renewal to maximize rail value has emerged as a critical focus in contemporary planning. Based on this, this paper proposes the node–place–revenue (NPR) model, which constructs evaluation indexes from the three dimensions of the node, place, and revenue. It determines the weights of each index by using expert scoring and the Analytic Hierarchy Process (AHP). Taking Shenyang Metro Line 1 as an example, the study first used the model to measure the node value, place value, and revenue value of each sample TOD station area. Secondly, K-means clustering analysis was used to form a spatial classification of five station areas. Finally, this paper proposes one differentiated urban renewal strategy for each type of station area. It is found that (1) the NPR model classifies stations into five categories: stress and high revenue, balanced, unbalanced node, unbalanced place, and dependence and low revenue and (2) the differentiated urban renewal strategies for each type of station area can be explored in terms of precise decongestion, node upgrading, function expansion, endogenous optimization, and infill quality improvement. This paper examines the economic driving effect of Shenyang Metro Line 1 stations on the renewal of the surrounding areas from the perspective of the economic balance of payments, providing a new reference for Shenyang-rail-transit-guided urban renewal work.

1. Introduction

China’s rapid urbanization has resulted in tight urban land resources and an urgent need for urban renewal efforts to further achieve high-quality urban development. In 2021, the “Implementation of Urban Renewal Initiatives” was included in China’s Government Work Report and the 14th Five-Year Plan for the first time, marking a pivotal shift from an incremental to a stock-based urban development era. Urban renewal has thus become a central challenge in China’s urban transformation agenda [1].

In the context of urban renewal, leveraging rail transit construction to achieve efficient land use and optimize urban spatial structure has emerged as a key strategy for significant cities [2]. Currently, China is experiencing a period of rapid development in rail transit. By the end of 2024, 58 cities in mainland China will have opened urban rail transit lines, totaling 361 rail transit lines. Capitalizing on this momentum is crucial for guiding transit-oriented development (TOD), which can accelerate urban renewal and promote compact, sustainable urban growth [3].

In recent years, some cities in China have successfully integrated rail transit with the development of surrounding land, achieving notable results [4]. Since 2010, cities such as Shenzhen, Shanghai, Guangzhou, Beijing, Chengdu, Hangzhou, Nanjing, Xiamen, and others have issued relevant implementing regulations, management measures, opinions, rules, and other restrictions. In 2015, the Chinese Ministry of Housing and Urban–Rural Development launched the “Guidelines for Planning and Design of Areas along the Urban Rail Transit”, focusing on the core of railway stations to build an intensive, efficient, humanized urban environment and activity space [5]. In 2021, the Urban Planning Society of China issued the group standard “Guidelines for Spatial Planning and Design of Facilities Around Urban Rail Transit Stations”, which puts forward corresponding optimized design guidance and control requirements for the transport connection and auxiliary facilities, distribution, and public space in the areas surrounding rail transit stations. Transit-oriented urban renewal has thus become a common development strategy for an increasing number of cities [6].

Although China’s design guidelines and standards for rail transit station areas are relatively complete at this stage, and the relevant integration practices have also achieved specific results, there is still a significant gap between China’s rail-transit-guided urban regeneration and that of advanced international cities. On the one hand, Tokyo and other international cases have demonstrated that successful renewal necessitates the establishment of a land value capture mechanism to internalize the external benefits generated by rail transit, thereby providing the impetus for renewal [7]. In China, the practice of value capture for urban rail transit development is far from mature. Unlike the U.S. urban rail transit system, which receives significant federal government subsidies, the construction and operation of urban rail transit in China typically do not receive any financial support from central and provincial governments [8]. Therefore, Chinese municipal governments have strong economic incentives to adopt viable and effective urban rail transit financing models. On the other hand, Singapore’s experience shows that integrated station development requires a collaborative model of risk-sharing and benefit-sharing among developers, operators, and owners. China’s integrated station development currently lacks institutional innovation in balancing the interests of multiple parties, and these practices mostly rely on the government to take the lead, failing to fully activate the synergistic effect of market capital and community participation [9].

Existing studies have shown that urban renewal guided by rail transit is completed under the background of multiple interests; rail transit has a positive premium effect on the surrounding areas of a station, stimulating urban rail construction and operation enterprises and real estate developers to enter the field of development of rail transit station areas, deriving from the “rail + property”, “rail + community”, and other modes [10]. At the same time, the government can utilize metro property joint development, land asset pricing financing, and other premium recovery policies to policies capture the land and real-estate value-added gains brought about by rail transit, serving as a financial source to support both rail transit construction and operation as well as site area development [11]. This also requires that the process of rail-transit-guided land development or renewal can be achieved to alleviate traffic congestion and improve land-use efficiency while also achieving capital gains with an additional premium. Additionally, it needs to consider the synergistic effect of multiple subjects.

Based on this, this paper tries to consider the transportation conditions, land use conditions, and external economic effects of the station area. The “node–place–revenue”(NPR) model is proposed, and the evaluation indicators are constructed from the three dimensions of the “node–place–revenue” model, and the weights of the indicators are determined by expert scoring and Analytic Hierarchy Process (AHP). Then, we choose Shenyang Metro Line 1 as the research object through the quantitative assessment of the node value, place value, and income value of the station area, using the K-means clustering method to summarize the station type, and then put forward both the urban renewal strategy under different station types as well as the exploration of the synergistic governance mechanism of multiple subjects in the process.

The rest of the paper is organized as follows. Section 2 reviews the previous literature and describes the research methodology. Section 3 analyzes the results of the station clustering model. Section 4 discusses the model station clustering results and proposes a differentiated regeneration strategy. Section 5 summarizes the main findings and recommendations for future research.

