Study on Development Pattern and Comprehensive Evaluation of Integration of Urban Underground Space and Rail Transit in China

Abstract

China’s urban development has entered the stage of urban renewal, transitioning from large-scale incremental construction to stock enhancement and renovation. In response to challenges such as traffic congestion and land-use constraints, urban planning has increasingly prioritized the development of urban underground space (UUS) and urban rail transit (URT). However, disparities in development levels across cities, as well as the fragmented and uncoordinated integration of UUS and URT, remain significant challenges. While existing studies have predominantly examined UUS and URT independently, the interaction between the two has been largely overlooked. In order to supplement this gap, the aim of this study is to construct an integrated measurement framework and quantitatively assess the level of integration between UUS and URT in 38 Chinese cities. We seek to explore the interrelationship between the two, identify the differences in the integration process across cities, and discuss the potential implications for urban planning and policy making. Specifically, this study assembles an integration measurement index framework from three dimensions (UUS, URT, and urban development), quantitatively evaluates the integration level of 38 Chinese cities by using the entropy weighting and VIKOR methods, and summarizes the regionalization characteristics by using K-means clustering. The results of the study show that (1) there is a positive correlation between UUS development and URT construction and the degree of correlation is affected by the level of urban development; (2) the construction of URT is dominant in the integrated system of UUS and URT; (3) the sample cities can be classified according to the characteristics of the integration into the types of developing cities (47.37%), mature cities (21.05%), and underdeveloped cities (31.58%), and there is a clustering effect on the overall spatial distribution.

1. Introduction

With the continuous acceleration of urbanization and the continuous expansion of population size, Chinese cities are facing a series of complex challenges, including scarcity of land resources and traffic congestion [1]. Against this background, the development and utilization of urban underground space (UUS) and urban rail transit (URT) have become two key areas for promoting sustainable urban development [2].

UUS refers to the underground area used for transportation, commerce, public facilities, and other functions in the city, including underground roads, underground shopping malls, and underground parking lots [3]. Through scientific planning and rational utilization, UUS can alleviate the tension of ground space and improve the utilization efficiency of land resources [4]. As an important part of modern urban public transportation systems, URT plays an important role in alleviating traffic congestion and enhancing the efficiency of urban access [5].

A natural synergy exists between UUS and URT, as underground metro lines, stations, and hubs form integral parts of underground infrastructure [6]. The strategic planning of URT stations directly influences the spatial layout of UUS, affecting underground commercial development, pedestrian connectivity, and service facilities. Additionally, expanding the URT network fosters greater interconnectivity within UUS, enabling seamless integration of underground spaces, such as transit-oriented commercial centers and multifunctional underground complexes. UUS provides new possibilities for solving the problem of land resource tension, while URT is an important means to relieve traffic pressure and improve the urban environment. The organic combination of the two can not only enhance the utilization efficiency of urban space but also promote the optimization and coordinated development of urban functions.

In recent years, China’s urban construction has made remarkable progress, especially in first-tier cities such as Beijing, Shanghai, and Shenzhen, where urban spatial networks have gradually taken shape [7]. However, in most cities where UUS development and URT construction are in their infancy, there are still problems such as the fragmentation of space and inefficient transportation [8]. With the development of social economy, how to realize the integrated development of UUS and URT has become the key to further enhance the vitality and sustainability of the city [9], and a large number of scholars have launched relevant research.

In terms of UUS development, additional physical space provides more capacity for densely developed urban areas [10], which helps to establish interconnected urban spaces in public areas and enhance the compactness of cities [11]. Related studies have focused on UUS level measurement [12], spatial sustainability evaluation [13,14,15], spatial vitality [16,17], and development potential [18,19].

In terms of URT planning, early UUS exists only as a connecting space between URT stations and surrounding land, and with the increasing prominence of the benefits of URT passenger flow, UUS gradually shows the trend of scale and network development [20,21]. Under the promotion of URT construction, the intensity of land development along the URT line and the degree of three-dimensionalization has been increased [22,23]. Relevant studies mainly focus on the intensity of URT passenger flow [24,25], factors affecting the built environment [26], drivers of public space [27,28], three-dimensional development [29,30,31], and the socio-economic impacts of transportation hubs [32,33,34,35].

When summarizing the research of previous scholars, two limitations are found: (1) Most of the existing studies focus on individual cities or a limited number of cities [36], primarily those with advanced economic development and well-developed metro systems. Overall, there is a lack of comprehensive studies on cities in different stages of development of UUS and URT. (2) UUS and URT have an interactive relationship in the state of socio-economic development; most studies, however, tend to assess the two separately rather than as an integrated system [37]. This compartmentalized approach makes it difficult to capture the overall development dynamics between the two.

To address these shortcomings, this study investigates the current development status of UUS and URT in cities in different stages of development, providing a new integrated development perspective for urban planning strategies. Specifically, this study seeks to answer the following key questions: (1) Do UUS and URT exhibit significant correlation in different urban contexts? (2) What are the spatial distribution characteristics of cities with varying levels of integration? (3) How can the integration of UUS and URT enhance the sustainable development of cities?

