A review of research methods on the coupling relationship between urban rail transit and urban space: revealing spatiotemporal relationships through big data

ABSTRACT

Urban rail transit (URT) systems play an evident role in shaping city spatial structures; however, the principles and mechanisms behind this influence are not fully understood. This paper reviews research progress on the coupling relationship between URT and urban space, focusing on big data analysis methods and the timeliness and sequence of coupling effects. It highlights the importance of the temporal dimension in coupling analysis. By thoroughly exploiting data value and extracting key elements, big data technology imparts temporal attributes to these elements, exploring their interaction and influence mechanisms over different time sequences. The paper also discusses the potential application of big data to urban planning to support sustainable urban development. Finally, the paper outlines future research directions, including the deepened application of big data to urban spatial analysis and the role of new data sources in understanding and shaping the coupling relationship between URT and urban space. This analysis offers new perspectives and methodologies for urban development and transportation planning.

KEYWORDS:

• Urban rail transit
• urban space
• coupling relationship
• functional elements
• data approach

1. Introduction

Presently, over half the global population lives in urban areas, with that figure expected to reach 70% by 2050 (BANK Apr 20; 2022). This urban expansion has introduced complex interactions and dependencies into various urban elements, complicating our understanding of urban dynamics. Urban rail transit (URT) systems, a pivotal element of modern urban infrastructure, significantly influence urban spatial development and support overall growth. They are considered a catalyst for urban revitalization because their construction and development can stimulate economic vitality along their routes, increase land value, and promote more environmentally friendly, efficient urban development patterns.

However, without careful planning and design, URT systems can lead to disordered urban expansion and residential migration, exacerbating social inequality and spatial segregation. The unclear coupling relationship between URT and urban space, along with uncertainties in the timeliness and sequencing of these effects, further complicates the understanding and management of their impact.

To understand this relationship in depth, this paper extensively reviews the utilization of big data in modern urban studies. The advent of big data in the digital era has revolutionized urban studies, offering profound insights into urban complexities, particularly through its temporal attribute information. Diverse data sources, including government records, mobile apps, social media, smart sensors, geographic technologies, and the Internet of Things, have significantly advanced empirical urban research (Xia et al. 2022). Leveraging the analytical prowess of big data, we mined the data value and extracted the information it embodies to better analyze the coupling relationship between URT and urban space.

2. Methodology

2.1 Bibliometric analysis

To conduct a comprehensive analysis of the coupling relationship between URT and urban space, researchers must perform a detailed literature review and analysis of existing studies. Utilizing Web of Science as the search engine is advantageous owing to its database encompassing several types of articles with high academic impact (Birkle et al. 2020). The purpose of our literature review was to summarize the coupling dynamics between URT and urban space. Employing bibliometric analysis tools, especially CiteSpace, facilitated a deep dive into the evolution and current state of research in this domain (Chen 2006). It focuses on understanding and interpreting network patterns and historical developments. CiteSpace offers various functions to facilitate comprehension, including identifying rapidly growing thematic areas, clustering networks, and automatically labeling clusters with terms extracted from cited articles. In addition, CiteSpace can perform clustering analysis based on commonly cited articles, and the collected dataset underwent quantitative analysis to identify patterns, trends, and thematic clusters within the research landscape.

2.2 Meta-Analysis

Following the bibliometric analysis, a comprehensive content analysis of the pertinent literature was undertaken. This phase aimed to discern how big data’s analytical capacity enhances our understanding of the URT-urban space nexus. By extracting and evaluating the informational value contained within big data, we delineated distinct functional elements indicative of big data’s utility in this context.

3. Previous studies on the coupling relationship between URT and urban space

The interdependence between urban transportation and urban space has been extensively researched (Cervero and Kockelman 1997). The construction of URT systems is both a part of urban infrastructure and a significant force shaping urban morphology and driving socio-economic development. The impact of URT development on urban space encompasses economic vitality, social inclusivity, and environmental sustainability. Conversely, changes in urban space, such as land-use patterns and the distribution of commercial and residential areas, also significantly influence the ridership and operational efficiency of URT systems. With the acceleration of urbanization, this coupling relationship has become more intricate, requiring in-depth analysis through scientific theories and data-driven approaches to comprehend its dynamic interactions and long-term impacts as well as to make predictions about URT ridership, optimize urban planning, and improve urban operational efficiency.

3.1. Impact of URT on urban space

URT not only introduces a mode of transportation but also acts as a powerful force capable of altering the urban spatial form, structure, and function. This impact can be examined from various perspectives, including the enhancement of land value, the shaping of urban morphology, and the promotion of urban economic activities.

As a subsystem of the urban transportation system, the study of how URT affects urban space can start from the perspective of how the transportation system affects urban space. Historically, the notion that transportation influences urban land value and utilization was first introduced by J.H. Von Thunen in 1842 through his agricultural location theory (Von Thünen 1842). This concept was further developed by Alfred Weber in 1929 with his industrial location theory (Weber and Friedrich 1929) and by Walter Christaller in 1955 through his central place theory (Christaller 1955), both of which detailed transportation’s pivotal role in urban spatial dynamics. Walter Isard’s introduction of the neoclassical macroeconomic location theory in 1956 underscored the complex links between transportation, land use, and economic growth (Isard 1956; Schumpeter 1946; Leontief 1936). By 1986, Jacques-Francois Thisse’s neoclassical microeconomic location theory, alongside Lowdon Wingo Jr.’s foundational economic model, had provided a systematic portrayal of the interplay between urban transportation and land use (Thisse and Vives 1988; Fujita and Thisse 1996). Collectively, these theories highlight transportation’s integral role in sculpting urban structures, driving economic trends, and fostering sustainable urban development shifts.

The 1980s marked a pivotal era as urban planners moved toward sustainable development, advocating for schemes that minimized dependency on motorized travel and encouraged transit-oriented, community-centric urban planning (Koopmans 1963; Lowry 1964). Today, the theoretical framework regarding transportation’s impact on urban spaces is well-established, with transportation economics offering comprehensive insights into the nexus between urban spaces and transportation systems (Wingo Jr 2016; Vergel-Tovar and Rodriguez 2018). As a subsystem of urban transportation, URT inherits these theories in its impact on urban space, but it diverges significantly owing to its high capacity, safety, reliability, and immunity to traffic congestion. URT’s unique ‘hyperlinking’ capability can efficiently transport large numbers of individuals across urban expanses without the hindrances imposed by road-based transport systems, marking a significant departure from other transportation systems. This underscores URT’s transformative potential in urban development, highlighting its role as a critical driver in the reconfiguration of urban spaces for enhanced connectivity and sustainability.

