Evaluating the Space Use of Large Railway Hub Station Areas in Beijing toward Integrated Station-City Development

Abstract

With the large-scale construction of high-speed railways and the continuous increase in population flows, railway hubs are becoming the most dynamic places in cities. As a key node of intercity traffic and an important part of urban development, railway hub stations are the main carriers for the implementation of the Integrated Station-City Development (ISCD) strategy. A comprehensive evaluation of the space use of railway hub station areas can provide a basic understanding of the intensive utilization and optimization of urban space. However, existing relevant studies lack a comprehensive assessment of the compound structures and functions within large railway hub station areas at the microscopic level. Therefore, this paper was guided by integrated station-city development, relying on Geographic Information Science (GIS)technology, and big data such as Points of Interest (POI) and real-time traffic, focusing on walking accessibility, facility convenience, function compound, and land development intensity used around railway hub station areas. The uses of the station areas in four large railway hubs in Beijing were analyzed. Based on this, we built an ISCD index, combined with the Analytic Hierarchy Process (AHP) method, and assessed the degree of ISCD in the four railway hubs. The study showed that among the four large railway hubs in Beijing, the Beijing North railway station offers the best walking accessibility. The Beijing railway station features the largest facility convenience, function compound, and land development intensity. In general, the levels of ISCD of the Beijing and Beijing North Railway Stations were significantly higher than those of the Beijing West and Beijing South Railway Stations.

1. Introduction

The 21st century witnessed the rapid development of urbanization all over the world. The global urbanization rate is expected to reach 67.2% in 2050 [1], and cities will become the main carriers of human living [2]. However, at the same time, urban traffic congestion [3], land shortages [4], and suburban spread are becoming increasingly serious issues [5]. The Transport-Oriented Development (TOD) model is seen as an ideal means to promote the sustainability of land use and urban development [6,7]. The TOD will not only help to reduce the dependence on private cars and achieve sustainable urban mobility [8], but also to drive the development of urban public transport nodes and areas along routes, shaping high-quality urban spaces [9]. China has become one of the countries with the most rapid railway development in the world. Large-scale railway construction has brought a large number of people, logistics, and capital flow to railway hub station areas [10]. However, due to the lack of planning and design, poor traffic organization, and the lack of coordination of land development, the station areas of most railway hubs face a series of problems, such as chaotic structural functions and separation from urban development. Therefore, it is necessary to deeply evaluate the structure and functions of railway hub station areas, and guide these to play an important role in optimizing urban land use, stimulating urban vitality, and driving urban economic growth.

Railway hub station areas are important transition places for transportation hubs and the city. Since the 1970s, the function of railway hubs has gradually expanded from connecting a variety of transportation to urban centers, covering transportation functions, consumption, dwell, and other functions [11]. Currently, railway hub station areas are developing towards becoming multi-functional, high density, urban complex centers [12]. Europe, the United States, and Japan have launched numerous practices to promote the renewal of hub station areas. Denmark stimulates its cities by enhancing the function of station areas, encouraging enterprises to settle within one kilometer of the railway stations, and building shopping centers near the railway stations to attract passengers to stay and consume [13]. The Netherlands has raised the optimization of hub station areas to the national strategic level, using it as a powerful means of promoting urban renewal and economic transformation.

The question remains as to how the station areas of railway hubs can be developed to promote positive interactions between railway hubs and cities. Indeed, this is a major challenge around the world. In the 1990s, European countries and the United States began to advocate the TOD model by allocating residential, office, and other functional sites in public transport station areas, to reduce motor vehicle use and the environmental pressure of the city [14]. The fundamental goal of the TOD is to address urban land use, energy, and environmental issues. However, as the population densities in many Asian cities, such as Tokyo, Hong Kong, and Singapore, are far greater than those of European countries and the United States, they hope that transportation hubs can become the engine of urban economic development and carriers of urban life. Therefore, based on the TOD theory of European and North America, Japan put forward a new idea of Integrated Station-City Development (ISCD). The ISCD is a highly composite model of utilization and agglomeration of urban development and community building centered around a railway hub. The goal of this model is to build the station areas of railway hubs into urban comprehensive centers with highly intensive land use, highly compound urban functions, high-end vitality of industrial forms, and a good image of station areas, so as to realize the integration of railway hub station areas and urban functions, facilities, land use, and landscapes [2].

The ISCD emphasizes the integration of the traffic functions of railway hubs and urban functions. The ISCD can build a high density and multi-purpose urban space by introducing high-quality infrastructures and new industries with the advantage of large population flows and good accessibility to railway hubs, so as to eliminate the problems of resource waste, poor convenience, poor connectivity with the city, and chaotic traffic order in current railway hub station spaces. Japan has implemented one of the most successful ISCD models in the world. Japan’s most prosperous neighborhoods almost all appear near railway stations. Shinjuku Station, Shibuya Station, and Ikebukuro Station have become the three urban centers of Tokyo through ISCD, driving the rejuvenation of the city through its stations [15]. The station area of Shinjuku Station has helped to develop the most prosperous business district in Japan, with countless restaurants, bars, clubs, and concert halls; Shibuya Station has developed into a key site of world popular culture by introducing creative industries into the station area and attracting many cultural facilities; by opening composite commercial facilities and upgrading leisure parks, the station area of Ikebukuro Station has become an urban composite center integrating multiple business forms, such as transportation, commerce, culture, residence, and education. Moreover, these three stations have also fully carried out the vertical development of underground and aboveground spaces, achieved the invisible effect of transportation hub facilities, and ensured the spatial landscape integrity of rail transit hub areas [16].