2. Materials and Methods

2.1. Previous Literature

TOD was proposed in 1993, aiming to create pedestrian-friendly neighborhoods around public transportation stations, reduce dependence on private cars, and promote sustainable development through high-density, mixed-use development patterns [12]. Since then, domestic and international scholars and academics have proposed implementation guidelines such as 3D principles (density, diversity, design) [13] and 5D principles (increased distance to transit, destination accessibility) [14] and have gradually derived a multidimensional covering transportation, economic, and environmental evaluation system [15]. Early studies focused on physical space design, and with the continued deepening of research and integration of multiple disciplines, scholars began to pay attention to the importance of the coordinated relationship between land use and transportation [16,17]. Lowry established the Land-Use and Transportation Model (LULM) earlier, which considered urban transportation and land use as a system. Handy [18] studied the integrated development planning of both urban land use and transportation systems from the perspective of urban smart growth; Lawrence et al. [19] studied the relationship between urban land use and transportation systems from the perspective of improving the quality of life; Davis et al. [20] addressed the sustainability of both of these from the viewpoint of technology, finance, land use, and transportation system integration.

With urban renewal gradually becoming the direction and goal of most urban development planning, TOD research has begun to expand to the trend of “Transit-Oriented-Renewal” [21], and some studies have explored the interactive relationship between rail transit and urban renewal from different angles, enriching the theoretical connotation of TOD-led urban renewal. Shin [22] analyzed the methods and characteristics of urban regeneration to explore urban planning methods applicable to the development of station impact zones in small- and medium-sized local cities, which are often excluded from domestic urban regeneration efforts. Dai et al. [23] explained the concept and principles of TOD, exploring the logic behind TOD and urban renewal. Sho et al. [24] focused on urban regeneration in Taipei City, Taiwan. They found that promoting connectivity between modes of public transportation can be effective, especially in residential land use areas, as it can improve policies based on the urban regeneration TOD model. The 15 min city concept, as mentioned in the studies by Mocák et al. [25] and Moreno et al. [26], emphasizes proximity, diversity, density, and ubiquity, which overlap with the TOD approach, and provides an analytical framework for studying the integration of transportation and urban renewal.

The basis for researching transit-oriented urban renewal is to evaluate each TOD accurately. Bertolini (1998) [27] developed a node–place model based on station redevelopment, which includes two key conclusions (Figure 1a). The first is to take the matching degree of node value and place value as an indicator to evaluate the effectiveness of spatial development in the station area. The second is that the model can be based on the degree of coordination between nodes and places and can classify the subway station area into five states: stress, unbalanced place, unbalanced node, balanced, and dependence. The “node–place” model is a standard model for evaluating subway station areas. It provides planning guidance based on the coordinated development statuses of station areas. To the author’s study, considering the influence of the feedback value on station area revenue, we add a revenue dimension to the original “node–place” model. Scholars have improved and extended various NP models for different application scenarios. Lyu et al. [28] refer to the concept of TOD and use “Transit” and “Development” to represent node and place indicators and add “Oriented” indicators to describe the interaction between transportation and urban development. Vale et al. [29] examine the urban conditions of pedestrian accessibility in station areas and introduce the “Design” dimension to enhance the understanding of the integration of transportation and land use. Cao et al. [30] included the “Ridership” indicator to analyze the interaction and coordination between the passenger flow of rail transit and the degree of node–place integration. Rodriguez et al. [31] extended the dimensions of “integration” and “value” to form a multi-axis model that planners and policymakers can use to understand the development of public transportation stations.

The importance of transit premium feedback has received much attention in existing studies of transit-led urban regeneration [32,33,34,35,36]. Turbay et al. [32] have shown that the Bus Rapid Transit (BRT) corridor in Curitiba, Brazil has benefited the high-income class and high-quality real estate along the corridor. Mulley et al. [33] and Cervero et al. [34] point out that an urban transit system can increase land and property values and create economic benefits. The value-added portion can fund existing and new urban transit systems. Yang et al. [35] further pointed out that urban rail transit has a positive impact on surrounding house prices; this positive impact diminishes rapidly as the distance from urban rail transit stations increases; interchange stations have a more significant positive impact on surrounding house prices than ordinary stations; urban rail transit accessibility has a more substantial effect on house prices in the suburbs. Mittal [36] and others summarize the land value capture implementation process and present current debates and controversies, providing an initial decision-making “toolkit” for cities seeking innovative financing solutions for infrastructure improvements.

Therefore, based on the “node–place” model, this paper considers the spatial and temporal economic effects of rail transit on the development of surrounding land, adds the element of revenue feedback to the 3rd axis, and constructs the “node–place–revenue” classification method to provide a quantitative analysis tool for transit-guided urban renewal. The K-means clustering method is used to summarize the development advantages and shortcomings of different types of site areas, identify the key directions of urban renewal, and formulate differentiated renewal strategies and multiple synergistic guidance. Sites can be classified into five theoretical situations based on the differences in 3-dimensional indicators: (1) stress and high revenue—node, place, and revenue value are all strong; (2) unbalanced place—place value is much higher than node value; (3) unbalanced node—node value is much higher than place value; (4) balanced—located in the middle of the diagonal area, nodes, place, and revenue value promote each other and develop in a balanced way; and (5) dependence and low revenue—node, place, and revenue value are all relatively absent (Figure 1b).

2.2. Research Method

This study was divided into four stages: sample data collection, indicator selection and processing, analysis of assessment results, and guidance for updating differences (Figure 2). In the first stage, we selected the station spatial research samples and calibrated the scope. We then systematically collected multi-source data related to the samples and integrated the multi-source data to construct the fundamental database.

In the second stage, we referred to relevant studies, constructed evaluation indicators from the three dimensions of “node–place–revenue”, determined the weights of each indicator using expert scoring and AHP, assigned values to the fundamental evaluation unit indicators, and chose the positive and negative attributes of the indicators. The positive and negative characteristics of the indicators were also determined.