The theoretical and practical foundation of transportation economics supported the explicit emergence of the concept and model of public transportation and high-density urban land integration and development (Knight and Trygg 1977; Knight 1980). Building upon this foundation, the concept of Transit-Oriented Development (TOD) represents the progression of thought within the field. TOD synthesizes the theoretical insights of urban transportation’s impact on spatial structures with the practical necessities of modern urban planning (Chai 2024; ESCAP 2024). It advocates for the integration of high-capacity public transportation systems, such as URT, with high-density urban land use (Brandão et al. 2024; Dierwechter and Miller 2024). This model, rooted in the principles of sustainable development and supported by empirical evidence (Ibraeva et al. 2020), posits that TOD is the most effective strategy for achieving sustainable urban growth(Ibraeva et al. 2020).

By relying on transportation systems centered on URT, buses, and other public transit modes, the TOD model aims to diversify and intensify the development around public-transit stations, which can further reinforce the coupling between URT and urban space. This intensification approach achieves a diversified, high-density, and walkable intensive development pattern around public-transit stations (Sim and Calthorpe 1986). Since the inception of the TOD concept, scholars have turned their attention to the transformative impact of urban rail systems on land-use patterns, real estate values, and urban spatial structures (Tzouvadakis 1992; Cervero and Landis 1993). Extensive research, including studies by Cervero (Cervero and Landis 1997), has been conducted to understand how the development of URT influences the dynamics of land use.

In the 1970s, R. L. Knight provided direct evidence of the impact of URT on urban land use (Knight 1980). In the 1990s, research on the impact of URT on real estate values came into focus (Cervero 1994; Kobayashi and Okumura 1997; Khasnabis and Natl Res 1998). Researchers observed a significant increase in property values near transit stations, often referred to as the transit premium. Building upon foundational theories and practices established in the 1980s, researchers of the 1990s validated the impact of URT on urban space By delving into more detailed and empirical explorations to quantify the influence of URT on various aspects of urban life (Hahn et al. 2002), including land-use patterns, real-estate values, and socioeconomic dynamics (Lawless 1999; Gakenheimer 1999; Hsu and Guo 2001; Cervero, Duncan, and Trb 2002). Studies during this period affirmed this theory: proximity to transit stations emerged as a crucial determinant of real-estate valuation, with property prices soaring in station-adjacent areas (Ibraeva et al. 2020; Vichiensan et al. 2022; Wan, Lu, and Sun 2023; Cervero, Duncan, and Trb 2002).

The influence of URT on urban spaces extends beyond merely elevating land values along its corridors. More significantly, it has been recognized for its ability to reshape the urban space. Studies of urban space are typically categorized into three distinct areas: spatial form, spatial structure, and spatial function (Lee 2006; Shen et al. 2020b; Xu and Chen 2021). Figure 1 shows a schematic diagram of the impact.

Figure 1. Schematic Representation of the Impact of Urban URT on Urban Spatial Configuration.

URT can reshape the spatial form of cities. This is primarily because of enhanced accessibility and the attractiveness of these areas for residential, commercial, and mixed-use developments (Chen et al. 2023; Jing and Liao 2023). Investigating the extent to which URT enhances land values and quantifying this increase is crucial (Soltani et al. 2024). Uncovering and measuring these patterns enables precise predictions of land values (Yang et al. 2020). Research on land-value capture (LVC) is a pivotal strategy in URT systems, enhancing economic viability and supporting sustainable urban development through innovative procurement models and policies (Li and Love 2022). Higher land values incentivize development and redevelopment efforts, potentially leading to urban renewal and gentrification in areas surrounding transit stations (Zheng and Kahn 2013; Bardaka, Delgado, and Florax 2018; Lin and Yang 2019; Meriläinen et al. 2024). By providing a fixed-route transportation network, URT creates focal points for urban growth, around which higher-density developments tend to cluster (Woo 2021). URT fundamentally transforms the spatial form of cities through the enhancement of land values, improvement of accessibility, alterations in land use and density, and facilitation of high-density developments.

URT has a profound impact on the spatial structure of cities. The presence of URT stations acts as a magnet for development, leading to changes in land use patterns around these transit nodes (Shen et al. 2020a; Kim and Byun 2021). In addition, areas around stations often increase in density and shift toward mixed-use development, and this concentration of mixed-use land contributes to the formation of urban-core agglomeration areas (Shi 2015; Haque and Patel 2018). URT facilitates the development of polycentric urban structures by improving connectivity between different parts of the city (Garcia-López, Hémet, and Viladecans-Marsal 2017; Roth et al. 2011; Ou, Zheng, and Nam 2022). The formation of these polycentric structures helps distribute economic activities more evenly across the urban area, reducing congestion in the city center and encouraging more balanced urban development (Roth et al. 2011; Giuliano et al. 2022; Kumar, Ghosh, and Singh 2022; Zhou et al. 2022). Polycentricity also facilitates the formation of a network by URT, thereby generating network effects (Moreno-Pulido, Pavón-Domínguez, and Burgos-Pintos 2021). Analyzing these network effects offers a more precise understanding of the relationship between URT and the urban spatial structure (He 2020; Wu et al. 2020; Yang et al. 2022). In essence, URT reshapes the urban spatial structure by promoting polycentric development, enhancing connectivity, and influencing land use and density.

URT significantly influences the spatial function of cities. By offering an alternative to car travel, URT enhances accessibility to various parts of the city, effectively shrinking distances and making the city more navigable for its residents. This transformation in mobility and accessibility patterns encourages a shift in how urban spaces are used (Jin, Wang, and Su 2022; Su et al. 2022). Areas previously deemed peripheral or inaccessible become integrated into the urban fabric, leading to a more uniform distribution of activities and opportunities (Vale, Viana, and Pereira 2018; Salov and Semerikova 2023; Song, Abuduwayiti, and Gou 2023). Moreover, the development of URT systems often includes the integration of public and community spaces within or around transit stations (Chen et al. 2023). By reshaping urban spaces to include communal areas, URT enhances the social function of these spaces, encouraging social interaction, cultural activities, and community building (Zhu 2009). URT acts as a catalyst for TOD, which prioritizes high-density, mixed-use developments around transit stations (Ibraeva et al. 2020). TOD also significantly alters the function of urban spaces by promoting a compact, efficient use of land (Wu and Li 2022). This leads to vibrant, active neighborhoods where living, working, and leisure activities are closely integrated (Dai et al. 2023). In essence, URT reshapes the urban spatial function by improving mobility and accessibility, facilitating TOD, stimulating economic activities, promoting social equity, enhancing environmental sustainability, and shaping public and community spaces.