With the continuous implementation of the TOD and ISCD models, the spatial use assessments of station areas and related studies are constantly emerging [17]. Among these, the research on the built environment of transportation hub station areas [18,19] and the development-type division of hubs and station areas [20] are the most common. The spatial structure and business layout of transportation hub station areas are crucial to understand whether the public is willing to use public transport and whether station areas can develop hub economies [21]. Many studies have shown that the more suitable the completed environment is for walking, the more likely residents are to walk or use public transportation [22,23]. Therefore, most scholars believe that station areas of ideal railway hubs should present a hierarchical spatial structure based on walking accessibility. For example, a range of about 500–800 m away from the station is the core area with the largest land development intensity, mainly developing high-level business offices. A range of 800–1500 m comprises the expansion area, which is the supplement of the first circle function with relatively large land development intensity, mainly developing commercial offices, and supporting functions. A distance range exceeding 1500 m is the influence area. However, realistic station area usage is often far from the ideal structure. Based on this, some scholars have carried out relevant research on the single characteristics of land use [24,25], industrial development [26], or walking environments [27,28]. However, in general, in order to achieve the coordinated development of transportation, economy, environment, and society, the station areas of transportation hubs should possess several characteristics such as functional compounds, walkability, convenient facilities, and large land development intensity. Therefore, it is necessary to comprehensively study the structure and function of station areas.

The type of development division of hubs and station areas can be seen in part as a comprehensive study of the structure and function of station areas. Type division research mainly divides the train stations and station areas into different types according to their morphological and functional characteristics, so as to help decision makers develop differentiated and targeted policies [29]. Common types of classification research methods can be roughly divided into two categories. One method involves constructing the index system from a functional one-dimensional perspective, so as to calculate the TOD index of each site, and evaluate the TOD development status and future potential of different sites in a region [30]. For example, Singh [31] included indicators such as urban densities, land use diversity, walkability, and cyclability into the TOD index calculation of 21 railway stations in Arnhem and Nijmegen in the Netherlands, identifying aspects that needed to be improved for each station. Another method involves considering the "node-place" theory with dual characteristics, based on the “node-place" proposed by Bertolini. As a node of urban traffic network, a railway hub can bring in a large amount of people and promote the development of a station area. As important urban places, railway hubs and station areas can help to improve their own utilization efficiency, attract more people to use railways as a travel tool, and promote the development of nodes [32]. This method is mainly used to construct the index system from a two-dimensional perspective of location and function to judge whether node attributes are balanced with place attributes, thus distinguishing traffic hub types. At present, the improved version of this model has been applied to evaluate the integrated development level of land use, transportation, and design in the station area of transport hubs in different countries and regions such as Germany [3], the United Kingdom [33], and Shanghai, China [34]. Overall, the type of division research can help decision makers recognize the weak elements of station area development, helping to adjust development priorities in time and guide future train station design to maximize benefits. However, it is undeniable that the type division studies represented by both methods both almost simplify the station area of the hub to a homogenized region, failing to consider the complex characteristics of the internal structure of hub station areas, thus making it difficult to observe the interactive relationship between the transportation hub and the station area.

The impact of transportation on urban development has increased significantly since the 21st century. In particular, with the increasing global railway network and railway hubs becoming the most dynamic and cohesive places in a city, the ISCD model has become the future development trend. However, existing studies have mostly considered the impact of railway hubs on regions from a macro level perspective, studying the internal functional structure of railway hub station areas from the micro perspective, and performing less research to quantitatively evaluate the ISCD level of hubs. Therefore, this paper took four railway hubs in Beijing as examples and selected four important aspects of ISCD: walking accessibility, facility convenience, function compound, and land development intensity. An indicator system for the space use evaluation of station areas was constructed, and the structure and function of the station areas were studied systematically. On this basis, the ISCD levels of the four railway hubs were quantitatively evaluated by calculating the "ISCD index". The purpose of this paper was to evaluate the use of station areas, identify the deficiencies in ISCD, and provide policy recommendations for the development of the railway hub economy in China.

2. Study Area and Data Sources

This section mainly introduces the research area and data sources of the paper.

2.1. Overview of the Study Area

Beijing is one of the most densely populated capitals in the world, with a recorded permanent population of 21.89 million in 2020 [35]. During the years 2010–2020, the population of Beijing increased at a rate of 228,000 per year. The increase in population is currently exacerbating traffic congestion and land shortages in Beijing, and the intensive optimization and spatial reconstruction of urban centers are unstoppable. For historical reasons, Beijing’s railway hubs are located in the city center (Figure 1), making these the ISCD transportation hubs with the most potential in China. Moreover, since 2017, the Beijing Municipal Government has issued the Beijing Urban Master Plan (2020–2035) [36] and Beijing Regional Territory Spatial Plan [37], all proposed to strengthen the urban governance in the hub area, establish a coordinated development mechanism of transportation, land use, and urban functions, and optimize to the comprehensive benefits of transportation hubs.

As one of the most important railway node cities in China, Beijing connects the Beijing–Guangzhou, Beijing–Shanghai, Beijing–Kowloon, Beijing–Baotou, Beijing–Nantong, and Beijing–Harbin Lines, among others. During the years 2000–2018, the Beijing railway passenger capacity increased from 46 to 143 million–a nearly threefold increase [38]. Railway hubs are of great significance to Beijing’s development of the hub economy. The Beijing Railway Station, Beijing West Railway Station, Beijing South Railway Station, and Beijing North Railway Station are the four main passenger transport hubs in the "Four Main and Two Auxiliary" railway passenger transport hub systems in Beijing (

Table 1. Basic Overview of the Beijing, Beijing West, Beijing South, and Beijing North Railway Stations.