In the third stage, we quantitatively evaluated each TOD site’s node value, place value, and revenue value based on the “node–place–revenue” model. Based on this, we used the K-means clustering algorithm to classify the station area types and finally formed five types of station areas with significant differences.

In the fourth stage, based on the research results of the first three stages, differentiated urban renewal strategies were proposed for the value characteristics of different types of station space. The research results can provide a scientific basis for decision-making and planning guidelines for rail-transit-oriented urban renewal in Shenyang.

2.2.1. Research Object and Scope

As the first city in Northeast China to open a subway, Shenyang’s rail transit development can cover multiple levels of impactful renewal of the station area after the completion of rail transit construction, renewal of the station periphery triggered by the intersection of old and new lines, and urban renewal initiated by the new construction of rail transit. It is helpful to analyze the prevailing problems comprehensively; therefore, this paper chooses to take Shenyang Metro Line 1 as the research object (Figure 3).

The first phase of Shenyang Metro Line 1 was put into operation on 27 September 2010, marking both the first subway line to be built and operated in Shenyang City as well as the first subway line to be put into operation in Northeast China. Line 1 Phase I project, starting from the west at Shisan-hao Street Station and ending at the east at Liming Square Station, passes through Yuhong District, Tiexi District, Heping District, Shenhe District, Dadong District, connecting Shenyang Economic Development Zone, Tiexi Industrial Zone and Shenyang Railway Station, Taiyuan Street Shopping District, Middle Street Square City, and other old city passenger flow distribution areas, facing the situation of micro-renewal within the site area after the completion of construction.

Shenyang Metro Line 1 Phase II East Extension Project, from Line 1 Liming Square Station in the west to Shuangma Station in the east, passes through Shenhe District, Hunnan District, and other new urban areas. It faces the urban renewal triggered by the new railway construction and the renewal situation around the site initiated by the intersection of the latest and original lines (

Table A1. Basic information of stations on Shenyang Metro Line 1.

Number	Station Name	Administrative District	Platform Type	Transfer Route
1	Shisan-hao St.	Tiexi District	Underground Side	-
2	Zhongyang Ave.
3	Qi-hao St.
4	Si-hao St.
5	Zhangshi		Underground Island
6	Kaifa Bdwy	Yu Hong District
7	Yuhong Sq.
8	Yingbin Rd.
9	Zhonggong St.	Tiexi District
10	Qigong St.
11	Baogong St.
12	Tiexi Sq.			Shenyang Metro Line 9
13	Yunfeng St.			-
14	Shengyang Rwy. Stn.	Heping District		Shenyang Metro Line 5 (Planned)
15	Taiyuanjie			Shenyang Metro Line 4
16	South Market			-
17	Qingnian Ave.	Shenhe District		Shenyang Metro Line 2
18	Huaiyuan-men			-
19	Zhongjie			Shenyang Metro Line 6 (Planned)
20	East Zhongjie	Dadong District		-
21	Pangjiang St.			Shenyang Metro Line 10
22	Liming Sq.		Underground Side	-
23	Xinhui St.	Shenhe District	Eastern Extension (Not Yet Opened)
24	Xinning St.
25	Dongdaying St.
26	Agriculture Univ.
27	Qianling	Hunnan District
28	East Mausoleum Park
29	Shuiquan
30	Boguan North Ave.
31	Botanical Garden
32	Shuang Ma

).

Overall, the diversity of TOD station types in Shenyang Metro Line 1 provides us with a rich and diverse dataset [37]. This diversity offers ample opportunities to study TOD-led urban renewal strategies, which help analyze prevailing problems and develop comprehensive targeted renewal strategies.

In studies on the built environment around rail transit stations, the Pedestrian Catchment Area (PCA) is commonly used to represent the service coverage of rail transit stations. Most existing studies employ circular buffer zones to represent the PCA of the stations [38]. Considering the concept of the 15 min city, according to the relevant guidelines [39], this paper selects an 800 m buffer as the PCA of metro stations.

2.2.2. Data Sources

The applied multi-source big data mainly includes railway station data, railway line data, bus station data, bus line data, point of interest (POI) data around the station, building data, land data, property data, and population data. The above multi-source data primarily come from various data sources, including electronic map operators and public data (

Table 1. Details of multi-source data usage.

Name	Field	Source
Rail Transit Station Data	Station Name, Latitude, and Longitude	Open street map [41]
Rail Transit Line Data	Line Name, Latitude, and Longitude	Amap [42]
Bus Station Data	Station Name, Latitude, and Longitude	Amap
Bus Line Data	Line Name, Latitude, and Longitude	Amap
POIs Around Stations	Name, Category, Latitude, and Longitude	Open street map
Building Data	Height, Type, Area, Location	Open street map
Land Use Data	Type, Area	Open street map
Real Estate Data	Name, Price	Lianjia [43]
Population Data	Population, Location	Swguancha [44]

). A spatial database was built on the geographic information system (GIS) platform for integration and measurement. This approach could comprehensively analyze the urban development factors related to TOD and effectively combine qualitative and quantitative data, thus supporting our research findings [40].

2.2.3. Indicator Selection and Processing

The indicator system of the “node–place–revenue” model is based on the various indicators of the classical “node–place” model, as described in the literature [45,46,47,48]. We have introduced the benefits of land and property premiums to construct an indicator system based on three dimensions with 12 indicators (

Table 2. Evaluation index system and weights of the “node–place–revenue” model.