The influence of URT on urban space also plays a pivotal role in steering its development toward sustainability (Brown and Werner 2008; Singh et al. 2017; Jurkovic, Hadzima-Nyarko, and Lovokovic 2021; Su et al. 2021; Liu and Xia 2023). It does so by providing a reliable, efficient alternative to private car usage (Zhao and Shen 2019; Ma and Lo 2013). It also helps reduce traffic congestion and related environmental impacts, fostering a shift toward more sustainable modes of transportation. As noted earlier, URT systems typically encourage nonmotorized transportation modes, such as walking and cycling. The development of pedestrian-friendly infrastructure around transit hubs promotes this shift, facilitating a healthier, more sustainable urban lifestyle (Lin, Li, and Lin 2022). URT also improves air quality and environmental sustainability by reducing dependence on private cars, contributing to better urban air quality (Stone et al. 2007). It helps decrease greenhouse gas emissions and other pollutants, creating a cleaner, healthier urban environment (Saxe, Miller, and Guthrie 2017; He et al. 2011; Saxe and Guthrie 2020). Additionally, the shift to more sustainable modes of transportation supports broader environmental sustainability goals, thus mitigating the impact of climate change (Zahabi et al. 2013). While rail transport may disrupt the ecology of interconnecting regions (Ozturk and Ozturk 2010), the construction of URT in high-density areas significantly reduces carbon emissions (Stone et al. 2007; Hook et al. 2010).

Existing research on the impact of URT on urban space has demonstrated that (1) URT can reshape the spatial form, structure, and function of cities; (2) URT can transform the urban core spaces along its routes into high-vitality, high-aggregation, and high-value areas; (3) scientific and rational URT planning can promote sustainable urban development.

Although most research and urban planning initiatives have emphasized the positive transformations brought about by URT in reshaping urban spatial structures and dynamics, addressing potential negative impacts is equally crucial. In many cases, URT has facilitated positive changes, enhancing connectivity and fostering economic growth; in others, the impact on urban space has not been as beneficial.

Israel’s analysis of the impact of commuter URT on residential location decisions explored the suburbanization of commuter URT and its effects on the rural suburbs of the Tel Aviv metropolitan area. URT strengthens the potential of both urban core areas and accelerates suburbanization and de-urbanization (Israel and Cohen-Blankshtain 2010). The increased attractiveness and rising land values around urban railway stations often lead to gentrification, which, while revitalizing communities, can simultaneously cause social and economic isolation by displacing longtime residents and local businesses unable to afford the escalating costs (Lizarraga 2012; Cohen and Brown 2017). This gentrification effect necessitates a balanced approach to urban development, ensuring that while the use of public transport is promoted for sustainable city growth, measures are taken to prevent the negative outcomes of overreliance (Lin, Broere, and Cui 2022). Without adequate investment in service quality, safety, and reliability, an overreliance on URT can result in overcrowded and inefficient transport systems, highlighting the importance of comprehensive planning in public-transport development (Stutsman and Trb 2002). Moreover, the substantial costs associated with constructing, operating, and maintaining URT systems pose significant challenges to public budgets, particularly if the anticipated ridership and economic benefits fall short of expectations (Mahboob et al. 2012; de Luca and Papola 2001; Giunta 2023). Thus, conducting thorough feasibility studies and cost–benefit analyses prior to initiating a URT project is critical to ensuring its economic viability and mitigating financial risks (Tang and Lo 2008). To alleviate the financial strain on public resources, exploring innovative financing models, such as public – private partnerships and LVC mechanisms, becomes essential. Nonetheless, URT systems can inadvertently deepen urban spatial divides because areas with direct URT access thrive while others remain disconnected and overlooked, exacerbating spatial inequality (Shinbein and Adler 1995). Addressing these potential adverse impacts demands a comprehensive and holistic approach to urban planning and transport policy, emphasizing the need for cities to adopt strategies that not only exploit the transformative potential of URT but also promote more inclusive, sustainable, and resilient urban environments (Vermote et al. 2014).

Research on such phenomena has shown that the construction of URT alone is insufficient to increase public-transit demand and positively impact the city’s economy, this instead requires integrated TOD development that combines various external factors, such as policy, the environment, and space stimuli (Xu et al. 2017; Knowles, Ferbrache, and Nikitas 2020; Woo 2021), to ensure the positive benefits of URT. Guided by TOD theory in urban planning and URT fields, the coupling and dynamically synergistic development between URT and urban space are inevitable. They play a mutually positive influence and guidance role (Yang et al. 2020; Papagiannakis, Vitopoulou, and Yiannakou 2021; Wan, Lu, and Sun 2023). By promoting a collaborative and inclusive urban development approach, we can maximize the benefits of URT while minimizing potential adverse impacts, creating a vibrant, inclusive, and sustainable urban space.

3.2. Impact of urban space on URT

Scientific urban planning should fully consider the positive impact of URT projects on urban spatial form and structure. A 2002 study (Srinivasan 2002) found that spatial features indeed influenced mode choice, whether for non-work-related or work-related. Optimizing urban space can guide and promote the growth of URT travel behavior, achieving sustainable urban transport and efficient operation (Gilat, Sussman, and Trb 2003; Greenwald 2006). Optimizing urban space plays a crucial role in stimulating the demand for URT travel behavior (Milakis, Vlastos, and Barbopoulos 2008). In urban space design, ample public spaces, including parks, squares, and pedestrian streets, should be provided to offer places for leisure and gatherings, further enhancing the appeal of URT (Deng and Zhang 2011; Zhang, Li, and Yu 2013; Qiao and Destech Publicat 2016; Otsuka and Reeve 2024; Salaheldin et al. 2023). In contrast to the complex effects of URT on urban space, the primary impact of urban space on URT is the variation in ridership.

Although numerous studies have confirmed the impact of urban space on URT ridership, some scholars remain skeptical of this conclusion. In a 1996 study, Wells found that the service quality of public transit commuting entrances stimulated ridership, but that the development of high-density residences seemed to have no effect on commuting demand (Wells and Hutchinson 1996). Dill found that the urban spatial form near public transit hubs seemed to have a minor impact on commuting ridership and more so affected the hub’s passenger demographics, increasing non-commuting ridership (Dill 2008). Lin, in a ridership regression analysis of Taipei City, discovered that the walking distance to the subway station entrance affected ridership, but that the impact of land development on ridership seemed inconclusive (Lin and Shin 2008). Kahn posited that people’s green travel ideology causes differences in travel behavior choices, with those with a higher green travel awareness being more inclined to choose URT as their mode of travel (Kahn and Morris 2009).