This study systematically constructs a comprehensive evaluation framework that integrates the three dimensions of urban development, UUS, and URT. Through the entropy weight method, the VIKOR method, and the K-means clustering method, we quantitatively assessed the level of integration of UUS and URT in 38 Chinese cities. The results of the study provide scientific support for urban planning and policy making, as well as inspiration for international urban development. Exploring the deep integration of UUS and URT can provide not only theoretical support for the sustainable development of cities but also new perspectives and practical paths for urbanization in other countries and regions [38]. The rest of this paper is arranged as follows: The second part introduces the research area and data; the third part introduces the research measurement method, including constructing the indicator system, selecting evaluation methods, and performing indicator assignment. Next, the fourth part elaborates the results and discussion; finally, the fifth part gives the conclusion of this paper, puts forward corresponding countermeasure suggestions, and provides an outlook on the future research process.

2. Methods

2.1. System Construction

2.1.1. System Construction Process

The selection of indicators follows a systematic and hierarchical approach to ensure scientific rigor. The process consists of three stages:

• Literature Review: A preliminary indicator system is formulated based on an extensive review of domestic and international studies.
• Expert Validation: Industry experts provide feedback to refine the indicator framework, ensuring its relevance and applicability.
• Iterative Verification: The framework undergoes multiple rounds of validation and adjustment to enhance its robustness and accuracy.

This structured approach ensures that the final evaluation system effectively captures the integration level of UUS and URT, providing a reliable basis for quantitative analysis (Figure 1).

2.1.2. Expert Validation

In this study, to ensure the validity and rationality of the constructed indicator framework, experts in the field were invited to revise and refine the indicators through a questionnaire survey. Specifically, 20 experts specializing in urban planning, transportation engineering, underground space development, and related fields were invited to participate in the validation process. All participating experts were professionals affiliated with renowned universities, research institutes, and urban planning and construction units, possessing extensive knowledge and practical experience in UUS and URT research and practice. The expert consultation form is provided in Appendix A. Based on expert feedback, the preliminary indicator framework was refined by incorporating new dimensions and optimizing the formulation of certain indicators to enhance its comprehensiveness and applicability.

2.2. Selection of Indicators

2.2.1. Integration Interactions

The coordinated development of a public transportation network and land use is of great significance to promote the sustainable and healthy development of cities, and this interactive relationship between transportation and land can be understood as the interactive feedback between URT and UUS at the underground level [39]. UUS increases with the increase in the development intensity of surface land, and its function is constantly enriched, promoting its development with the URT link [40] and gradually forming a development trend of integration with urban socio-economic development. Previous studies have pointed out that human perception is an important indicator of UUS [10], but it is not included in this paper due to its overly subjective nature. Similarly, some important but difficult-to-identify indicators have not been used.

Figure 2 summarizes the interactions among system elements. It should be noted that in this paper, the setting of urban development dimension indicators is not to reconstruct them but to use them as a background or reference to examine the level of integrated development of UUS and URT. Therefore, this paper does not seek to enrich the number of indicators but tries to ensure that there is one indicator for each necessary criterion.

2.2.2. Indicator Content

URT promotes station accessibility, which in turn promotes population density and development intensity, thus leading to an increase in spatial function demand; UUS provides additional public space for the city, which helps to improve the spatial structure of the city and promote social and economic development. Combined with the existing research, the evaluation indicators and elements of the integrated development level of UUS and URT are summarized in

Table 1. Composition of elements for evaluating the level of development of integration of UUS and URT.

Indicator Layer	Element Layer	Meaning and Calculation Method	Reference
UUS (T1)	Intensity of UUS development (T11)	Indicator measuring the orderly and connotative development of UUS resource utilization; the higher the development intensity, the higher the economic efficiency of land use. Calculated as UUS construction area/urban area	[1,9,16,21,26]
	Size of UUS per capita (T12)	Per capita ownership of underground space floor space. Calculated as total UUS area/resident population	[11,12,14,17,20,26]
	Social domination of UUS (T13)	Indicator measuring the social-led or policy-led character of UUS development. Calculated as area of urban public underground space (minus the scale of human defense works)/total area of UUS	[14,19,41]
	Parking undergrounding rate (T14)	Indicator measuring the functional structure of urban underground space and the rational allocation of infrastructure. Calculated as underground parking spaces/actual total parking spaces in the city	[13,16,19,25]
URT (T2)	Annual average daily passenger flow (T21)	The daily average of cumulative passenger traffic on the transportation system throughout the year. Calculated as total annual passenger volume/365	[20,22,25,29,31]
	Number of lines in operation (T22)	Total number of lines actually operating in the URT system, which measures the size of the URT network.	[21,22,26,29]
	Line density (T23)	Density of operating lines in the URT network. Calculated as total line length/built-up area of the city	[20,27,29,31]
	Site density (T24)	Density of all service stations installed in the URT system, reflecting the size of the nodes covered by the network. Calculated as total number of stations/built-up area of the city	[20,24,28]
	Passenger flow intensity (T25)	Annual passenger flow per unit of line length, used to measure the efficiency of line utilization. Calculated as total annual passenger flow/total length of the line	[23,24,29]
	Orbital coverage ratio (T26)	Space share of area covered within the service radius of the station. Calculated as area of built-up land within 800 m of URT station/total area	[42]
Urban development (T3)	GDP (T31)	Reflecting the level of regional economic development. Calculated as regional GDP/total population	[18,28]
	Percentage of tertiary sector (T32)	The share of the service sector in the structure of the regional economy. Calculated as value added of the tertiary industry/regional GDP	[18,28]
	Urban travel intensity (T33)	Average frequency of transportation trips per unit of population, which is used to reflect the level of transportation demand in the city. Calculated as total number of trips in the city/total population	[43]
	Population density (T34)	Number of people per unit area. Calculated as total urban population/area of administrative area	[35,44]
	Per capita road area (T35)	Urban road area allocated per unit of population. Calculated as total road area/total urban population	[35,39,40]

.