Station	Beijing Railway Station	Beijing West Railway Station	Beijing South Railway Station	Beijing North Railway Station
Grade	Special station	Special station	Special station	First-Grade station
Built-up area	80,000 m2	700,000 m2	320,000 m2	21,400 m2
Platform and line	Eight platforms and 16 lines	Ten platforms and 20 lines	13 platforms and 24 lines	Six platforms and 11 lines
Average daily passenger flow	180,000–300,000	180,000–200,000	150,000–250,000	10,000–15,000
Peak daily passenger flow	210,000	262,2000	217,000	30,000
Ordinary railway station or high-speed railway station	Ordinary railway and high-speed railway are at the same station	Ordinary railway and high-speed railway are at the same station	High-speed railway station	Ordinary railway and high-speed railway are at the same station
Arrival and departure trains	151	304	307	52
Main railway line	Beijing–Harbin railway, Beijing–Chengde railway, Beijing–Guangzhou railway, Beijing–Kowloon Railway, and Urban sub-center line of Beijing suburban railway	Beijing–Kowloon Railway, Beijing–Guangzhou railway, Beijing–Guangzhou high speed railway, and Beijing suburban railway and urban sub-center line	Beijing–Shanghai high speed railway and Beijing–Tianjin Intercity Railway	Beijing–Zhangjiakou high speed railway and Beijing–Baotou Railway

Notes: The grade refers to the Chinese railway station grade, including six grades such as a special station, first-grade station, second-grade station, third-grade station, fourth-grade station, and fifth-grade station. The special station has the highest rank, followed by the first-grade station.

). This paper took these as the research areas.

2.2. Data Sources

Considering the research needs, necessity, and availability of data, this paper mainly carried out the research with the advantage of the massive scale and high precision of multi-source big data [39], which are described as follows:

• POI (Points of Interest) data. The POI data used in this paper was derived from the 2018 data of Amap [40], which specifically included the coordinate information and names of catering services, shopping services, corporate enterprises, attractions, bus stations, and parking lots. There were 62,398 POI data obtained in this paper.
• Real-time traffic data. This paper took the exit of the railway hubs as the starting point, with each POI as the target point; through Python programming, a batch of navigation data from the starting point to each target point was requested from Amap. The fastest route and the minimum time consumption that were expected to reach the target point by walking were then selected. Since walking data were selected in this paper, daily peaks and holidays were not considered. The amount of real-time traffic data obtained was consistent with POI data, totaling 62,398 items.
• Building outlines data and road data in Beijing. All the data were obtained from Amap. Among them, the building outline data included building outline and building height. The road data included provincial roads, municipal roads, expressways, and other roads at all levels.

3. Methods of Study

This section mainly introduces the research area and data sources of the paper. This section also describes the overall research design, research methods, and specific research steps followed in this paper.

3.1. Study Design

Large railway hub station areas are important transition spaces connecting hubs and cities, with large flows of people and compound construction environments. From the perspective of countries, the ISCD model proposed in Japan is more suited to the development of station areas in cities with high population densities. The ISCD model involves carrying out the integrated planning and development of railway or subway hubs and their station areas to create high-intensity spaces with walkability, convenient facilities, and complex functions, so that railway hubs can increase the vitality of a city, promote urban economic growth, and produce positive interactions between the hub and the city. Taking ISCD as the goal, this paper attempts to find its existing shortcomings by exploring the space utilization of station areas, and propose targeted suggestions for future hub development.

The framework of this study consisted of three steps (Figure 2). The first step was to define the scope of railway hub station areas and list the use of station areas in previous studies in reference to TOD research. The second step was to screen the indicators, construct the index system, and collect relevant data. We evaluated the use of station areas from four perspectives: walking accessibility, facility convenience, function compound, and land development intensity. The third step was to combine the Analytical Hierarchical Process (AHP) method with the Delphi method, considering the walking accessibility, facility convenience, function compound, and land development intensity in the station areas. Next, we built the ISCD Index to assess the ISCD effect of the four stations in Beijing.

3.1.1. Building the Indicator System

Considering the basic logic of ISCD, the actual situation of China’s big cities and large railway hubs and the availability of data, this paper considered that ISCD needed to focus on the walking environment, service facility, functionality, and development strength of the station areas. An index system for evaluating the use of station areas at large railway hubs was also constructed (

Table 2. Evaluation Index System of Station Area for Large Railway Hubs.

One-Level Indicators	Two-Level Indicators	Description	References
Walking accessibility (A)	Walking time (A1)	Walking time with the exit of the railway station as the starting point	[29,41]
Facility convenience (B)	Public service convenience (B1)	POI density of public service facilities	[12]
	Consumer service convenience (B2)	POI density of public service facilities	[42]
	Recreation and entertainment convenience (B3)	POI density of recreation and entertainment facilities	[12]
	Employment convenience (B4)	POI density of corporate and business facilities	[12,27,42]
	Dwell convenience (B5)	POI density of dwell facilities	[12,27,42]
Functional complexity (C)	Functional complexity (C1)	The mixing degree of different functions, such as dwelling, business, consumption, leisure, and public services in the same space	[29,31]
Land development intensity (D)	Building density (D1)	Building density within a certain land area
	Floor-area ratio (D2)	Land intensive degree within a certain land use scope	[43]

). The index system included four one-level indicators, including walking accessibility, facility convenience, function compound, and land development intensity. It also included nine two-level indicators, such as walking time, public service convenience, and consumer service convenience.

The reasons for the index selection were as follows:

• Walking accessibility is the key feature of ISCD [44]. A shorter time to reach the railway station can attract a greater passenger flow and improve the utilization rate of railway stations.
• Facilities convenience reflects the dwelling environment of the railway hub station area. The layout of high density facilities can not only attract passengers to stay and consume, but also attract citizens to settle [45]. This paper divided POIs into five categories: public service, corporate business, dwelling, consumer services, recreation, and entertainment. The distribution density of various types of POIs can reflect the ease of use of the facility.
• Diversified functions can meet the different needs of passengers, which is the key to attracting passengers to stay, and determines whether the station city can operate in an integrated and efficient manner [46]. Considering that the traditional land use data were not sufficient to reflect the spatial layout of formats, this paper improved the land use mixing metrics to function compound and introduced POI data to assess the degree of urban function compound in station area.
• Land development intensity reflects the intensive degree of land use, which determines whether the station area can produce the maximum land value. The floor-area ratio and building density were used to quantify land development intensity.