Discussing the impact of urban space on URT through dialectical thinking, the question arises of why some scholars remain skeptical of the impact of urban space on URT. Lavery’s case study in Canada provided insights. Lavery developed an integrated urban model (IUM) depicting the relationships between land use, transportation, and activities to predict the impact of Canada’s light URT (LRT) on urban space. His conclusion challenges the notion that constructing an URT network alone is insufficient to establish a causal relationship with economic development and changes in land-use patterns (Lavery and Kanaroglou 2012; Liu et al. 2022).

Most case studies suggesting that urban space does not influence URT ridership have focused on the earliest URT lines in developed countries during the last century (Wyczalkowski 2017; Veka 2007; Jon Whiteaker 2024). These lines often led to urban sprawl, instead of inward contraction. In other words, the construction of URT was not coordinated with urban space. To ensure that URT brings positive impacts and benefits to urban space, we must define the correct coupling relationship between urban space and URT.

Guided by TOD theory, URT construction results from a well-coordinated coupling of urban space and URT, fostering a synergistic relationship (Li, Wu, and Yan 2022). If URT fails to positively influence the spatial form and structure of urban space, or if optimizing urban space does not induce growth in URT ridership, it indicates an ineffective coupling and synergy between the two (Rietveld 1994; Feng et al. 2023). In such cases, revitalization and transformation of both URT and urban space become imperative. Otherwise, the urban transportation system will operate inefficiently and unsustainably, leading to issues such as excessive private vehicle use, traffic congestion, and a lack of viable alternatives.

Furthermore, substantial investments in URT construction without commensurate returns exacerbate social and economic inequalities. The impact on marginalized communities results in unequal mobility and socioeconomic development opportunities. Overlooking the coupling of transportation and land use not only wastes the opportunity to use URT as a catalyst for compact, walkable, and TOD but also hinders the realization of environmental, social, and economic benefits (Nanayakkara et al. 2023; Aulia and Octaviana 2024). Comprehensive transportation policies should be formulated to encourage residents to use URT, providing convenient connecting options to enhance its accessibility and attractiveness. Figure 2 shows a schematic diagram of the coupling relationship between urban space and URT.

Figure 2. Schematic diagram of coupling relationship between urban space and URT.

Considering the coupling relationship between URT and urban space and implementing effective planning and design are crucial to achieving sustainable and efficient urban transportation. Urban planning should thoroughly consider the layout and coverage of URT, ensuring a seamless integration of URT stations with surrounding functional areas and providing a pedestrian-friendly environment. Exploring the coupling and dynamic synergy between URT and urban space requires further research into the impact mechanisms of urban space on URT, which can enhance our understanding of their interdependence.

3.3. Temporal and sequential aspects of the coupling Relationship between URT and urban space

The impact of URT on urban space primarily manifests in the morphological transformation of spaces along the transit lines into high-activity, high-density, and high-value areas. Urban space’s influence on URT is primarily reflected in changes in passenger flow. The fundamental reason for the coupling effects between the two lies in the changing behavioral needs of people. These needs determine how people travel, the purpose and frequency of their trips, and the scope of their travels, thus influencing URT passenger flow and determining the layout and operation of the transit system. Human subjective consciousness drives changes in the form, structure, and function of urban space (Shi and Fu 2022).

It is also essential to recognize that transit construction is a long-term, large-scale project. The strength and direction of these coupling effects will evolve over time, showing characteristics of lag and non-linearity (Jiao et al. 2022). The impact of URT on urban development and spatial configuration does not manifest immediately (Altieri, Raskova, and Costa 2022). Instead, there is often a lag between the construction of URT systems and the observable changes in urban space. This lag can be attributed to various factors, including the time required for urban policies to adapt, for real estate markets to respond, and for residents and businesses to relocate or change their behaviors based on the new transit options (Deng et al. 2019; Xia et al. 2022). Acknowledging these lag effects is essential for long-term urban planning. It allows policymakers and planners to anticipate future changes in the urban form and function and to design adaptable, forward-looking policies that accommodate these gradual transformations.

The relationship between URT and urban space is not linear, meaning that the effects of URT on urban development cannot be directly proportional or predictable (Lin and Tian 2020; Wang et al. 2022). Instead, they are influenced by a complex set of factors, including the existing urban form, density, land-use policies, and the socioeconomic characteristics of the population (Yang et al. 2023). These factors interact in diverse ways, leading to varying outcomes in different contexts (Peng et al. 2020). Thus, investigating the influence of railway stations on urban sprawl through the lens of cellular automata can provide valuable insights into the spatial and temporal dynamics of urban development (Wang, Jia, and Qin 2003; Wang, Jia, and Qin 2003). Cellular automata models, which simulate the interactions of complex systems based on simple rules, can be particularly useful in understanding the nonlinear and lagged effects of URT on urban spaces (Ma and Chen 2019; Yang et al. 2019; Na et al. 2021; Wang et al. 2022). Applying cellular automata models to study the impact of URT can also help in visualizing potential future urban-growth patterns, assessing the effectiveness of different urban planning policies, and identifying strategies to leverage URT for sustainable urban development.

By outlining the nonlinear relationship and spatiotemporal lag of coupling between URT and urban space, we review the coupling relationship between URT and urban space at different stages, trying to reveal the fundamental reasons for this nonlinear relationship and lag.

During the planning stages of URT projects, strategic decisions regarding land use and the optimization of urban spaces lay the groundwork for future transformations (Tan Zhangzhi et al. 2017). This phase is pivotal in redefining commercial, residential, and public service layouts along future transit lines, injecting new vitality and functional diversity into urban areas. Moreover, the planning process initiates the vital coupling between URT and urban spaces, with decisions on station placement and land integration shaping the city’s developmental trajectory. Importantly, this phase also molds resident expectations and promotes public participation, ensuring that the planning of URT projects aligns with community needs and aspirations (Liu and Xia 2023). This early engagement fosters societal acceptance and paves the way for the successful realization of URT initiatives, highlighting the planning phase’s crucial role in driving sustainable urban development and enhancing the efficiency of urban environments.