2.2.3. Indicator Statistics

In the process of collecting data on the city’s relevant indicators, some missing values were estimated by using linear interpolation. Specifically, Stata (version 17.0) was utilized to perform linear interpolation for adjacent known years. This method operates under the assumption that indicator trends change approximately linearly over short periods, allowing missing values to be estimated based on the average slope of known values. This approach offers advantages in terms of simplicity and computational efficiency and is particularly suitable when the proportion of missing data is small.

2.3. Weighting Scheme

In order to accurately reflect the intrinsic connection between objective indicators and the degree of change and determine the weight of each indicator, this study chooses the entropy weight method [45], the weighted composite score method [46], and the VIKOR method [47] as the evaluation methods, and their main processes are as follows.

2.3.1. Z-Score Scaling of Data Indicators

In the process of indicator calculation, due to the differences in units or nature among indicators, the evaluation results may be biased when directly analyzing the raw data. Therefore, in order to ensure comparability between indicators of different unit and nature, the indicators need to be dimensionless to obtain data that can be analyzed and calculated in a uniform manner. Since the indicator elements of the system constructed in this study are all positive indicators and do not involve the calculation of negative indicators, it is recommended that the Z-score scaling method be adopted. The Z-score scaling calculation method is as follows:

$$x_{ij}^{\prime }={\displaystyle \frac{x_{ij}-{\mu }_i}{{\sigma }_i}}$$

(1)

where $x_{ij}^{\prime }$ is the jth constituent element of element set i, $i=1,\dots ,n;j=1,\dots ,m$; $x_{ij}$ is the original value of the element; ${\mu }_i$ is the mean value of element set i; and ${\sigma }_i$ is the standard deviation of element set i.

2.3.2. Determination of Indicator Weights

The weight calculation of the indicator system adopts the entropy weight method, combining the geometric mean and linear weighting to calculate the degree of contribution of each indicator to the set of elements, as the result of the weight of the indicator. The process is as follows:

• Calculation of indicator ratios:

$$p_{ij}={\displaystyle \frac{x_{ij}^{\prime }}{{\displaystyle \sum _{t=1}^n{x}_{ij}^{\prime }}}}$$

(2)

where $p_{ij}$ is the ratio of the jth indicator for element set i, $i=1,\dots ,n;j=1,\dots ,m$.

2.

Calculation of entropy:

$$e_j=-{\displaystyle \frac{1}{\ln n}}{\displaystyle \sum _{i=1}^n{p}_{ij}}\ln p_{ij}$$

(3)

where $i=1,2,\dots ,n;j=1,2,\dots ,m$.

3.

Calculation of coefficient of variation:

$$g_j=1-e_j$$

(4)

where $j=1,2\dots ,m$.

4.

Calculation of indicator weights:

$${\omega }_j={\displaystyle \frac{g_j}{{\displaystyle \sum _{j=1}^{\mathrm{m}}{g}_j}}}$$

(5)

where $j=1,2\dots ,m$.

2.3.3. Comprehensive Evaluation

The comprehensive evaluation score adopts the weighted comprehensive score method and the VIKOR method to fully utilize the information of the original data and accurately reflect the gaps between the evaluation programs. The process is as follows:

1.

Calculation of the weighted comprehensive score:

$$P_i={\displaystyle \sum _{j=1}^m{w}_j}{x}_{ij}^{\prime }$$

(6)

where $i=1,2,\dots ,n;j=1,2,\dots ,m$.

2.

Calculation of the positive and negative ideal solutions:

$$x_j^{+}=\max \left\{x_{ij}^{\prime }\right\}$$

(7)

$$x_j^{-}=\min \left\{x_{ij}^{\prime }\right\}$$

(8)

where $x_j^{+}$ is the positive ideal solution and $x_j^{-}$ is the negative ideal solution,$i=1,2,\dots ,n;j=1,2,\dots ,m$.

3.

Calculation of group benefit values and individual regret values:

$$S_i={\displaystyle \sum _{j=1}^m\frac{w_j(x_j^{+}-x_{ij}^{\prime })}{x_j^{+}-x_j^{-}}}$$

(9)

$$R_i=\max w_j({\displaystyle \frac{x_j^{+}-x_{ij}}{x_j^{+}-x_j^{-}}})$$

(10)

where $S_i$ is the group benefit value and $R_i$ is the individual regret value,$i=1,2,\dots ,n;j=1,2,\dots ,m$.

4.

Calculation of the degree of compromise:

$$Q_i=\lambda {\displaystyle \frac{S_i-S^{-}}{S^{+}-S^{-}}}+(1-\lambda ){\displaystyle \frac{R_i-R^{-}}{R^{+}-R^{-}}}$$

(11)

where $S^{+}=\max \left\{S_i\right\}$, $S^{-}=\min \left\{S_i\right\}$, $R^{+}=\max \left\{R_i\right\}$, $R^{-}=\min \left\{R_i\right\}$, and $i=1,2,\dots ,n$. $\lambda$ is the compromise coefficient, with a value range of [0, 1]; when $\lambda$ > 0.5, it is said to maximize the interests of the group as the basis for decision making; when x < 0.5, it is said to minimize the individual regret as the basis for decision making. In order to simultaneously pursue the maximization of the group’s utility and the minimization of individual regrets, we select a compromise coefficient of 0.5. The integration development level of each city is ranked according to the Q-value, with smaller values representing higher levels.