3.1.2. Determining the Scope of the Station Area

The scope of the station area of the transportation hub is the influence range of a transportation hub on its surrounding space, and determining its boundary is conducive to a better study of the interaction between the hub and the city. Previous studies have mostly determined the station area as a circular buffer with a radius of 500 m [4], 600 m [25], 700 m [47], or 800 m [11] based on the time that passengers are willing to walk to the site. However, some scholars have raised doubts about this [48,49]. For example, Yang [48] et al. found that the impact boundary could extend to 1250 m for a 15 min walk, by studying the effect of subway stations on the strength of land development intensity.

However, most of these studies targeted bus stops or subway stations. Compared with bus stops and subway stations, railway hubs feature essential differences in terms of building volume, human flow, and driving radiation capacity to the surrounding areas. The range of railway hub station areas is generally much higher than that of subway stations or bus stations. We interviewed ten experts in the transportation field and ten graduate students in urban and rural planning projects who often chose railway travel. The purpose of the interviews was to generate suggestions for the delimitation of the scope of the station area from the perspective of experts and passengers. The question in the interview was: "if you need to stay at the railway station for half a day and wait for the next train, what is the maximum walking time you can accept to walk from the railway station for entertainment or consumption?". To ensure consistency, we provided options for experts to choose from: 30 min, 40 min, 50 min, and 60 min. More than 90% of respondents thought that 40 min was the maximum acceptable walking time and could be roughly designated as a circular buffer with a radius of three kilometers. To highlight the impact of walking time on the railway hub, we divided 10 min, 20 min, 30 min, and 40 min isochrones based on walking accessibility within the buffer zone, with the railway station exit as the center.

3.2. Evaluation of Large Railway Hub Station Area Use

According to the index system, different methods were introduced separately to assess the walking accessibility, facility convenience, function compound, and land development intensity of the station areas.

3.2.1. Walking Accessibility Assessment

The walking accessibility assessment mainly evaluates the time cost of walking to other sites from a given location and can be used to reveal the connectivity between hubs and cities [41,50]. The walking accessibility assessment in this paper mainly combined Amap’s big data with ArcGIS. The Amap data could automatically plan the shortest route and time of the walk according to the start and end of the walk route. Moreover, the pedestrian navigation routes of Amap adopted artificial intelligence technology, which fully considered traffic lights, the number of intersections, and overpasses, as well as underground passages, to estimate the walking time more accurately. This has since been used in regional traffic analysis, urban agglomeration analysis, urban traffic convenience analysis, public service facility accessibility analysis, and life convenience evaluation.

First, based on the walking navigation data suggested by Amap, the walking time and distance of the exit of several POIs to the station areas was obtained. Second, reverse distance space interpolation with ArcGIS 10.5 software enabled the determination of the walking time from the exit to any location within the station area. To better reflect the effect of walking time on station area, the isochronous cycle of 10 min, 20 min, 30 min, and 40 min times were constructed at ten minute intervals. Finally, to quantitatively evaluate the walking accessibility, we calculated the walking distances that could be achieved. The larger the walking accessibility in a fixed time, the better the accessibility would be, and vice versa. The calculation formula is as follows:

$$R={\displaystyle \sum }_{n=1}^n{a}_i$$

(1)

where R is the walking range within a fixed time; a_i is the unit area of the grid i in the accessible area, and the n is the total number of grids in the accessible area.

3.2.2. Facility Convenience Assessment

Facility convenience assessment is an assessment of the facility configuration density of public services, consumer services, recreation, and entertainment in the station areas of the railway hubs. First, for research purposes, the POI data were regrouped into five categories: public service, corporate business, dwelling, consumer services, recreation, and entertainment. The spatial distribution of the POIs was then analyzed by the kernel density estimation and the standard deviation ellipse.

The kernel density estimation stems from the first law of geography. The closer to the core element, the greater the density extension value that is obtained. The method is a nonparametric test used to estimate an unknown probability density function, which enables deep-level feature rule information mining of the spatial feature distribution. This paper introduces a method to determine the distribute the characteristics of each type of POI in the study area. The higher the kernel density is, the more concentrated the distribution will be [51]. The calculation formula is as follows:

$$f\left(x,y\right)=\frac{1}{n{h}^2}{\displaystyle \sum }_{i=1}^{n}k\left(\frac{d_i}{n}\right)$$

(2)

where f (x, y) represents the kernel density value of the point (x, y); h represents the bandwidth or smoothing parameter; k represents the kernel function; and d_i represents the distance between the point (x, y) and the i-th observed position.

Standard deviation ellipses are important methods with which to study the direction and characteristics of the elemental distributions of spatial points. The standard deviation ellipse consists of three elements: the standard deviation along the long axis, the standard deviation along the short axis, and the angle of rotation. The size of the ellipse reflects the overall concentration of the spatial pattern, and the long half axis reflecting the dominant direction of the pattern [52]. The calculation formula is as follows:

$$SD{E}_x=\sqrt{\frac{{{\displaystyle \sum }}_{i=1}^n{\left(x_i-\overline{X}\right)}^2}{n}}$$

(3)

$$SD{E}_y=\sqrt{\frac{{{\displaystyle \sum }}_{i=1}^n{\left(y_i-\overline{Y}\right)}^2}{n}}$$

(4)

where x_i, y_i represent the coordinates of element i, {$\overline{X}$, $\overline{Y}$} represents the average center of the element, and n is equal to the total number of elements.