The construction phase of URT projects profoundly influences urban space, bringing both positive enhancements and potential disruptions. While it catalyzes urban improvements like infrastructure upgrades and increased green spaces, fostering a more livable environment around transit areas, it also poses challenges (Jiao et al. 2022). These include disruption to the existing urban fabric, increased traffic congestion, potential economic displacement of residents and businesses, and a temporary decline in local business activity due to construction-related disturbances. Concurrently, the initiation of URT impacts passengers’ travel behavior, shifting expectations and needs toward public-transit use and altering daily travel patterns. This adjustment reflects the evolving demand for more sustainable and efficient transportation options (Xia et al. 2022). In conclusion, while URT construction promises long-term benefits for urban development and mobility, it necessitates careful management of its immediate, often mixed, impacts on urban spaces and resident lifestyles, underscoring the need for strategic planning and community engagement to mitigate negative effects and maximize positive outcomes.

In the initial operational stages of URT, tangible impacts on urban space and passenger behavior emerge, highlighting the system’s transformative role (Zhang and Jiao 2019). Initially, changes in URT passenger-flow and station-usage patterns become evident, fueling commercial vitality and enhancing land value along transit corridors. This period sees an uptick in spatial utilization efficiency, with urban spaces near URT lines experiencing increased vibrancy and more effective use of commercial and residential areas. Concurrently, the convenience and efficiency of URT lead to shifts in residents’ travel habits, influencing urban traffic flows and bolstering the commercial and living environments adjacent to transit routes. These early operational impacts lay a practical foundation for optimizing urban spaces and transportation systems (Xing-lei et al. 2023). The adjustment phase offers valuable insights into urban planning, illustrating the clear and significant mutual influence between URT and urban development, thereby supporting more informed decision-making in urban and transport planning strategies.

In the mid to late operational stages of URT, the system’s mature operation catalyzes significant impacts across urban environments, operational efficiency, and community dynamics (Healey, Thomas, and Lahman 2013). Enhanced operational efficiency leads to a more reliable and efficient URT service, contributing to a stable increase in passenger flow and invigorating urban spaces along transit corridors. This not only boosts the urban economy but also elevates the city’s overall image. Concurrently, the attractiveness of land along URT routes escalates, fueling commercial and residential development owing to improved transportation convenience, which underscores the profound coupling between URT and urban land use. Moreover, URT’s mature operation spurs comprehensive community development, making surrounding areas more desirable for living and investment, this, in turn, drives new constructions and renovations. This synergy between URT and urban development enhances the quality of urban spaces and fosters prosperous, livable communities. Collectively, the mid to late stages of URT operation underscore a deep-seated relationship with urban spaces, characterized by operational enhancements, land-value appreciation, and holistic community development, reflecting the transformative power of URT in shaping sustainable urban futures.

A schematic diagram illustrating the coupling relationship between URT and urban space across different time stages is presented in Figure 3. The coupling relationship between URT and urban space, intricately woven across various stages of planning and operation, is a complex, multidimensional process that unfolds both spatially and temporally. This dynamic interplay, extending beyond mere spatial morphology to encompass passenger flow, land-use evolution, and community development, necessitates a comprehensive understanding. Big data technology has emerged as a pivotal tool in this context, providing a granular, time-dimensional perspective that enriches our understanding of the URT and urban space nexus. By harnessing the power of big data, we can delve into patterns of passenger behavior, land-use changes, and community growth trends, offering quantified insights that serve as a scientific underpinning for urban and transportation planning enhancements. This approach underscores the importance of incorporating temporal analysis into the study of URT and urban space coupling, advocating for a methodical exploration of their interaction over time. Utilizing big data’s time attribute enables a more precise analysis of this relationship, offering critical data support for the strategic planning, long-term operation, and maintenance of URT systems. This nuanced, data-driven examination is essential for fostering sustainable, intelligent urban development, positioning big data as a cornerstone in deciphering the evolving dynamics between URT and urban space.

Figure 3. Schematic Representation of the Temporal Effectiveness and Sequencing in the Coupling Relationship between Urban URT and Urban Spatial Configuration.

3.4. Bibliometric analysis

We delved into this exploration through bibliometric analysis, a method that allowed us to systematically review and analyze the extensive body of literature on the subject. The methodology employed in our literature search involved entering the following query into WOS: TS = ((((Urban rail transit*) OR (subway) OR (URT*) OR (railway) OR (Metro) OR (Tube) OR (underground railway*) OR (Rapid transit*)) AND ((urban space*) OR (city space*) OR (metropolis space*) OR (urban land*) OR (land use*) OR (spatial pattern*) OR (space structure*))) AND ((Affect) OR (effect) OR (influence) OR (impact))). This query yielded over 6,000 literature works, necessitating further refinement. Filtering focused on the transportation domain by selecting the mid-level theme of ‘traffic’; at the micro-level, we excluded aviation, train scheduling, and road safety to align the results more closely with the research topic. Finally, document types were restricted to articles and review articles. As of September 1, 2023, a total of 1,029 relevant literature pieces had been obtained, and CiteSpace was used to perform bibliometric analysis on these documents.

Figure 4 illustrates the keyword analysis of 1,029 literature works, outlining the historical trajectory and diverse dimensions of research on URT and urban space. The color of the connecting lines between different keywords in the visualization indicates the chronological order of the research themes; purple signifies earlier studies, while yellow denotes more recent research. This visualization portrays the gradual evolution and deepening of this field. In its early stages, research predominantly focused on the impact of public transportation on urban air quality, greenhouse gas emissions, and real estate prices, establishing a foundational understanding of the preliminary coupling between URT and urban space. Over time, the research direction shifted toward more complex and diverse areas, encompassing aspects such as the built environment, land use, urban form, and density. This shift signifies a heightened focus on the interaction and influence between URT and urban space.

Figure 4. Keyword distribution maps obtained by keyword analysis using CiteSpace.

Particularly noteworthy is the increased attention given to pedestrian accessibility and transportation connections at stations (e.g. shared bicycles). These factors directly affect the passenger flow and the efficiency of URT, highlighting its recognition as a significant factor influencing urban form and the built environment.

Additionally, researchers have increasingly utilized big data and advanced data analysis tools to deepen exploration in this field. The application of smart transit card data and mobile signaling data provides additional dimensions and depth to analyze the complex relationship between URT and urban space, offering a fresh perspective to interpret their coupling. Moreover, the application of machine learning and predictive analytics to big datasets offers the potential to forecast future trends in urban development and URT system performance, facilitating more informed urban planning and policy decisions. With these advancements, we anticipate future research to even more comprehensively focus on revealing the additional layers and dimensions of the coupling relationship between URT and urban space. Researchers will also explore how optimizing the design and planning of URT and urban space can foster more harmonious, sustainable urban development.