Target Layer	Indicator Layer	Indicator Description	Weight	Attribute
Node Value	Number of Station Entrances/Exits (N1)	The number of entrances and exits at the station.	0.14	Forward direction
	Number of Service Directions (N2)	Terminal stations have 1 direction; additional transfer lines increase number of directions by 2.	0.32	Forward direction
	Number of Bus Stops in the Area (N3)	The count of bus stops within the station’s vicinity.	0.22	Forward direction
	Bus Transfer Convenience (N4)	The number of bus transfer routes within 200 m of the station.	0.26	Forward direction
	Accessibility to City Center (N5)	Distance from each station to the city center (Zhongjie).	0.07	Positive direction
Place Value	Building Development Intensity (P1)	The ratio of total building floor area to the total land area.	0.13	Forward direction
	Building Density (P2)	The ratio of building footprint area to the total land area.	0.12	Forward direction
	Land Use Function Mix (P3)	Calculated using the entropy method for POI land use mix. ($\mathrm{H}=\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{p}}_{\mathrm{i}}\mathrm{l}\mathrm{n}{\mathrm{p}}_{\mathrm{i}}$)	0.31	Forward direction
	Number of Facilities (P4)	The count of POIs within the station area.	0.25	Forward direction
	Population Density (P5)	The ratio of the population in the station area to the total land area.	0.19	Forward direction
Revenue Value	Land Premium Benefit (R1)	Calculated based on empirical values from urban rail transit premium effect assessments.	0.53	Forward direction
	Property Premium Benefit (R2)	The ratio of average housing prices within 500 m to those within 500–800 m of the station.	0.47	Forward direction

). The selection of indicators is directly or indirectly related to the urban renewal of subway stations, and the indicator system of the multidimensional “node–place–revenue” model has been constructed to measure the renewal potential of the station area under different circumstances.

• Nodal value reflects the transportation service function and value presented by metro stations as urban transportation nodes. The number of station entrances and exits (N1) (Reusser et al. [45]), the number of directions served by the station (N2) (Reusser et al. [45]), and the number of bus stops in the station domain (N3) (Reusser et al. [45], Kim et al. [46]), ease of bus transfer (N4) (Bertolini et al. [47]), and accessibility to the city center (N5) (Kim et al. [46], Bertolini et al. [47]) are five indicators.
• Place value reflects whether the built environment of the station area where the metro station is located is conducive to urban renewal. Building development intensity (P1) (Kim et al. [46]), building density (P2) (Lu et al. [48]), a functional mix of land use (P3) (Reusser et al. [45], Lu et al. [48]), number of place facilities (P4) (Lu et al. [48]), and population density (P5) (Reusser et al. [45]) are five indicators.
• Revenue value reflects the ability of the land and property value added generated in the process of urban renewal to feed back the investment in rail transit construction in the form of rent or sales profit in the future. Two indicators, land premium benefit (I1) (Salat et al. [7]) and property premium benefit (I2) (Salat et al. [7]), have been selected.

In this paper, combined with the development stage of Shenyang urban rail transit and station space characteristics, firstly, the evaluation index system is constructed through hierarchical analysis, and the expert scoring method is used to compare the relative importance of the relationship between the two and to determine the weight of each index [49]. Secondly, the evaluation results of each indicator are weighted, with negative indicators receiving a positive weighting, allowing them to be integrated with the positive indicators to reflect the overall evaluation results. Finally, to eliminate the influence of different scales in each index data, this paper, based on SPSS 24, uses normalization to process the data of each index so that all the data are re-adjusted to a range between 0 and 1.

In terms of weight selection for node value, Li et al.’s [50] weights are the same as ours. Both set the station service capacity and bus transfer convenience as higher weights and set the city center accessibility as lower weights. In the selection of weights for place value, the weights of Li et al. [51] are the same as ours. Both set the land-use-function mixing degree as a higher weight and the building development intensity as a lower weight. In the weight selection of revenue value, the weights are distributed relatively evenly to avoid subjective bias.

2.2.4. Site Clustering Method

Existing studies have shown that the station evaluation method based on the extended “node–place” model can effectively identify the characteristics of TOD development and provide a scientific basis for urban planning. Guo et al. [52] classified the state of urban development into nine modes by constructing a three-dimensional evaluation system of nodes, places, and green spaces. The experimental results in Harbin showed that this method can provide specific guidance for urban planning and sustainable development policies. Similarly, Yang et al. [53] also extended the “node–place” model to classify Ningbo metro stations into four categories based on their suitability for TOD improvement. The practice in Ningbo demonstrated that the method is effective in identifying station locations suitable for TOD improvement.

Based on this, to further explore the synergistic effect between node, place, and revenue dimensions and to evaluate the stations in more detail, this paper adopts the K-means cluster analysis method to validate the results and form the empirical findings of station spatial typing. This method automatically divides the data into K clusters with high cohesion by calculating the Euclidean distance between samples [54], which is suitable for identifying potential divergence laws in station spatial features. Specifically, we first normalize the results of node, place, and revenue data to eliminate the influence of magnitude; secondly, we determine the optimal number of K clusters through the elbow rule to ensure the maximization of interclass differences with the minimization of intraclass differences; finally, we combine the profile coefficients to validate the reasonableness of the clustering results. This method not only reveals the spatial differentiation characteristics of TOD but also identifies the dominant dimensions of each type of station area through the eigenvalues of the clusters, providing empirical evidence for the subsequent development of differentiated renewal strategies.

3. Results

3.1. Indicator Results

Based on the above index system and calculation method, the weights and attributes of each basic index were determined and obtained. Then, the weighted calculation yielded the node, place, and revenue values for 32 metro TOD station areas along Shenyang Metro Line 1 (

Table A2. Normalized sample station data.