3.2.3. Function Compound Assessment

The function compound is the key to improving the socio-economic benefits of a railway hub [53]. The purpose of functional composite evaluations is to evaluate the mixing degree of different functions, such as the residence, commerce, consumption, and culture in the same space. Compared with traditional land use data, POI data offer a finer spatial division, which can more accurately reflect the spatial distribution of human activities, and offer great convenience to the computational function compound [54,55]. Therefore, based on the distribution density of various POI, a binary method was used to determine whether the grid possessed a certain function. If the density of a grid POI was greater than 50%, the grid was considered to possess the function and the value was 1. Otherwise, the function was not considered and the value was 0. Finally, five additional functions were analyzed to obtain their degree of mixing. The calculation formula is as follows:

$$F={\displaystyle \sum }_{j=1}^5{d}_j$$

(5)

where F indicates function compound, with a value between 0 and 5, and a value of 5 means that the area includes a total of 5 functions, and the other values are the same. The value d_j indicates whether the grid possesses a certain function, and the values are 1 and 0, meanwhile the value 1 indicates that it possesses a certain function.

3.2.4. Land Development Intensity Assessment

Land development intensity is used to characterize the intensification of land use in station areas. Building density and floor-area ratios were used as important indicators for the assessment of land development intensity in this paper. Building density was used to reflect the clearing rate and building density within a certain range of land use. The floor-area ratio was used to measure land use intensity [9]. To quantitatively assess the land development intensity, this paper divided the plots based on the road data and added the building profile data to estimate the floor-area ratio and building density around the railway hubs with a superposition. The calculation formula is as follows:

$$FAR=\frac{T_{area}}{C_{area}}$$

(6)

$$BD=\frac{B_{area}}{C_{area}}$$

(7)

where FAR represents the floor-area ratio; BD represents the building density; T_area represents the total building areas above the ground; B_area represents the total base areas of the building; and C_area represents the area of the plot.

3.3. ISCD Level Assessment

In this study, we evaluated the level of ISCD and constructed an ISCD index by comprehensively considering the levels of walking accessibility, facility convenience, function compound, and land development intensities of hub station areas. However, integrating factors with four different criteria is a major challenge. To meet this challenge, we first standardized different data, before introducing the AHP method to calculate the weights of different factors according to the expert score, and finally conducted a spatial superposition to obtain the integrated station-city development index. The calculation formula is as follows:

$$ISCD={{\displaystyle \sum }}_{j=1}^9{V}_{ij}\times W_{ij}$$

(8)

where ISCD indicates the integrated station-city development index (ISCD index); V_ij represents the standardized value of the j factors on the i-th grid; and W_ij represents the weight of the j factors on the i-th grid. The value range of V_ij is [0, 1]. The calculation formula is as follows:

$$V_{ij}=\frac{V{V}_{ij}-V_{min}}{V_{max}-V_{min}}$$

(9)

where VV_ij indicates the i-th index value of grid j; V_max represents the maximum value of the i-th index of grid j; and V_min represents the minimum value of the i-th index.

The weights were calculated using the Analytic Hierarchy Process (AHP) method and the Delphi method. The AHP method is a subjective and objective method which was proposed by American operations research scientist T.L. Saaty in the 1970s. It can be broken down into complex multi-objective research objects at a multi-level structure, comparing the evaluation indexes at each level, determining the relative contribution rate of each evaluation index at each level, and further converting it into the weight of an evaluation index.

The weight calculation included three steps. The first step was to interview three experts, two of whom are from the field of urban and rural planning, and one from the field of geography. We constructed the pairwise matrix according to the experts’ opinions (

Table 3. Pairwise Matrix and Weight of Sub-Criteria.

	Criteria	A	B	C	D	Weight
A	Walking accessibility	1	½	⅓	¼	0.095
B	Facility convenience	4	1	3	2	0.467
C	Function compound	2	⅓	1	½	0.160
D	Land development intensity	3	½	2	1	0.278
CR = 0.0114
	Sub-criteria	B1	B2	B3	B4	B5	Weight
B1	Public service convenience	1	⅓	2	½	½	0.125
B2	Consumer service convenience	2	1	4	2	1	0.311
B3	Recreation and entertainment convenience	1	⅓	1	½	⅓	0.100
B4	Employment convenience	3	½	3	1	1	0.241
B5	Dwelling convenience	2	½	3	1	1	0.223
CR = 0.0669
	Sub-criteria	D1		D2		Weight
D1	Building density	1		½		0.333
D2	Floor-area ratio	2		1		0.667

) and calculated the product M_i of each row element in the pairwise matrix and the n-th power root w_i of M_i.

$$M_i={\displaystyle \prod }_{j=1}^n{c}_{ij}\left(i=1,2,\cdots ,n\right)$$

(10)

$$w_i=\sqrt[n]{M_i} \left(i=1,2,\cdots ,n\right)$$

(11)

The second step was to determine the preliminary weight of each index. The weight calculation process can be regarded as standardizing the vector w_i (w₁, w₂, …, w₉):

$$w=\frac{w_i}{{{\displaystyle \sum }}_{i=1}^n{w}_i}\left(i=1,2,\cdots ,n\right)$$

(12)

The third step was to check the consistency of the pairwise matrix. The purpose of the consistency test was to reduce the weight calculation error caused by inconsistent judgment thinking. The consistency test of indicators needed to make the consistency ratio CR < 0.10. If CR < 0.10, then the weight coefficient of each indicator was effective. Otherwise, the judgment matrix needed to be reconstructed. The calculation formula is:

$${\lambda }_{max}={\displaystyle \sum }_{i=1}^n\frac{{(BW)}_i}{n{w}_i}$$

(13)

$$CI=\frac{{\lambda }_{max}-n}{n-1}$$

(14)

$$CR=\frac{CI}{RI}$$

(15)

where λ_max is the maximum eigenvalue and RI is the random consistency index. When n = 2, RI = 0; when n = 3, RI = 0.58; when n = 4, RI = 0.9.