The development overview of research on the coupling relationship between URT and urban space can be quickly understood through CiteSpace’s co-citation analysis feature. This feature utilizes complex network calculations on co-cited literature to summarize specific research directions within the field. The co-citation network view, with the positions of clusters and their associations, reveals the intellectual structure of the science mapping field, providing researchers with a holistic understanding of the overall landscape (Chen 2018). Figure 5 clearly reflects the clustering distribution in this field. The color gradient within each cluster block indicates the first year of co-citation relationships in that cluster, ranging from dark to light. The legend located in the lower-left corner denotes the year. The numbering of each cluster denotes its importance in overall research. Labels for each cluster can be marked using the titles, keywords, and abstract themes of citing references. The calculated results in the top-left corner of the image reveal that the high modularity was 0.9312, signifying a clear delineation of various subdomains within the research on the coupling relationship between URT and urban space. Another indicator for cluster validity, the average silhouette value, was 0.8913, relatively high, demonstrating that each cluster in the image has a consistently high average silhouette value, confirming the clarity of boundaries for each domain.

Figure 5. Cluster distribution maps obtained by co-citation and coupling analysis using CiteSpace.

Figure 5 illustrates the current state and developmental trends in the research on the coupling relationship between URT and urban space, revealing two primary research directions. First, the central focus was on TOD, emphasizing the impact of URT on urban space. This research primarily concentrated on promoting the efficient utilization and sustainable development of urban space by optimizing transportation layout and design. This includes the reorganization of urban spatial structures and the integrated optimization of public transit networks, aiming for a more harmonious and coordinated urban development. Second, another research direction centered around subway ridership, highlighting the influence of urban space on URT. Studies in this area focused on optimizing and adjusting the planning and operation of URT through the analysis and understanding of the characteristics and dynamics of urban space. This involves predicting and analyzing ridership, as well as adjusting and optimizing transportation networks to achieve more efficient and seamless traffic operations.

A significant finding in Figure 5 is that geographically weighted regression (GWR) emerged as a core research area, second only to TOD and ridership. Both primary research areas intersected with GWR, making it a key tool in unraveling the coupling relationship between URT and urban space. GWR, by providing more precise and accurate spatial analysis, proved to be a powerful tool for revealing the complex coupling relationship between URT and urban space. It not only aids in a better understanding of the local effects of spatial objects but also offers more accurate and reliable predictions and analyses.

4. Big data in urban research

Bibliometric analysis verified that big data analysis has become a key means to clarifying the collaborative development model of URT and urban space. The necessity of big data for future research lies in its potential to uncover insights that traditional methods cannot, thereby guiding the collaborative development of URT and urban spaces toward enhanced efficiency, sustainability, and livability. As we continue to harness the power of big data, it will play a more pivotal role in shaping the future of URT systems and the cities they serve, ensuring that they evolve in harmony with the needs and behaviors of their residents.

Based on the results of our bibliometric analysis, we focused on reading articles in the GWR domain and articles with keywords related to big data. After cross-referencing these articles, we identified patterns in the literature. The first pattern was that the types of big data used could be described and summarized into two categories. The first category involved traffic behavior data used to build detailed human mobility behavior models (Huang, Wang, and Fu 2019; Nasri and Zhang 2019; Kim and Sohn 2020; Luan et al. 2020; Xue et al. 2020; Deng et al. 2021; Jie et al. 2022), and the second category involved traditional urban spatial data and new types of urban big data used to depict urban spaces (Someya, Saito, and Kiyohara 2019; Liang, Xie, and Bao 2024; Li et al. 2018). The new types of urban big data primarily included geotagged data based on satellite imagery (Gonzalez-Navarro and Turner 2018; Gendron-Carrier et al. 2022; Ou, Song, and Nam 2024) and subjective review data from social media (Tu et al. 2022; Gao et al. 2024). Both behavioral data and spatial data encompassed a variety of data sources. The second pattern was that these studies could be divided into four research themes. The first theme explored the impact of different spatial data on behavior using existing behavioral data (Zhou et al. 2019). The second theme investigated which behavioral data could influence human mobility behavior using known spatial data (Khosrosereshki and Moaveni 2022; Davies et al. 2023). The third theme used spatial data to predict behavioral data (Dai, Sun, and Xu 2018; Li et al. 2019; Tang et al. 2019; Zhang et al. 2020; He et al. 2022; Dong et al. 2023; Li et al. 2023; Yi et al. 2023; Chen et al. 2024). The fourth theme utilized traffic data to predict spatial models (Li et al. 2021; Liu and Xia 2023). The research methods used in these four themes included GWR, machine learning, and deep learning, where the artificial intelligence models differed, indicating that the research methods varied across studies.

Investigating the complex coupling relationships between URT and urban spaces through big data analysis is a multifaceted approach, each with its unique strengths and limitations. Within this analytical framework, urban big data transcends mere information accumulation, serving as a vital lens through which urban phenomena are understood and interpreted. The interplay of ‘behavior’ and ‘space’ emerges as a fundamental, inseparable dynamic, central to unraveling the intricacies of urban development.

At the core of these explorations, the scope and accuracy of big data stood out as essential factors in thoroughly elucidating the coupling dynamics between URT systems and the urban fabric. Big data’s expansive coverage offers a detailed representation of urban spatial dynamics and passenger flow behaviors, while its precise temporal insights enhance the understanding of these complex relationships. It is, therefore, crucial to pinpoint specific datasets that are key to decoding these interactions and to harness the significant value residing within big data attributes.

Using meta-analysis to analyze the types of big data that appeared in previous papers using big data for coupled-relationship-analysis research and the value of their data, our research categorized urban big data into three levels: ‘influencing elements (IEs),’ ‘functional elements (FEs),’ and ‘key functional elements (KFEs).’

‘IEs’ encompass all elements that may impact the coupling relationship between URT and urban space, including environmental, socioeconomic, and technological factors. They can directly or indirectly influence the design, planning, and operation of URT and urban space, constituting the diverse background of urban development (Zhai 2020; Lin, Broere, and Cui 2022; Lin et al. 2024).

‘FEs’ are more specific and refined elements directly involved in and influencing the coupling relationship between URT and urban space. In this realm, a close connection exists between the morphology and functionality of urban space and people’s behavior, forming a dynamic system of mutual influence and promotion (Li et al. 2021; Chan, Ma, and Zhou 2023; Deng and Zhao 2022; Tu et al. 2022; Ma et al. 2023).