2.4. Cluster Analysis

Clustering is a data analysis method for intuitively understanding the structure of data. As a vector quantization method, K-means clustering is one of the most basic and widely used unsupervised machine learning methods [48]. Based on the Euclidean distance (or other distance metrics), K-means clustering calculates the distance from each sample point to all cluster centers and assigns it to the cluster corresponding to the nearest cluster center. The computation process is as follows:

1.

Calculation of distance:

$$d(a_i,c_j)=\sqrt{{\displaystyle \sum _{n=1}^m{(a_i-c_j)}^2}}$$

(12)

where $a_i$ is the coordinate of the sample point, $c_j$ is the coordinate of the jth clustering center, and m is the dimension of the feature.

2.

Update of the clustering center:

$$c_j={\displaystyle \frac{1}{N_j}}{\displaystyle \sum _{a_i\in C_j}{a}_i}$$

(13)

where $c_j$ is the jth clustering center, $N_j$ is the number of sample points in the jth cluster, and $C_j$ is the set of samples contained in the jth cluster.

3.

Repeating of iterations:

Equations (12) and (13) are repeated until the clustering center no longer changes or the maximum number of iterations is reached.

4.

Calculation of the sum of squared errors:

$$J={\displaystyle \sum _{j=1}^k{\displaystyle \sum _{a_i\in C_j}\Vert a_i-c_j{\Vert }^2}}$$

(14)

where $J$ is the sum of error squares and k is the number of clusters; the algorithm converges gradually by iteratively updating the assignments and clustering center locations to minimize $J$.

3. Research Objects and Data Sources

3.1. Objects of Study

As of 31 December 2024, a total of 58 cities in mainland China (excluding Hong Kong, Macao, and Taiwan) had operational urban rail transit systems. Thirty-eight cities were selected for this study (Figure 3) according to the following criteria:

• Since this study examines the relationship between UUS and URT, all selected cities have operational subway lines and URT stations.
• The selected cities differ in size and level of development, encompassing first-tier, second-tier, and relatively smaller cities to ensure that the findings are representative of diverse urban characteristics and needs.
• The selected cities are geographically distributed across eastern, central, and western China, ensuring that the findings reflect regional variations in urban spatial patterns.
• These thirty-eight cities possess comprehensive public data sources, including construction statistics yearbooks, rail transit analysis reports, and government-released data on underground space development.

3.2. Data Sources

The data used in this study are all from China’s official statistical yearbooks and research reports. Among them, the T11~T14 data are from the Blue Book of China’s UUS Development 2024; the T21~T25 data are from the Annual Statistical and Analytical Report on URT 2023; the T26 data are from the Annual Monitoring Report on Commuting in China’s Major Cities 2024; the T31 and T32 data are from the Statistical Yearbook of Chinese Cities 2023; the T33 data are from China Urban Transportation Report 2023; the T34 and T35 data are from China Urban Construction Statistics Yearbook 2023. The statistics all represent the year 2023 data, and a few missing data were filled in by interpolation.

4. Results and Discussion

4.1. Measurement Results of Each Index Layer

After the above data analysis and calculation, we obtained the weights of each element of the evaluation system of the development level of integration of UUS and URT and the scores of each indicator layer for 38 cities (

Table 2. Weights of the integrated system of UUS and URT.

Indicator Layer	Weights	Element Layer	Group Weights	Combined Weights
UUS (T1)	0.256	Intensity of UUS development (T11)	0.2574	0.066
		Size of UUS per capita (T12)	0.1788	0.046
		Social domination of UUS (T13)	0.2537	0.065
		Parking undergrounding rate (T14)	0.3101	0.079
URT (T2)	0.531	Annual average daily passenger flow (T21)	0.3742	0.199
		Number of lines in operation (T22)	0.2116	0.112
		Line density (T23)	0.0991	0.053
		Site density (T24)	0.0706	0.037
		Passenger flow intensity (T25)	0.1145	0.061
		Orbital coverage ratio (T26)	0.1300	0.069
Urban development (T3)	0.213	GDP (T31)	0.2426	0.052
		Percentage of tertiary sector (T32)	0.1971	0.042
		Urban travel intensity (T33)	0.1303	0.028
		Population density (T34)	0.3001	0.064
		Per capita road area (T35)	0.1299	0.028

and Figure 4). To improve the robustness of the results, a sensitivity analysis was performed. Specifically, the composite scores for each city indicator were recalculated by using the TOPSIS method and compared with the original measurements. Details can be found in Appendix B.

In the dimension of UUS indicators, the top five cities in terms of composite scores are Nanjing, Hangzhou, Shanghai, Shenzhen, and Beijing, and the bottom five are Guiyang, Nanning, Kunming, Luoyang, and Urumqi; in the dimension of URT indicators, the top five cities in terms of composite scores are Shanghai, Beijing, Shenzhen, Guangzhou, and Chengdu, and the bottom five are Nantong, Jinan, Taiyuan, Urumqi, and Dongguan. A comparative analysis of UUS and URT indicators reveals a strong coupling relationship between the two. Cities with extensive UUS development tend to exhibit more advanced URT systems. For instance, Beijing, Shanghai, and Shenzhen demonstrate both high UUS intensity and well-developed metro networks, primarily due to their early adoption of integrated urban planning and strong economic support. Conversely, cities like Urumqi and Luoyang rank lower in both indicators, reflecting their lower urban density and delayed investment in underground infrastructure. These findings underscore the critical role of urbanization level and economic conditions in shaping the synergy between UUS and URT.