Station Name	N1	N2	N3	N4	N5	P1	P2	P3	P4	P5	R1	R2
Shisan-hao St.	0.50	0.00	0.12	0.31	0.00	0.03	0.21	0.70	0.01	0.37	0.00	0.50
Zhongyang Ave.	0.50	0.33	0.21	0.04	0.04	0.13	0.44	0.89	0.03	0.37	0.09	0.41
Qi-hao St.	0.50	0.33	0.33	0.04	0.11	0.01	0.13	0.87	0.03	0.49	0.08	0.45
Si-hao St.	0.50	0.33	0.17	0.23	0.17	0.12	0.30	0.87	0.04	0.60	0.05	0.46
Zhangshi	0.50	0.33	0.67	0.27	0.24	0.51	1.00	1.00	0.12	0.49	0.16	0.54
Kaifa Bdwy	0.26	0.33	0.67	0.23	0.30	0.39	0.67	1.00	0.16	0.54	0.22	0.50
Yuhong Sq.	0.26	0.33	0.79	0.77	0.37	0.45	0.62	0.91	0.34	0.51	0.33	0.38
Yingbin Rd.	0.26	0.33	0.37	0.04	0.41	0.35	0.74	0.89	0.23	0.31	0.16	0.18
Zhonggong St.	0.26	0.33	0.46	0.27	0.49	0.38	0.56	0.93	0.31	0.43	0.23	0.33
Qigong St.	0.26	0.33	0.96	0.58	0.54	0.57	0.56	0.91	0.43	0.40	0.46	0.23
Baogong St.	0.26	0.33	0.83	0.23	0.59	0.65	0.80	0.87	0.62	0.40	0.52	0.27
Tiexi Sq.	1.00	1.00	0.92	1.00	0.65	0.82	0.79	0.87	1.00	0.40	0.74	0.15
Yunfeng St.	0.00	0.33	1.00	0.19	0.69	0.84	0.73	0.96	0.81	0.71	0.70	0.35
Shengyang Rwy. Stn.	1.00	0.33	0.71	0.00	0.77	0.87	0.73	0.93	0.89	0.49	0.77	0.13
Taiyuanjie	0.26	1.00	1.00	0.62	0.81	1.00	0.76	1.00	0.89	0.60	1.00	0.13
South Market	0.00	0.33	0.92	0.00	0.86	0.81	0.84	0.96	0.67	0.66	0.56	0.21
Qingnian Ave.	0.50	1.00	0.67	0.12	0.91	0.66	0.71	0.91	0.63	0.46	0.76	0.17
Huaiyuan-men	0.26	0.33	0.92	0.42	0.97	0.74	0.85	0.89	0.70	0.34	0.75	0.07
Zhongjie	0.76	0.33	0.75	0.00	1.00	0.71	0.84	0.87	0.98	0.54	0.71	0.08
East Zhongjie	0.50	0.33	0.96	0.35	0.99	0.73	0.82	0.89	0.80	0.77	0.70	0.44
Pangjiang St.	0.26	1.00	0.83	0.12	0.92	0.49	0.68	0.91	0.33	0.40	0.41	0.30
Liming Sq.	0.50	0.33	0.71	0.23	0.86	0.43	0.66	0.87	0.27	0.37	0.25	0.35
Xinhui St.	0.26	0.33	0.50	0.08	0.79	0.42	0.84	1.00	0.16	0.54	0.34	0.61
Xinning St.	0.50	0.33	0.54	0.38	0.66	0.42	0.66	1.00	0.21	0.49	0.39	0.45
Dongdaying St.	0.26	0.33	0.25	0.58	0.66	0.23	0.60	0.96	0.02	0.29	0.12	0.22
Agriculture Univ.	0.26	0.33	0.08	0.27	0.60	0.18	0.49	1.00	0.07	0.29	0.12	0.16
Qianling	0.26	0.33	0.12	0.27	0.55	0.22	0.37	1.00	0.04	0.23	0.10	0.10
East Mausoleum Park	0.26	0.33	0.17	0.19	0.47	0.00	0.01	0.57	0.01	0.00	0.01	0.00
Shuiquan	0.50	0.33	0.04	0.00	0.36	0.10	0.18	0.43	0.06	0.66	0.07	1.00
Boguan North Ave.	0.50	0.33	0.00	0.00	0.28	0.12	0.17	0.39	0.00	0.43	0.07	0.53
Botanical Garden	0.26	0.33	0.04	0.00	0.21	0.19	0.28	0.46	0.01	1.00	0.03	0.94
Shuang Ma	0.26	0.00	0.00	0.00	0.11	0.00	0.00	0.00	0.00	0.00	0.00	0.79

).

3.1.1. Node Value

The node value reflects the comprehensive transportation advantages of the TOD station area, and the higher its value is, the stronger the centrality of the station area is. From the perspective of the spatial differentiation of the node value of Shenyang Metro Line 1 (Figure 4), the node value of station areas did not show a single decrease from the center to the periphery but instead formed a hierarchical differentiation of high, medium, and low values within the center and peri-urban areas, respectively. High-quality station areas are primarily concentrated in the city’s central area, such as Tiexi Square Station and Taiyuanjie Station, which have well-developed transportation and significant location advantages. Medium-value station areas are primarily located in central and suburban areas, such as Huaiyuan-men, East Zhongjie, and Pangjiang Streets, which are essential subway intersections and have obvious transportation advantages. Additionally, Zhangshi and Xinhui Streets are considered low-value station areas. Railways and other low-value station areas are typically mixed-use areas, combining residential, industrial, and commercial uses with some transportation value. Low-value station areas are primarily located in suburban areas, such as around the Boguan North Avenue and Botanical Garden stations, where transportation support has not yet been fully established and ecological or cultural tourism functions predominate.

3.1.2. Place Value

The place value reflects the station area’s development intensity and functional diversity. From the perspective of the spatial differentiation of the place value of Shenyang Metro Line 1 (Figure 5), the place value of the station area decreased from the city center to the surrounding area, showing the apparent spatial differentiation feature of “high center and low periphery”. High-value station areas are primarily located in the city center, mostly in mature business districts or residential areas such as Zhongjie, Taiyuanjie, and Yunfeng Street. These areas are saturated with land development, and urban functions are well supported, forming self-contained urban vitality centers. Medium-value station areas are primarily located in the city center and suburban areas, with mixed-function areas such as South Market, Qingnian Avenue, and other medium-high-value station areas, which are highly mixed with functions and have relatively perfect support facilities, and medium-low-value station areas such as Yuhong Square and Qigong Street, which are relatively low in function mixing but have formed basic support facilities. Although the mix of functions is relatively low, an essential support system has been formed. The low-value station areas are mainly located in the outlying suburbs such as around East Mausoleum Park Station and Boguan North Avenue Station. These areas are restricted by the ecological protection policy or the stage of urban development and have a relatively single type of function with low development intensity and an insufficient coverage of public service facilities, which shows the typical characteristics of urban peripheral areas.