Finally, the final weight of each factor was obtained by multiplying the weight of the criterion layer and the target layer (Figure 3).

4. Results

This section describes the walking accessibility level, facility convenience level, function compound level, and land development intensity of the four railway hubs in Beijing. Moreover, we also constructed the ISCD index to evaluate the degree of the Station City integration of four railway hubs.

4.1. Walking Accessibility Levels of Four Railway Hub Station Areas in Beijing

Walking accessibility is closely related to the radiation range of rail hubs, but it needs to be improved in all three other railway hubs except for Beijing North Railway Station (

Table 4. Walking Accessibility of Four Railway Hubs in Beijing at Different Times (unit: km²).

Walking Isochron	Beijing Railway Station	Beijing West Railway Station	Beijing South Railway Station	Beijing North Railway Station
0–10 min	0.54	0.56	0.39	0.80
10–20 min	1.77	2.55	2.19	2.95
20–30 min	4.22	4.49	3.89	5.45
30–40 min	6.91	6.63	6.51	7.42

). For a 10 min walk, Beijing North Railway Station features the best walking accessibility, with a range of 0.80 km². Beijing South Railway Station, on the other hand, features the worst walking accessibility, with a range of 0.39 km². The ten-minute reachable ranges of Beijing and Beijing West Railway Stations are 0.54 km² and 0.56 km², respectively. With 40 min as the maximum time considered as acceptable for walking, the Beijing North Railway Station still offers the maximum reach range at 16.62 km², accounting for 54% of the total station area. Beijing South Railway Station features the smallest reach range, at only 12.98 km², about 42% of the total station area. The accessible ranges of Beijing and Beijing West Railway Stations are 13.44 km² and 14.23 km², respectively, also accounting for 47% and 50% of the total site areas, respectively.

From the spatial distribution of the walking accessibility (Figure 4), the walking isochron is in the same direction as the main urban streets. Beijing West Railway Station and Beijing North Railway Station extend from East to West; Beijing Railway Station extends from North to South, and Beijing South Railway Station extends in three directions: North, East, and West.

4.2. Facility Convenience Levels of Station Areas for Four Railway Hubs in Beijing

The distribution status and convenience level of the facilities around railway hubs can give full play to the agglomeration effect of railway hubs, and promote the optimization and adjustment of the spatial structures of the surrounding areas. From the perspective of the number and proportion of POIs (

Table 5. Number and Proportion of Points of Interest (POI) around the Four Railway Hubs in Beijing.

POI Classification	Beijing North Railway Station		Beijing Railway Station		Beijing South Railway Station		Beijing West Railway Station
	Quantity	Proportion	Quantity	Proportion	Quantity	Proportion	Quantity	Proportion
PS	8797	42.9%	9603	36.9%	6478	44.5%	6307	44.0%
CB	6107	29.8%	8189	31.5%	3288	22.6%	3559	24.8%
D	1524	7.4%	1511	5.8%	941	6.5%	935	6.5%
CS	3354	16.4%	5544	21.3%	3191	21.9%	2932	20.4%
RE	725	3.5%	1160	4.5%	646	4.4%	609	4.2%
Total	20,507		26,007		14,544		14,342

Notes: PS: public service; CB: corporate business; CS: consumer services; D: dwelling; RE: recreation and entertainment

), the greatest number of POIs was found in the Beijing Railway Station area, reaching 26,007, followed by Beijing North Railway Station, with a total of 20,507. The Beijing West Railway Station and the Beijing South Railway Station demonstrated relatively few of these, with 14,342 and 14,544, respectively. The POIs around the four railway hubs were found to be mainly public service facilities and corporate business facilities, accounting for about 70% of the total. The Beijing Railway Station featured more POIs in business, consumer services, recreation and entertainment, and public services than the other three stations. The Beijing North Railway Station featured more dwelling POIs than the other three stations.

According to the kernel density distribution of various types of POI, the POI concentration degrees in the ten-minute walking isochron of the Beijing and Beijing North Railway Stations were better, and the concentration degrees of the Beijing West and Beijing South Railway Stations were slightly worse (Figure 5). From the perspective of the kernel density of consumption services, there were consumption services concentrated within a ten-minute walk of the four stations, especially at the Beijing North Railway Station. From the perspective of the kernel density of recreation and entertainment, except for the good concentration degree of the Beijing Railway Station, the other three stations did not form an obvious agglomeration area in the circle of a 20 min-walking isochron. In terms of the kernel density of public service facilities, the Beijing North Railway Station formed an obvious agglomeration in a ten-minute walking isochron and for the 10–30 min walking isochron, the Beijing Railway Station and Beijing North Railway Station featured large agglomeration areas. From the perspective of the kernel density of businesses, Beijing North Railway Station formed a large agglomeration area in the ten-minute walking isochron, indicating that the employment density of the Beijing North Railway Station area was relatively large, and those of Beijing and Beijing West Railway Stations also featured a certain agglomeration in the ten-minute walking isochron. From the perspective of the kernel density of dwelling services, a belt agglomeration area was formed in the West of the Beijing North Railway Station, and a circular dwelling agglomeration area was formed outside the Beijing Railway Station area along the 30 min isochron.

4.3. Function Compound Levels of Station Area for Four Railway Hubs in Beijing

In terms of the overall compound level of the station areas, the function compound level of the Beijing Railway Station was the best, followed by Beijing North Railway Station (Figure 6). The station area of the Beijing Railway Station formed a large area of five functions, dwelling, working, consumption, recreation and entertainment, and the Beijing Railway Station formed four-to-five kinds of function compound spaces in a circle of ten minutes in the walking isochron. The function compound of the Beijing North Railway Station area was second only to that of Beijing Railway Station. Multiple complex spaces with four functions were formed within the Beijing Railway Station area, with a scattered distribution. Moreover, the Beijing North Railway Station was close to the railway station exit with a high function compound. The function compounds of the Beijing West and Beijing South Railway Stations were generally poor. Although there were five function compound areas sporadically distributed in its station area, no strong function compound area was formed in the ten-minute distance in the walking isochron, which was not conducive to attracting passengers to stay for consumption to develop the hub economy.