In the composition of the integrated system of UUS and URT, URT indicators account for a higher proportion than UUS indicators. This is due to the fact that as a whole, the degree of development of URT in each city is not balanced and the indicators differ greatly. The sequence of URT development time is the main reason for this phenomenon. The population studied in this paper contains Beijing (1970), the first city in China to open a rail transit line, and Nantong (2022), which has just recently opened a rail transit line. The development time span of nearly 50 years has led to a huge gap between cities in terms of rail stations, line network size, and passenger flow intensity. The gap is also accelerated by the different levels of economic development of the cities.

Although URT currently plays a more dominant role in the integrated system, from a long-term and comprehensive perspective, the development of UUS is indispensable for improving urban land use, alleviating congestion, and enhancing environmental quality. Ideally, the two should form a mutually reinforcing interaction: the expansion of URT can stimulate the development of commercial and public functions in UUS, while the efficient utilization of UUS can provide additional support for URT construction, creating a synergistic effect.

4.2. Results of Urban Typology Classification

4.2.1. Description of Classification

Calculated by K-means analysis, the clustering effect is best when the K value is taken as 3 (

Table 3. Results of cluster category ANOVA difference comparison.

	Mean ± Standard Deviation
	Cluster_1 (n = 18)	Cluster_2 (n = 8)	Cluster_3 (n = 12)
UUS (T1)	0.08 ± 0.03	0.16 ± 0.05	0.04 ± 0.02
URT (T2)	0.12 ± 0.06	0.36 ± 0.09	0.10 ± 0.07
Urban development (T3)	0.11 ± 0.01	0.12 ± 0.01	0.07 ± 0.01

), and the sum of squared errors SSE = 31.836 (

Table A2. Cluster center iteration results.

	Initial Cluster Center			Final Cluster Center
	Cluster_1	Cluster_2	Cluster_3	Cluster_1	Cluster_2	Cluster_3
UUS (T1)	1.681	0.617	−1.176	−0.146	1.486	−0.772
URT (T2)	−0.386	2.326	1.870	−0.350	1.572	−0.522
Urban development (T3)	0.470	−0.746	0.278	0.446	0.880	−1.256

SSE = 31.836; average profile factor: 0.564.

). The proportion of each of the three parts is 47.37%, 21.05%, and 31.58%; the overall distribution is more uniform; and the clustering effect is better (Figure 5).

Table 4. Summary of basic information on clustering categories.

Clustering Category	Frequency	Percentage	Cluster Content
Developing	18	47.37%	Tianjin, Xian, Harbin, Suzhou, Zhengzhou, Foshan, Changsha, Ningbo, Wuxi, Qingdao, Fuzhou, Hefei, Shijiazhuang, Xiamen, Changzhou, Hohhot, Nantong, and Jinan
Mature	8	21.05%	Beijing, Shanghai, Guangzhou, Shenzhen, Wuhan, Nanjing, Chengdu, and Hangzhou
Underdeveloped	12	31.58%	Chongqing, Dalian, Shenyang, Kunming, Nanchang, Dongguan, Nanning, Guiyang, Urumqi, Xuzhou, Taiyuan, and Luoyang
Aggregate	38	100%	-

demonstrates the basic clustering results, which are classified into three categories:

• Developing cities (18 cities): These cities are characterized by moderate UUS development (0.08 ± 0.03), insufficient URT planning (0.12 ± 0.06), and overall good urban development (0.11 ± 0.01). Although the current level of UUS and URT development in these cities remains moderate, they exhibit significant potential for future growth. Notably, Tianjin, Suzhou, Xi’an, and other cities, leveraging their strong economic foundations, have made rapid progress in rail transit construction in recent years and demonstrate positive momentum for future development.
• Mature cities (8 cities): These cities are characterized by advanced UUS development (0.16 ± 0.05), well-planned URT systems (0.36 ± 0.09), and robust urban development (0.12 ± 0.01). This category includes some of China’s most developed cities, such as Shanghai, Beijing, and Shenzhen, which exhibit significantly higher levels of infrastructure development than other cities, as well as more balanced growth and strong capacity for sustainable development.
• Underdeveloped cities (12 cities): These cities are characterized by underdeveloped UUS (0.04 ± 0.02), inadequate URT planning (0.10 ± 0.07), and weak urban development (0.07 ± 0.01). Most cities in this category are in the early stages of UUS and URT development, with lower economic levels, smaller populations, and other limiting factors compared with the previous two categories. Two cities in this category require special consideration: Chongqing, as a municipality with a mountainous terrain and a vast non-central urban area, has statistical indicators that are significantly affected by its administrative classification, leading to a reported development level lower than its actual status. Meanwhile, Dalian, due to relatively low population density and a well-developed surface transportation network, has only opened three rail lines since 2003, as its urban development priorities do not focus on UUS and URT expansion.