3.1.3. Revenue Value

The revenue value measures the economic and social benefits generated by urban renewal and its ability to repay the investment in railway construction from the perspective of spatial differentiation in the revenue value of Shenyang Metro Line 1 (Figure 6). High-value station areas are primarily located in the city center, primarily in the development of mature commercial areas, such as Tiexi Square, Taiyuanjie, and Zhongjie, which feature a mature transportation network and commercial agglomeration effect. This leads to the urban renewal process producing land premium benefits and property premium benefits that are higher. Medium-value station areas are mainly distributed in the city center and suburban areas, mostly in mixed residential and industrial areas or new expansion areas, such as Baogong Street, Qingnian Avenue, and other medium-to-high-value station areas, as well as Yuhong Square and Liming Square and other medium-to-low-value station areas, where the income value matches the development intensity. Low-value station areas are concentrated in the remote suburbs of the city, primarily in ecological zones or undeveloped areas, such as around Shuiquan Station and Shuang Ma Station, where land and property premium benefits are low. Still, they have huge development potential due to the large amount of inefficient land.

3.2. Cluster Results

To further identify the type of station space, clustering was utilized using the K-means clustering algorithm. The elbow and contour maps verified the plausibility of contour clustering (Figure A1). The clustering results showed that 34% of the station spaces were categorized into Cluster 5 (11), followed by Cluster 2 and Cluster 4 (8), Cluster 3 (3), and Cluster 1 (2) (

Table 3. The centroid features and descriptive statistics table of NPR model clustering results.

Cluster	Centroid						Cluster Distribution
	Node		Place		Revenue		Number of Cases (Units)	Proportion (%)
	Average Value	Standard Deviation	Average Value	Standard Deviation	Average Value	Standard Deviation
Cluster 1	0.91	0.13	0.95	0.08	0.95	0.08	2.00	0.06
Cluster 2	0.50	0.11	0.76	0.13	0.76	0.13	8.00	0.25
Cluster 3	0.66	0.12	0.37	0.06	0.37	0.06	3.00	0.09
Cluster 4	0.39	0.09	0.19	0.09	0.19	0.09	8.00	0.25
Cluster 5	0.14	0.09	0.03	0.02	0.03	0.02	11.00	0.34

). These five categories of stations were characterized as follows: Category 1 had high node, place, and revenue values; Category 2 stations had low node values but high place and revenue values; Category 3 had high node values and medium place and revenue values; Category 4 had medium node, place, and revenue values; Category 5 had low node, place, and revenue values. According to the positions of the categories in the model, combined with the clustering of the sample node, the place and revenue values are summarized as follows: stress and high revenue, unbalanced place, unbalanced node, balanced, and dependence and low-revenue station space, respectively (Figure 7). The categories’ spatial differentiation in Shenyang City is shown in Figure 8.

3.2.1. Stress and High Revenue

Category 1 is characterized by high pressure and revenue and is defined by a state of pressure in terms of node and place value and simultaneously exhibits a high revenue feedback value. It is typically situated in the city’s core business district, offering convenient transportation, a functional composition, and high revenue-generating value. However, due to space development, it tends to be saturated, and its further renewal and development may be limited. For example, Taiyuanjie Station is the traditional commercial core area of Shenyang, containing both Line 1 and Line 4 of the interchange station, but also connects the Shenyang station rail passenger transport and has a high node value, and the surrounding collection of the central commercial body, medical and cultural facilities, and place function is highly complex. The Tiexi Square station also belongs to this category as it is the core business district and transportation hub, and development has been saturated.

3.2.2. Unbalanced Place

Category 2 is summarized as being of the uneven place type, where the place function significantly exceeds the node function, and the development intensity is high, but the transportation support is inadequate. Members of this category are primarily located in urban central business districts (CBDs), core business districts, and mature residential areas. This area type features mixed land use, high development intensity, dense population, rich commercial resources, and high property value. However, there are problems, such as a larger neighborhood scale, lower traffic accessibility, and poor road network penetration. Its transportation system struggles to accommodate the large number of people generated by the high-density development of the area, which is prone to traffic congestion. At the same time, the land price of this type of station area is relatively high, with strong market development momentum. For example, Zhongjie Station is a traditional commercial pedestrian street with high development intensity; however, the connection between the station and the pedestrian street is not optimal, and the utilization rate of the underground passage is low.

3.2.3. Unbalanced Node

Category 3 is summarized as being of an unbalanced node type; the node value of this type of site is significantly higher than the place value, and the transportation location advantage is outstanding; however, development is lagging. This site area is primarily located in residential clusters near the city center, and it is served by two rail transit lines and numerous conventional bus lines, offering obvious transportation advantages. However, the level and scale of commercial and public service facilities available at these sites are relatively low, and the range of services offered is limited. There is some revenue feeder value, making it a hotspot for investment by developers and members of the public. For example, Pangjiang Street Station has obvious transportation advantages as a subway interchange station and is near the East First Ring Expressway. Still, excessive transportation functions detract from the area’s urban functions, and the site’s low development intensity and poor mixed land use create an imbalance in the value of the sites.

3.2.4. Balance

Category 4 is characterized as a balanced type, where the balance between node value, place value, and revenue is relatively high. Usually located in the city’s suburbs, these indicators are relatively balanced, with a certain foundation of transportation facilities, functional layout, and revenue-generating value; however, they still have the potential for further improvement. For example, the Liming Square Station is mainly for residential functions, and its node value and place value are more balanced; as the connecting station of Line 1 Phase I and Phase II, it has a certain scale of developable land or land to be renewed, and it is a potential zone for upgrading the urban functions and redeveloping the land at this stage.