4.4. Land Development Intensity of Four Railway Hub Station Areas in Beijing

The overall land development intensity of the four railway hubs in Beijing was not high (Figure 7). From the perspective of the floor-area ratio of the station areas, the floor-area ratio of the Beijing Railway Station area was the highest, with an average of 2.02. The floor-area ratios of the Beijing North and Beijing West Railway Stations were 1.61 and 1.59, respectively, while the floor-area ratio of the station area of Beijing South Railway Station was only 1.33. From the perspective of the spatial distribution of floor-area ratios, except for the sporadic plots near the Beijing Railway Station where the floor-area ratio was greater than 3, the floor-area ratios of the other three stations close to the hub were generally low.

In terms of the building density of the station areas, the building density of the station area of the Beijing Railway Station was the largest, with an average value of 0.32, while that of the Beijing North Railway Station was 0.28. The average values of the Beijing West and Beijing South Railway Stations were 0.24 and 0.23, respectively. From the perspective of the spatial distribution of building density, the building density on the southeastern side of the Beijing North Railway Station was the highest, reaching more than 0.35. The building densities on the west and north sides of the Beijing Railway Station were high, both greater than 0.25, and on the north side, the building density was greater than 0.35. Field research indicated that the surrounding commercial offices were mostly public service facilities, indicating that the commerce surrounding the Beijing Railway Station was prosperous, offices were convenient, and it was convenient to go to school, obtain employment, and seek medical treatment. The areas with higher building densities by the Beijing West Railway Station were distributed in a ring shape within 20–30 min walking isochron and with a building density between 0.25 and 0.35. Field surveys found that the surrounding office buildings, administrative buildings, commercial buildings, and educational facilities were in the majority. The building densities on the north and south sides of the Beijing South Railway Station were between 0.25 and 0.35, and their distribution was relatively scattered. The field survey also found that the majority of buildings were commercial buildings, residential buildings, and exhibition and sightseeing buildings.

4.5. ISCD Levels of Four Railway Hub Station Areas in Beijing

The ISCD levels of Beijing Railway Station, Beijing West Railway Station, Beijing South Railway Station, and Beijing North Railway Station differed greatly, with Beijing and Beijing North Railway Stations being significantly better than Beijing West and Beijing South Railway Stations (Figure 8). The Beijing Railway Station demonstrated the highest level of ISCD, which was 0.218, forming an area with a good interaction effect between multiple stations and cities in the station area. Beijing North Railway Station was second only to Beijing Railway Station, and its ISCD index was 0.201. However, compared with Beijing Railway Station, Beijing North Railway Station was within the ten-minute walking isochron, and the ISCD index had reached 0.300, which was significantly higher than that of the other three stations. The Beijing West and Beijing South Railway Station ISCD indices were low, at 0.157 and 0.149, respectively. Beijing West Railway Station formed a good area for ISCD within a 30 min walking isochron, but the ISCD effect was not significant enough. The station area of the Beijing South Railway Station only featured only a few stations with good interaction effects, and the area was small, but the ISCD index on the 10 min isochron was higher than that of the others.

5. Discussion

This section further discusses the deficiencies and reasons for the station area of Beijing’s large railway hub, puts forward policy suggestions, and finally explains the limitations of the research.

5.1. Insufficiency and Reasons for the Development of Station Areas of Large Railway Hubs in Beijing

Large-scale railway hubs are important nodes connecting traffic networks inside and outside cities, with significant passenger flows. They are also gateways and landmarks, presenting an image of the city. In the era of ISCD, railway hubs and their station areas are becoming an open and multi-functional centers of urban life. However, through our study, it appears that there is still a gap between the four major railway hubs in Beijing, compared with cities such as Tokyo, Japan, and Hong Kong, China. For example, in terms of walking accessibility, Japanese railway stations mostly connect the exit of the railway stations to the surrounding commercial facilities through corridors, while Beijing West and Beijing South Railway Stations mostly contain underground passageways, with a long distance and a poor associated experience. In terms of land development intensity, the floor-area ratios of Osaka, Tokyo, and Hong Kong in China have now reached 8, but that of Beijing is only between 2 and 3 [56].

The urban properties and site selection of Beijing are the main reasons for the restriction of ISCD. As a famous tourist city, Beijing features many scenic spots and parks. For example, Beijing West Railway Station is situated close to the Lotus Pond Park, and the Ming Dynasty Wall Relics Park is close to the south of Beijing Railway Station; these affect the land development intensity of the station areas to a certain extent. At the same time, the walking accessibility, facility convenience, and function compounds of these parks and scenic spots form an obvious low-value area. This shows that although these park attractions are located close to railway hubs, the latter still attract very few passengers. The Beijing South and Beijing North Railway Stations were upgraded in 2008 and 2020, respectively, to open to the public. Although the Beijing South Railway Station integrated the ISCD idea in its design process, the results were still unsatisfactory because the station area of the Beijing South Railway Station is mainly residential, and other supporting facilities could not be updated with the renovation of the Beijing South Railway Station. However, as a post-transformation station, the ISCD degree of the Beijing North railway station is clearly better. The main reason for this is that the Beijing North railway station is close to the Xizhimen Station, a subway station with the largest passenger flow in Beijing. The development of subways has driven the surrounding business, residential, and consumer facilities, forming a high degree of mixing of land use and composite urban functions. Moreover, in the process of transformation, the Beijing North Railway Station has paid attention to the design of walking area streamlines, which makes it convenient for passengers to transfer and quickly enter the surrounding shopping malls and companies. The opening of Beijing North Railway Station has significantly enhanced the competitiveness and cohesion of the area.