4.2.2. Distribution of Cities

Figure 6 shows the spatial distribution of the three types of cities. It can be seen that developing and mature cities are mainly concentrated in the three major urban agglomerations of the Yangtze River Delta, Beijing–Tianjin–Hebei and Pearl River Delta. And the degree of integration of the Yangtze River Delta city cluster led by Shanghai is higher than that of other city clusters. This is mainly due to its location in the alluvial plain of the Yangtze River Delta, where the geology is dominated by soft soil layers and the terrain is flat, making the development of UUS and the construction of URT relatively convenient. Moreover, the distribution of cities in the Yangtze River Delta region is relatively dense, and the population flow and population density of the cities are high, resulting in higher demand for UUS and URT development. Underdeveloped cities are more evenly distributed, with undeveloped areas in the central and western regions predominating. This is mainly due to the insufficient financial funds caused by the low level of economic development, which makes it difficult to support the construction and operation costs of UUS and URT; secondly, the lack of population density and the lag of urban planning in the central and western cities also make the implementation and advancement of related projects limited [36].

4.3. Overall Measurement Results

Figure 7 demonstrates the overall ranking of integration, and the calculation results based on the VIKOR algorithm are shown in

Table A3. VIKOR calculation results.

City	Sum of Distance Ratios of Optimal Solutions S	Maximum Value of Distance Ratio of Optimal Solution R	Benefit Ratio Q	Program Ranking
Beijing	0.2858	0.0639	0.1076	4
Shanghai	0.2244	0.0448	0.0002	1
Tianjin	0.6374	0.1695	0.7103	14
Chongqing	0.6316	0.1283	0.5723	10
Guangzhou	0.34	0.0448	0.0854	3
Shenzhen	0.2991	0.0525	0.0802	2
Wuhan	0.4504	0.1264	0.432	7
Nanjing	0.4364	0.1456	0.4841	8
Shenyang	0.6982	0.17	0.7567	16
Dalian	0.7364	0.1868	0.8395	26
Chengdu	0.4312	0.0837	0.2792	5
Xian	0.5758	0.1299	0.536	9
Harbin	0.7179	0.1855	0.8217	23
Suzhou	0.5986	0.171	0.6863	12
Zhengzhou	0.622	0.1669	0.6904	13
Kunming	0.7711	0.185	0.8591	27
Hangzhou	0.3923	0.1252	0.3851	6
Foshan	0.6847	0.1969	0.834	25
Changsha	0.5675	0.1491	0.5924	11
Ningbo	0.677	0.1808	0.7762	18
Wuxi	0.6701	0.1908	0.8036	20
Nanchang	0.7152	0.1801	0.8021	19
Qingdao	0.6581	0.1751	0.7436	15
Fuzhou	0.6919	0.1865	0.8057	21
Dongguan	0.9009	0.1985	0.9989	38
Nanning	0.7345	0.1818	0.8218	24
Hefei	0.6831	0.1784	0.773	17
Shijiazhuang	0.7525	0.1915	0.8666	28
Guiyang	0.7838	0.19	0.8849	29
Xiamen	0.7041	0.1875	0.8177	22
Urumqi	0.8887	0.1988	0.991	37
Changzhou	0.7609	0.1969	0.8905	31
Xuzhou	0.7627	0.1958	0.8881	30
Hohhot	0.8101	0.1972	0.9278	33
Taiyuan	0.8605	0.1986	0.9694	36
Luoyang	0.8565	0.1978	0.9641	35
Nantong	0.7973	0.1987	0.9233	32
Jinan	0.8185	0.1957	0.929	34

. The top ten cities in the integrated ranking are Shanghai, Shenzhen, Guangzhou, Beijing, Chengdu, Hangzhou, Wuhan, Nanjing, Xian, and Chongqing, in this order, with most of them being at the top of the list in each indicator. However, there are exceptions; for example, Xian scores low in the UUS indicator, which is due to the fact that Xian is a famous historical and cultural city, with a large number of cultural relics sites and historical relics buried under the ground, which need to be strictly protected, restricting the scope of development of UUS [12]. Moreover, the urban expansion of Xian has long been mainly flat and epitaxial, and the concept of three-dimensional development has not yet been fully formed, which affects the development of UUS to a certain extent.

4.3.1. Analysis of Measurement Results

From the results of the basic data, it can be seen that UUS and URT have a certain connection; in order to verify this feature, we carry out Spearman correlation analysis on the indicator layer.

The results in

Table 5. Results of Spearman analysis.

	Mean	Standard Deviation	UUS (T1)	URT (T2)	Urban Development (T3)
UUS (T1)	0.084	0.051	1
URT (T2)	0.167	0.125	0.683 *	1
Urban development (T3)	0.100	0.020	0.559 *	0.432 *	1

* p < 0.01.

show that the correlation coefficients between T1 and T2, T1 and T3, and T2 and T3 are 0.683, 0.559, and 0.432, respectively, indicating that there are significant positive correlations among the three. This shows that urban development has a positive promotion effect on UUS and URT construction. In order to verify the correlation between UUS and URT more accurately and attenuate the influence of urban development indicators on the two, we carried out a partial correlation analysis. The final partial correlation coefficient ρ is 0.64.

Figure 8 exhibits that after controlling for urban development, there is still a strong and significant positive correlation between UUS and URT. However, its correlation coefficient is smaller than the initial correlation result (0.64 < 0.683), indicating that the relationship between the two is influenced by urban development and the influence is positive. The results show the existence of mechanisms of interaction between UUS and URT. This is mainly reflected in the aspects below.