3.2.5. Dependence and Low Revenue

We summarize Category 5 as dependent and low-revenue, a generally low composite indicator of locations. The areas in this category are typically situated on urban fringes or in undeveloped areas. The current situation is characterized by predominantly non-urban built-up areas or ecological protection zones, with a lack of road and public transportation facilities, as well as commercial and public services, resulting in a weak potential for market renewal. For example, Shuangma Station, as the terminus of Metro Line 1 East Extension, has a low significance in the rail transportation network. Regarding land use, the surrounding area consists of almost undeveloped land that has not yet been planned or constructed and lacks the foundation of transportation and service facilities.

4. Discussion

4.1. The Spatial Differentiation Characteristics of Multidimensional Value in Station Domain

The study found that the nodes, places, and revenue value of the station areas of Shenyang Metro Line 1 exhibit spatial differentiation characteristics characterized by a “high center and low periphery” pattern. The phenomenon of unbalanced development in the station area is prominent, and the differences between regions are significant. The issue of unbalanced growth in the railway station area has been widely discussed in numerous studies both domestically and abroad. Yang et al. [55], Zhang et al. [56], and Zhang et al. [57] also showed, in their studies, that there is a certain degree of imbalance in station development among regions. In the study by Zhu et al. [58], it was noted that transportation–land mismatch, edge lag, and the influence of value dimensions can lead to significant imbalance. In the studies by Guo et al. [52] and Yang et al. [59], it was noted that station areas with lower comprehensive development indexes are primarily located in the city’s suburbs. In comparison, those with higher indexes are typically located in the center of the town, where socio-economic conditions are generally better.

4.2. Characteristics and Update Potential Evaluation of Station Domain Clustering

In this paper, the station areas of Shenyang Metro Line 1 are categorized into five types using the clustering method: pressure and high revenue, unbalanced place, unbalanced node, balanced, and dependence and low revenue. By analyzing the characteristics of the five types of subway station areas and examining their renewal potential, it has been found that areas with high pressure and revenue are usually located in the city’s core business districts, and further development is limited, resulting in low renewal potential. The unbalanced areas are generally located in the CBD, core business district, and mature residential areas, with strong renewal potential. The unbalanced node is typically situated in residential clusters near the central business district (CBD) and has strong renewal potential. Balance nodes are usually located in the city’s suburbs and have some potential for regeneration. Dependence and low-income areas are generally located on the urban fringe or in undeveloped areas and have weak regeneration potential. Dou et al. [60], Nemroudi et al. [61], and Dai et al. [62] also showed that based on the composite scores of the NP model, the geographic distribution of clustering results roughly decreases from the city center to the suburbs.

4.3. Differentiated Update Strategy for Site Domain Types

Based on the characteristics and renewal needs of different types of station spaces, this paper proposes five differentiated urban renewal strategies [63] and further discusses the corresponding policy tools, synergistic benefit mechanisms, and dynamic effectiveness indicators to ensure the effective implementation of the strategy and the evaluation of their effectiveness (

Table 4. Differentiated governance strategies for different types of site areas.

Station Type	Stress and High Revenue	Unbalanced Place	Unbalanced Node	Balance	Dependence and Low Revenue
Key Characteristics	Urban core with high pedestrian and vehicular traffic, peak land value	Urban centers with strong place functions but weak node functions	City center fringe, with strong transportation but weak service functions	Suburban areas with a balanced work and residence profile but with demand for upgrading	Urban fringe, lagging in development, low financial returns but high social demand
Renewal Strategy	Precise deconcentration	Node upgrades	Function expansion	Endogenous optimization	Infill quality improvement
Policy Tools	Congestion charging, plot ratio transfer, air rights sale	Microcirculation public transportation subsidy	Functional replacement tax deduction, temporary land use permit	Incremental tax financing and flexible control of mixed land use	Land allocation + franchising, tax exemption period + small and micro loans
Stakeholder Collaboration Mechanism	Developer: Underground Link Construction (Hong Kong) Rail Company: Integrated Interchange Hub (Shanghai)	Neighborhood Council: Slow Path Optimization (Shanghai) Bus Company: Microcirculation Routes (Singapore)	Artists: Public Space Revitalization (Tokyo) Community: Shared Facility Operation (Beijing)	Merchants Union: Street Renewal (Barcelona) Transportation Bureau: Bus Optimization (Chengdu)	Government: subsidized schools/hospitals (Guangzhou) Social organizations: tourism training (Kyoto)
Performance Metrics	Short-term: 20% reduction in congestion Long-term: commercial rents balanced with commuter efficiency	Short-term: 30% increase in feeder satisfaction Long-term: ≥40% share of non-commuter commercial revenue	Short-term: 50% increase in non-commuter traffic Long-term: destination station conversion rate achieved	Short-term: job-to-life ratio of 1.2 to 1.0 Long-term: 30% increase in business diversity index	Short-term: 200 new jobs Long-term: ≥30% of earnings from specialty industries

).

Firstly, for stress and high-revenue station spaces, in order to avoid over-development, it is appropriate to adopt the strategy of “fine deconcentration”. The capacity of transportation facilities should be able to meet the demand of regional peak traffic distribution, avoid large-scale construction behavior, and pay more attention to the enhancement of the degree of functional mixing and refinement of urban design. Specific policy tools can be adopted, such as congestion charging, the transfer of plot ratio, the sale of air rights, etc. For the synergistic mechanism, one can refer to Hong Kong’s model of underground corridor construction, or Shanghai’s case of an integrated interchange hub built by the government in cooperation with a railroad company [34]. The long-term goal should be able to achieve a balance between commercial rents and commuting efficiency.