In addition, China’s railway industry started late, and the development of railway hub station areas was not on the agenda until the early 2000s. From the perspective of the development stage of the railway hubs, the four stations in Beijing had only achieved the interconnection of various transportations and convenient transfer. There is still a long way to go to fully achieve the integration of stations and cities. However, as early as the 1960s, Japan began to build its hubs into high-intensity, high-density, and compact compound urban centers, and successfully developed a number of successful cases, such as Tokyo, Shibuya, and Osaka stations.

5.2. Policy Implication

In recent years, China’s rapid high-speed railway construction has been promoting the construction of new cities and land use renewal in urban suburbs and old cities [57]. The development potential of railway hub station areas has increased dramatically, and it is necessary to explore the Chinese model of ISCD-oriented railway hub station area development. Moreover, railway hubs and station spaces, as the core areas of urban renewal in the future, have been incorporated into the urban development strategy. Relying on its huge advantage of large population flows, building high-density and multi-purpose urban spaces and improving the service quality of station spaces can not only effectively display the city’s image and improve the possibility of people revisiting, but also promote the multi-center development of the city. However, according to our research, ISCD still faces many bottlenecks in China. This section suggests policies for overcoming this development bottleneck and better integrating the ISCD strategy into future urban development plans.

The suggestions are as follows:

• Improving the connectivity between railway hubs and cities. In the future, a three-dimensional pedestrian network streamline connecting the railway hub and the station area should be built as soon as possible, so as to shorten departure times and improve walking efficiency, so that passengers can reach different POIs, such as consumer services and public services, in the station areas through the shortest possible walking time.
• Building a barrier-free and inclusive environment. In the planning and design of railway hubs, we should not only consider the needs of average people, but also take care of people with reduced mobility.
• Optimizing the built environment of station areas. Focusing on attracting enterprises and businesses through renovation and redevelopment within a ten-minute walking isochronous circle of the railway hubs, and especially attracting cultural industries, new businesses, and high-quality houses with core competitiveness, so as to build an urban public center and meet people’s high-quality living needs.
• Introducing relevant policies as an important guarantee to promote the integration of stations and cities. In the next step, we should first combine the top-level design with planning practices, formulate relevant specifications and standards of ISCD, determine the operation modes and management mechanisms, and better ensure the promotion of Station City integration in the face of the current problems of China’s railway hubs.

5.3. Research Uncertainties

Although this paper combined field research with big data to evaluate the spatial use at ISCD levels of four railway hubs in Beijing, there were still some uncertainties in the study. First, by being limited to obtaining data at the micro level, we only evaluated four main aspects in the selection of indicators, and abandoned some meaningful indicators for which it was difficult to obtain data, such as the proportion of jobs and housing in the station spaces and the number of jobs provided in the station spaces, which led to our research not being comprehensive enough. On the other hand, however, our index system construction offered the huge advantage of data accessibility and could be repeated in any city around the world to assess the use of station areas and the evaluation of their ISCD level. Second, we interviewed experts in the fields relevant to the study, defined the scope of the station areas according to these expert opinions, and invited them to participate in the determination of the index weights. However, expert opinions may be different from the priorities of ordinary citizens. In future research, the travel behavior of citizens could be included to enhance the value and practicality of the research. Thirdly, the POI data used in this paper are point data. This method is more precise than using land use data in characterizing the spatial distribution density and distribution form in the horizontal and vertical dimensions, which helped us to study the mixing degree of different business formats. However, it is undeniable that POI data lacks area attributes. In this study, it is likely that 100 square meters of POIs was equivalent to ten square meters of POI, which caused further uncertainty. Fourth, in the process of promoting the integration of stations and cities, the passenger flow of railway hubs includes not only the passing passenger flow at the stations, but also the flow of purposefully shopping passengers. The demand of the two is likely to be greater than the supply of the railway hub. However, due to the limited availability of data, we cannot predict how many POIs should be configured around the railway hub to meet people’s future consumption needs. Fifth, when we considered people’s walking times, we took the walking time of average people as the standard, and did not consider people with reduced mobility.

6. Conclusions

The ISCD model is an innovative practical application of TOD theory in cities with high population densities. So far, the ISCD model has been successfully applied in Japan, Hong Kong, and Singapore, and has made a clear contribution in driving urban economic growth and alleviating the shortage of urban land use. With the large-scale construction of high-speed railways in China, railway hub station areas, as important transition places between link hubs and cities, are facing a new round of planning, design, and renewal. However, existing studies lack a comprehensive evaluation of the complex structure and functions within large railway hub station areas at the microscopic level. Therefore, this paper took four large railway hubs in Beijing as examples; the spatial use of the station areas was analyzed in terms of walking accessibility, facility convenience, function compound, and land development intensity. Considering these four factors comprehensively, the ISCD index was constructed to evaluate the level of ISCD for the four railway hubs.

The results show that walking accessibility was the best at Beijing North Railway Station, and its facility convenience, function compound, and land development intensity were better than those of the other three stations. In terms of ISCD degree, Beijing and Beijing North Railway Stations were significantly better than Beijing West and Beijing South Railway Stations. The station area of Beijing Railway Station has formed a number of stations with a good interaction effect. Beijing North Railway Station initially featured ISCD characteristics in the ten-minute walking isochron. However, the ISCD effects of Beijing West and Beijing South Railway Stations were not obvious because of their low employment density and poor function compound. In the future, to improve China’s ISCD, it will be necessary to improve the connectivity between railway hubs and cities, build a barrier-free and inclusive environment, optimize the built environment of the station area, and issue relevant guarantee policies to promote the integration of stations and cities.