• Complementary Urban Functions

UUS primarily addresses the issue of limited urban surface space by incorporating underground commercial areas, parking facilities, and public amenities. The development of UUS offers essential spatial support for URT, including stations and interchange facilities. Simultaneously, URT construction stimulates demand for underground space, fostering its rational layout and efficient utilization. In other words, URT generates the demand for UUS, while UUS ensures the necessary physical space for URT.

2.

Urban Activity Promotion

URT construction significantly enhances urban transportation efficiency and reduces commuting time, thereby fostering the concentration and development of social activities. Additionally, UUS facilitates new activities and social interactions by offering underground commercial, office, and recreational spaces. In this context, the underground space surrounding URT stations serves as a vital hub for commercial and service functions.

3.

Urban Environment Improvement

URT, as a low-carbon and environmentally friendly transportation mode, enhances the urban transportation system, thereby fostering a healthier and more convenient living environment. Meanwhile, UUS optimizes urban space utilization, reducing the need for surface construction and alleviating pressure on land resources. This also helps mitigate environmental issues such as the heat island effect. Collectively, the two contribute to the improvement in the urban environment.

4.

Planning Synchronization

Cities typically integrate UUS and URT planning to ensure coordinated development. For instance, in rail transit planning, designers proactively consider underground space utilization to enable seamless connections with subway stations and interchange hubs. The government ensures the coordinated development of UUS and URT through policy support, financial investment, and regulatory frameworks, thereby fostering efficient urban resource utilization and sustainable development.

4.3.2. Spatial Distribution Pattern

Figure 9 illustrates the overall distribution of cities, where a smaller value of the benefit ratio Q indicates a higher level of integration. It is evident that cities that actively develop UUS and URT are predominantly concentrated south of the Hu Huanyong line, in regions with a population share exceeding 90%. This region covers the economic core of China, with dense urban population, relatively high urban density and scale, a well-developed regional industrial structure, and an active economy. The cities with the top overall ranking in the level of integrated development of UUS and URT are in turn concentrated in the Triangle city cluster, the Pearl River Delta city cluster, the Beijing–Tianjin–Hebei city cluster, and the Chengdu–Chongqing city cluster. This is mainly due to the fact that these urban agglomerations are highly developed economically; are important clusters in various regions of the country; gather major social, scientific, and technological resources; and develop synergistically within the urban agglomerations, attracting a large number of people and generating demand for the construction of UUS and URT.

In northern China, cities not in the Beijing–Tianjin–Hebei city cluster have lower overall rankings. In addition to the factors of economic underdevelopment and underpopulation, its cold climate and permafrost environment increase the technical difficulty and construction costs of UUS and URT construction [49]. Moreover, compared with southern cities, northern cities have insufficient access to policy resources and financial support due to developmental positioning issues, making it difficult to promote integrated development.

4.4. International City Comparison

Numerous international studies have demonstrated that integrating UUS and URT enhances urban transportation efficiency while optimizing urban space utilization. For example, in the 1980s, Japan introduced urban planning strategies that incorporated underground transportation networks and underground space utilization [50]. Peng [51] noted that the development of Osaka’s URT led to a more intensive utilization of UUS. Around URT stations, UUS are utilized for commercial and transportation purposes, fostering economic and social development.

Faced with land constraints, Singapore has implemented an “underground city” development model. Its spatial planning integrates the URT system with underground entertainment and office spaces [52]. This three-dimensional development model centralizes urban functions within UUS, offering valuable insights for cities experiencing similar land and resource constraints.

In Europe, major cities such as Paris and London have exemplified successful integration of UUS and URT. In Paris, the metro system is seamlessly integrated with underground shopping malls, creating a multifunctional district centered on transportation [53]. In London, the recently completed “Crossrail” project has integrated newly constructed rail networks with existing underground spaces, fostering urban regeneration and regional economic growth [54].

However, some international cities exhibit inadequate integration of UUS and URT. For example, Vichiensan [55] examined transit-oriented development strategies in Bangkok and found that despite two decades of metro expansion, station-area development has not effectively alleviated urban congestion, thereby reducing overall city efficiency. This pattern parallels the challenges faced by “developing cities” in China.

A comparative analysis of international cities reveals that the deep integration of UUS and URT is essential to urban efficiency worldwide. Examining experiences across different countries and regions provides deeper insights into global trends and challenges in UUS–URT integration.

5. Conclusions

5.1. Research Conclusions

Unlike previous studies that focus on UUS or URT separately, this research study establishes a holistic integration framework to systematically evaluate the interaction between UUS and URT. By applying entropy weighting, the VIKOR method, and K-means clustering, we quantitatively assess the integration levels of 38 Chinese cities and classify them into three types: developing cities, mature cities, and underdeveloped cities. The main conclusions are drawn as follows.

1.

UUS development and URT construction are correlated with each other

UUS development is correlated with URT construction (ρ = 0.64, p < 0.01), and the correlation is affected by the level of urban development. Although this is not a causal relationship, the data of the 38 cities studied in this paper still verified the positive link between the two in a quantitative way. On this basis, the results of the partial correlation analysis indicate that if the city is in a benign development stage, the synergy between UUS and URT construction is stronger, while if the city’s development level is insufficient, the development of the two will be more isolated.