In URT, delving into the characterization relationships of ‘behavioral’ FE is crucial to understanding their inherent connection with urban space. These factors primarily focus on passenger behavior data, such as travel decisions, behavioral patterns, and travel preferences. These data not only enhance our understanding of passenger needs and behavior but also serve as a vital reference for urban and transportation planning (Orellana and Guerrero 2019; Wang, Kwan, and Hu 2020; Zheng et al. 2022).

Simultaneously, in urban planning, an in-depth exploration of the characterization relationships of ‘spatial’ FE acting factors is essential to understanding their inherent connection with URT. Here, ‘spatial’ FE refers to the qualitative and quantitative description of urban space, including semantic representation of physical environment elements such as urban spatial forms, vitality, and functions. Spatial acting factors focus on data within urban space, such as land use, transportation networks, and building distributions. These data, obtainable and processable through GIS, provide crucial reference points for urban and transportation planning (Sugimoto, Ota, and Suzuki 2019).

To better utilize massive data in urban research, researchers need to categorize and organize data. This categorization involves not just dividing data based on different attributes and features but, more importantly, associating and merging data with FE. By combining massive data with behavior FE, researchers can discover hidden patterns and values within the data. Classifying urban big data into spatial and behavioral FE aids in gaining a better understanding and application of data, allowing for an in-depth study of the coupling relationship between urban space and URT from different perspectives. By comprehensively analyzing these two types of data, researchers can obtain a comprehensive understanding of the characteristics, demands, and potential issues of the URT system, providing more accurate and scientific decision-making support for urban and transportation planning.

Through data classification efforts, researchers can establish a data framework based on behavior and spatial FE. This framework facilitates a better understanding and description of behavioral patterns and rules within the URT system, enabling an in-depth exploration of the coupling and collaborative development mechanisms between URT and urban space. Using this data framework, researchers can conduct characterization and analysis of behavior and spatial FE, further exploring the mutual relationships and impact mechanisms between behavior and space.

Finally, attention should be given to ‘KFEs,’ which are elements within the ‘FE’ that have decisive or significant influences. Through in-depth analysis and research, identifying those elements with significant impacts on the coupling relationship between URT and urban space is possible. These KFEs are considered the focal points of research because they can significantly influence the collaborative development of URT and urban space. In-depth research and analysis of these KFEs can accurately reveal the collaborative patterns between URT and urban space, providing more scientific decision support for urban and transportation planning (Gao et al. 2024).

In summary, after reviewing numerous studies, we observed the crucial role of big data in the research on the coupling relationship between URT and urban space. However, to deeply understand these complex relationships, we cannot do mere data accumulation. We must meticulously categorize these data and combine them with the core FE of ‘behavior’ and ‘space.’ Such categorization not only aids in a systematic understanding of the phenomena behind the data but also reveals the intrinsic connections between behavioral patterns and urban space layout. This step is crucial as it determines how we apply theory to practice and formulate effective urban and transportation policies based on research outcomes. The classification is outlined in Table 1.

Table 1. Classification and data sources of functional elements.

Element Classification	Functional Elements	Data Sources
‘Behavior’ Functional Elements	City Residents’ Travel Survey Data (Shen, Chen, and Pan 2016)	Travel Purpose Data
		Mode of Transportation Survey Data
		Travel Time Data
		Travel Distance Data
		Data on Transportation Mode Selection
		Survey Data on Transfer Behavior
		Travel Frequency Data
		Survey Data on Time Distribution
	Data on Urban Transportation Behavior and Socioeconomic Characteristics (Tosa et al. 2018; Bardaka and Hersey 2019)	Correlation Data between Passenger Travel and Demographic Features
		Correlation Data between Passenger Travel and Income Levels
		Correlation Data between Passenger Travel and Residence Type
		Correlation Data between Passenger Travel and Educational Levels
	Urban Traffic Flow Data (Teng and Wang 2018; Wang and Cai 2019)	Traffic Flow Data for Road Segments and Roads
		Vehicle and Pedestrian Flow Data for Intersections and Crossroads
		Traffic Congestion and Bottleneck Data
	Passenger Travel Behavior Data within URT Systems (Eom et al. 2019; Lu et al. 2021; Jie et al. 2022)	Time and Location Data for Passengers Using URT
		Data on Passenger Travel Purposes and Modes
		Data on Passenger Travel Frequency and Duration
		Data on Passenger Transfer Behavior
		Data on Passenger Satisfaction and Experience Ratings
	URT Ticketing and Intelligent System Data (Gong, Lin, and Duan 2017; Zhao et al. 2017; Wang et al. 2018; Shen et al. 2020a; Tang et al. 2020; Xue et al. 2020; Deng et al. 2021)	URT Fare and Ticket Type Data
		URT Ticket Revenue and Cost Data
		Electronic Ticketing System and Smart Card Data
		Automatic Access Control and Passenger Flow Monitoring Data
	URT Network Data (Liu, Yao, and Li 2019; Wang et al. 2020; Luo et al. 2023)	URT Line and Station Data
		URT Operation and Scheduling Data
		URT Vehicle and Equipment Data
		URT Economic Benefit Data
		Complementary and Competitive Data between URT and Other Modes of Transportation
	Data on the Impact of Urban Space on URT (Lu et al. 2022)	Data on the Impact of Urban Land Use on URT Passenger Flow
		Data on the Impact of Urban Morphology on URT Passenger Flow
		Data on the Impact of Urban Development Strategies on URT Demand
	Traffic Behavior Model Data (Zhou et al. 2019; Luan et al. 2020)	Passenger Behavior Model Parameters and Demand Model Data
		Data on Travel Mode and Transportation Mode Choice Models
		Data on Transfer Behavior and Path Choice Models
	Traffic Demand Prediction Data (Gao et al. 2021)	Future Population and Employment Growth Forecast Data
		Urban Planning and Land Use Change Forecast Data
		Forecast Data on Urban Transportation Mode Transition
‘Spatial’ Functional Elements	Traditional Urban Spatial Attribute Data (Mbuh, Wheeler, and Cook 2021)	Land Use Data
		Land Use Change Data
		Urban Structure Data
		Environmental Data
		Urban Transportation Facility Data
		Underground Space Utilization Data
	Data on Innovative Urban Spatial Attributes (Zhuang, Zhang, and Lu 2022)	Urban Landscape and Visualization Data
		Urban Planning and Architectural Design Data
		Mobile Communication Data
		Geotagging Data
		Social Media Data
	Data on the Impact of URT on Urban Space (Zou, Xu, and Gao 2008)	Data on the Impact of URT on Urban Land Use
		Data on the Impact of URT on Urban Morphology
		Data on the Impact of URT on Urban Development Strategies
		Data on the Impact of URT on Environmental Quality
		Data on the Impact of URT on Socioeconomic Factors

Summarizing the classification of ‘behavior’ and ‘space’ FE and their data sources not only integrates previous research findings but also deepens the study of the coupling relationship between URT and urban space. This process helps identify and utilize patterns and trends within the data, providing a more scientific and precise basis for urban and transportation planning. Analyzing the interaction between ‘behavior’ and ‘space’ FE is a crucial task in advancing URT theory research. This analysis guides how we apply these findings in future work, facilitating the optimization of URT systems and the collaborative development of urban spaces.