Second, for unbalanced place station spaces, it is appropriate to adopt a “node upgrade” renewal strategy. Priority should be given to upgrading the value of the nodes to match the value of the place to clarify the regional traffic demand and traffic supply level, assess the necessity and feasibility of connecting commercial and other facilities in the station space, attract the market to make up for the gap in transportation facilities in a targeted manner, and improve the level of integration and connectivity of transportation connections. Policy tools such as micro-circulation bus subsidies can be used; benefit synergies can learn from Shanghai’s slow path optimization practice or Singapore’s micro-circulation line operation model. The long-term goal should be to make the share of non-commuter commercial revenue ≥40%.

Thirdly, for unbalanced node station spaces, it is appropriate to adopt the “function expansion” renewal strategy. Scientific planning is needed to guide the functional differentiation and complementary development of the station area, expand diversified functional nodes, attract new business development, and strengthen the construction of commercial facilities and public facilities in the station area through public investment to stimulate the market demand and realize the synergistic and high-quality development of the station area. Policy tools, such as tax credits for functional replacement and temporary land use permits, etc., can be used. For the synergy of interests, one can refer to Tokyo’s public space revitalization or Beijing’s shared-facilities operation case [55]. The long-term goal should be to achieve the conversion rate of “destination stations”.

Fourth, for balanced station spaces, it is appropriate to adopt an “endogenous optimization” renewal strategy. One must study the station type and explore the development potential of the station area. This type of station area typically still retains a particular scale of urban renewal land, offering a relatively considerable investment return and high market development potential. In the future, it is advisable to adopt appropriate incentives in conjunction with market development to guide market forces toward simultaneously enhancing the node value and place value. Policy tools include tax incremental financing and the flexible control of mixed-use land among others. The synergy of interests could be based on Barcelona’s merchant alliance street renewal or Chengdu’s transit optimization practice. The long-term goal should be to increase the business diversity index by 30%.

Fifth, for dependence and low-revenue station spaces, it is appropriate to adopt the “infill and improve quality” type of renewal strategy. For this type of station area, it is relevant to limit its disorderly development shortly, retain the ecological base, and stimulate the vitality of the station area through financial, planning, and other multi-faceted policies in the long term; analyze the mismatch between the advantages of the regional development and the functional positioning of the area on the regional scale; and promote the development of such an area in an orderly and concentrated manner. Policy tools can include land allocation and franchising, as well as tax holidays and small- and medium-sized loans. The synergy of interests can be exemplified by the government-subsidized school and hospital model in Guangzhou or the tourism training program for social organizations in Kyoto. The long-term goal is to achieve a 30% share of revenue from specialty industries.

4.4. Limitations

However, this paper still has some limitations. Firstly, the “node–place–revenue” model proposed in this paper primarily aims to elucidate the guiding effect of rail transit on the revitalization of the surrounding areas from the perspective of economic balance of payments. The paper has selected Shenyang Metro Line 1 as the research object, which limits the generalizability of the paper’s conclusions due to the geographic environment, financial structure, and policy context of a single city. Therefore, in the future, the sample coverage will be expanded, and a comparative multi-case survey will be conducted to refine the common patterns and adaptive adjustment strategies across regions.

Second, in the current study, the indicators of the “node–place–revenue” model were static data, which represented the current stage. It is necessary to incorporate dynamic variables that reflect the city’s stage of development, policy cycle, and market environment into future research. In particular, the measurement of land premium benefits and property premium benefits may vary depending on the market environment, and an in-depth discussion of land premium and property premium benefits will be added in the future.

Finally, the clustering analysis results are limited by the available data size and the limitations of algorithm selection. Future research can explore the introduction of machine learning algorithms, combined with richer, multi-source, heterogeneous data, to improve classification accuracy and the model’s explanatory power and to further investigate the intrinsic correlations of complex systems.

5. Conclusions

This study conducted systematic research on rail-TOD-guided urban renewal, and the main conclusions were as follows:

• A three-dimensional evaluation system of TOD-guided urban renewal was constructed at the theoretical level. By interpreting the mechanism of rail-TOD-guided urban renewal, it was transformed into three dimensions and 12 indicators, among which the node dimension reflected the transportation service function and value of the subway station as an urban transportation node, the place dimension reflected whether the built environment of the station area where the subway station was located was conducive to urban renewal, and the revenue dimension demonstrated the economic and social benefits generated by urban renewal that could feed back into the investment in rail transit construction.
• We developed a quantitative analysis framework for urban renewal guided by rail TOD at the methodological level. Based on multi-source data, the node value, place value, and revenue value of 32 Shenyang Metro Line 1 stations were quantified. The K-means clustering method was applied to divide the stations into five categories: pressure and high revenue, unbalanced place, unbalanced node, balanced, and dependence and low revenue. The different types of stations exhibited significant spatial differentiation characteristics, providing a scientific basis for the development of differentiated renewal strategies.
• At the practical level, differentiated regeneration strategies were proposed. Based on the differences in the characteristics of five types of stations—stress and high revenue, balanced, unbalanced node, unbalanced place, and dependence and low revenue—it was proposed that targeted regeneration strategies can be explored from the perspectives of precise dissolution, node upgrading, function expansion, endogenous optimization, and infill and quality improvement. We also incorporated the exploration of policy instruments, synergistic mechanisms, and dynamic effectiveness indicators to enhance the relevance of the proposed strategies.

This article can serve as a practical reference for urban renewal in areas with sizable urban rail transit stations. However, this study still had some limitations, including the selection of cases limited to a single type of city, a lack of dynamic adaptability in the construction of the indicator system, and limitations in the application of data and algorithms for clustering methods. Future research can further explore the applicability of the NPR model in other cities. Secondly, it can construct a dynamic indicator system that allows the evaluation model to be adjusted according to the stage of urban development. Ultimately, it can incorporate machine learning methods to enhance classification accuracy.