2.

URT construction dominates in integrated development

As we all know, urban construction is affected by economy, policy, population, and other aspects, and there is a big gap among cities. On the one hand, URT is an important part of urban construction, and on the other hand, its functional attributes can strengthen the connection of urban space and improve the efficiency of urban commuting. In the integrated comprehensive evaluation system of the sample cities, the weight of the URT indicator is the highest (0.531), while the weights of the UUS indicator (0.256) and the urban development indicator (0.213) are lower. This suggests that URT construction dominates the integrated development process and that strengthening URT infrastructure can be a catalyst for UUS development.

3.

The level of urban integration development is spatially heterogeneous

This paper divides the research object into developing cities, mature cities, and underdeveloped cities according to the scores of indicators of each dimension and the comprehensive evaluation results, accounting for 47.37%, 21.05%, and 31.58%, respectively. In terms of the overall pattern, the sample cities are mainly concentrated in the southeastern region, with very few cities in the northwest, and most of the cities in the northwest are classified as underdeveloped cities with lower comprehensive scores. This indicates that the overall distribution of urban space is extremely unbalanced, which is closely related to the laws of population migration and economic development in China. In terms of the pattern of city clusters, there is spatial agglomeration of different types of cities, that is, a regional pattern with mature cities as the core and developing cities surrounding them. For example, the city cluster formed by Shanghai and Nanjing (mature cities) and Suzhou, Hefei, Wuxi, and Changzhou (developing cities) covers the most important cities in the Yangtze River Delta region. Outside of the city clusters, it is difficult for isolated cities to promote integrated high-quality development. This disparity highlights the need for differentiated development strategies tailored to local conditions.

5.2. Suggestions

This study finds that the integration of UUS and URT is reflected not only in their significant positive correlation but also in the multiple benefits they jointly bring to cities. The integration of UUS and URT can effectively optimize urban space utilization, alleviating land scarcity on the surface. Additionally, by seamlessly connecting underground commercial spaces, public services, and parking facilities with rail transit, it significantly enhances travel efficiency and mitigates traffic congestion. This integrated model not only fosters urban economic development and commercial investment while driving industrial transformation and upgrading but also improves the urban environment and reduces carbon emissions, thereby promoting green and sustainable urban development. Therefore, urban land use and transportation planning should fully consider the synergy between UUS and URT, establishing a comprehensive and integrated planning framework.

This study provides tailored recommendations for three categories of cities in varying stages of integration, aiming to foster regional coordination and promote sustainable urban development:

• For underdeveloped cities, governments should implement targeted measures to reduce regional disparities and promote balanced urban development. Policies should prioritize infrastructure investment and financial support. The most effective approach involves increasing financial transfers, offering targeted subsidies and policy incentives, prioritizing investments in URT infrastructure, and implementing phased UUS development. Balanced development does not imply indiscriminate expansion; rather, cities should adopt a strategy aligned with their economic capacity, development stage, and actual needs. For example, in resource-depleted cities, UUS development can be integrated with the repurposing of abandoned mining sites, underground parking structures, or former air defense facilities.
• For developing cities, policies should prioritize optimizing the efficiency of existing infrastructure and progressively enhancing the integration between UUS and URT. Traffic congestion and energy consumption can be reduced by strengthening public transportation networks and promoting multimodal transportation systems. Urban planners should prioritize core urban districts, key transportation hubs, and high-density areas. The development of multifunctional complexes integrating rail transit stations with commercial, office, and public service facilities can enhance multilevel spatial planning and facilitate urban connectivity.
• For mature cities, integration efforts should align with national regional development strategies to strengthen the influence of core cities, expand metropolitan areas, foster coordinated city cluster development, and establish model cases. For instance, the cross-regional integrated planning of UUS development and URT projects can enhance planning efficiency, implementation effectiveness, and project coordination. Additionally, advanced digital tools such as GISs and BIM can be employed to develop a dynamic monitoring and evaluation system, enabling the real-time tracking of planning implementation and the assessment of construction outcomes.

5.3. Research Limitations and Prospects

UUS and URT construction influence each other and promote each other, and the synergistic development of the two is influenced by the level of urban development. From the results of this study’s integration measurement of the three dimensions of UUS, URT, and urban development, there are certain problems between city clusters and individual cities. However, due to the limited selection of indicators in the system, such as topographic and geomorphic features, citizens’ traveling habits, etc., it is difficult to achieve a comprehensive extraction of the current problems. In addition, data on UUS are difficult to obtain, and data from public government reports is inherently lagging; therefore, the study’s conclusions are difficult to match with the latest state of construction.

With the gradual slowdown of URT construction in China, government departments no longer approve new cities for subway construction. Therefore, in subsequent research, we will broaden the selection of study subjects by incorporating countries with advanced underground construction experience (e.g., Japan and Singapore). This expansion aims to refine the indicator framework and facilitate an in-depth exploration of the developmental patterns of various city types. In terms of research methodology, structural equation modeling and panel data analysis will be considered to further validate the causal mechanisms between UUS and URT. Additionally, the continuous expansion of cities introduces new technologies and transportation modes. Emerging innovations, such as Urban Air Mobility [56] and Automated Vehicles [57], are reshaping perceptions of urban space and transportation systems. Future research should further investigate the synergies between these technologies and traditional transportation systems to optimize the design and functionality of urban transportation and spatial planning.