5. Overview of the coupling relationship

Behavioral FEs play a vital role in the study of urban URT. They reflect passenger needs and satisfaction, shaping the development of urban spaces on a broader level. Similarly, spatial FEs provide quantitative descriptions of urban spatial structures and functions, fundamentally influencing the planning and operation of URT systems. Therefore, a comprehensive analysis of these two types of FE is essential for accurately understanding the coupling relationship between urban URT and urban space and for grasping the evolutionary patterns of urban spaces.

The primary goal of our review was to construct a conceptual framework that reveals the interaction between behavioral characteristics within urban URT systems and urban spatial structures. As in Figure 6, the framework’s foundation lies in a thorough analysis of behavioral and spatial elements, including their data sources and classification. The integration of a temporal dimension, provided by big data sources, is crucial in analyzing the coupling relationship between urban URT and urban spaces. Adding this temporal aspect allowed us to observe changes in elements over time, leading to a comprehensive understanding of the complex dynamics between them.

Figure 6. Conceptual Framework Diagram Illustrating the Coupling Relationship between Urban URT and Urban Spatial Structure.

At a macro level, the framework helped us observe how urban development trends, population migration patterns, and economic activity distributions affect and are affected by URT systems. At a micro level, it aided in analyzing how individual passengers’ travel decisions interact with specific characteristics of urban spaces. This framework, incorporating big data technology from various data sources, enabled us to track the representation of elements over time, facilitating the construction of a more comprehensive, dynamic model.

To deepen our understanding of the coupling relationship, we employed precise analysis methods. Specifically, we developed and applied a scientific system model that encompasses a broader range of elements to study the representation mechanisms of both behavioral and spatial elements. This analysis can help optimize URT services and plan new routes, providing data support for rational urban spatial planning, ensuring sustainable urban development, and efficient operation of URT systems. The construction and application of this coupling relationship framework also enabled us to extract meaningful patterns and relationships from data, quantifying the interaction between the two types of elements and emphasizing the role of the time dimension, thus revealing their interaction through complex urban and transportation system networks.

In summary, a comprehensive analysis of both behavioral and spatial elements enabled us not only to deeply understand the interaction between urban URT systems and urban spatial elements but also to provide scientific bases for the development of urban planning and URT systems. These findings can help guide urban planners and transportation engineers in designing more efficient and sustainable urban URT networks, as well as in optimizing urban space. By identifying key factors affecting the coupling relationship, our findings offer decision-making support to policymakers, aiding them in formulating effective policies to promote the healthy development of urban URT and the rational planning of urban space.

6. Future research directions

As URT systems continue to deepen their interaction with urban spaces, future research should focus on key areas to enhance our understanding and application capabilities.

1. Utilizing the data value of multisource big data to comprehensively analyze the coupling relationship between URT and urban space:

Advancements in information technology have enabled urban URT and urban space research to transcend the limitations of traditional data sources. Future studies will explore methods to integrate diverse data sources, such as social media, mobile signal data, and satellite remote sensing. This multisource data integration and analysis will provide deeper insights into urban dynamics, including population movement, economic activities, and environmental changes, fostering a more comprehensive portrayal of the interaction between urban URT and urban spaces.

(2)	Responding to changes in URT and urban space in a timely manner by accessing real-time updated big data:

Real-time data analysis holds significant potential in urban URT planning and management. Future research should focus on leveraging real-time passenger flow and traffic state data for immediate traffic dispatch and urban planning responses. This approach will enhance the efficiency and flexibility of urban URT systems and support the rapid adjustment of urban spaces.

(3)	Research on more accurate URT and urban spatial-coupling model and data analysis methods:

Advanced data analysis techniques such as machine learning, artificial intelligence, and complex network analysis will play an increasingly vital role in future research. These methods will assist researchers in identifying and forecasting complex patterns and trends in the coupling relationship between urban URT and urban spaces, providing precise decision-making support for policymakers.

(4)	Studying the temporal order of coupled relationships using the time-dimension property of big data:

Future research will concentrate on addressing timeliness and sequencing issues in the interaction between urban URT and urban spaces. Researchers will delve into the temporal evolution and variations of coupling effects, aiming to thoroughly understand the dynamic nature of this relationship and to provide accurate modeling and analysis methods for timing-related factors.

(5)	Deepening Interdisciplinary Research:

The coupling relationship between URT and urban spaces is a multidisciplinary field involving urban planning, transportation engineering, geographic information science, sociology, and more. Future research will further promote interdisciplinary collaboration, integrating theories and methods from different disciplines to form more comprehensive and in-depth research outcomes.

(6)	Policy-oriented Applied Research:

Future research will focus on the policy application of research findings. Through close collaboration with policymakers, researchers will strive to translate research results into specific policy recommendations to guide the development of URT and urban spatial planning, fostering sustainable urban development.

These research directions can not only enrich and refine the theoretical framework of the interaction between URT and urban spaces, but also provide scientific guidance and decision support for the planning, construction, and management of URT systems. This, in turn, will contribute to the development of more efficient, sustainable, and intelligent URT networks, promoting the optimization and sustainable development of urban spaces.

7. Conclusion

This study reviewed the progress in research on the mutual influence between URT and urban space, indicating that the coupling relationship between URT and urban space has characteristics of lag and nonlinearity, and that it operates differently at various temporal sequences. The results of the bibliometric analysis also indicate that big data technology is a future research trend in exploring the coupling relationship between URT and urban space. The temporal attribute information of big data can explain the nonlinear relationship and sequential issues of the coupling relationship. Our research, through a meta-analysis, delved into the hidden data value of big data and classified it. Ultimately, a research framework for the coupling relationship between URT and urban space was formed. As big data technology continues to advance and its application fields continue to expand in the future, this evolving framework will provide stronger data support and intelligent analysis for the harmonious development of URT and urban space.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number: 52078027, 51678029].