The planning of metro-led underground spaces often neglects public perception, while traditional methods struggle to translate unstructured social media data into actionable insights. This study introduces a novel framework that integrates large language models (LLMs) and machine learning to quantify public experience systematically. Using 25,349 Google Maps reviews for 91 Hong Kong metro stations, an LLM engine performs fine-grained perception extraction across 14 indicators. We propose a “perception value” (PV) metric that synthesizes perception frequency and preference, significantly improving modeling performance. Machine learning analysis identifies “transfer” as the most critical contribution to public satisfaction. Notably, “barrier-free design” emerges as a high-contribution but low-preference factor, signaling an urgent need for improvement. Spatially, our analysis reveals a distinct “center-periphery” pattern in perception quality, enabling targeted renewal strategies. Our framework provides a scalable, interpretable paradigm for transforming public perception into planning intelligence, advancing human-centered governance for urban underground spaces.

The master planning of urban underground space (UUS) is essential for guiding orderly development, spatial coordination, and sustainable resource allocation. However, existing UUS planning remains largely static and blueprint-oriented, with insufficient attention to the spatial equilibrium among development demand, supply capacity, and existing utilization performance. Taking the central urban area of Jinan, China, as a case study, this research develops a data-driven spatial equilibrium framework based on multi-source urban data. A multi-dimensional spatial autocorrelation analysis matrix (MSAAM) is constructed by integrating bivariate spatial autocorrelation analysis (BSAA) results to identify local–neighbor relationships and differentiated UUS development strategies. The identified combinations are classified into five types: Internal Development, External Development, Balanced Development, Limited Development, and Self-contained Development. Among the 869 classified spatial units, Limited Development accounts for the largest proportion (57.5 %), followed by Balanced Development (23.2 %), Internal Development (7.8 %), Self-contained Development (7.5 %), and External Development (3.9 %). Balanced Development areas are concentrated in mature built-up areas, whereas Internal and External Development areas occur mainly in urban expansion and transitional zones. Limited and Self-contained Development areas are primarily located in urban–rural fringe areas, ecological corridors, and low-density peripheral zones. These findings show that UUS development is shaped by spatial differences in the alignment of demand, supply, and benefit. The framework supports differentiated master planning, strategic zoning, development prioritization, and more efficient allocation of underground space resources.

Urban underground space (UUS) is crucial for sustainable urban development in high-density megacities. However, a comprehensive understanding of its spatial patterns and development mechanisms is still lacking. This study overcame UUS data limitations by employing points of interest to conduct municipality-wide analysis of Shanghai at the sub-district level. UUS spatial patterns and their driving mechanisms in the main city and suburbs were separately examined to unveil the spatial heterogeneity. Kernel density analysis showed a strong correlation between UUS clusters and urban public activity centers or metro systems. Distinct differences in UUS distribution were observed between the main city and suburbs, especially for underground parking and public service facilities. All tested underground spaces exhibited significant spatial autocorrelation. Geographical detector analysis identified significant driving forces of socio-economic conditions, land use, and urban location on UUS development. However, the benchmark land price, reflecting urban land scarcity, was the major factor to promote UUS development in the main city. In contrast, it exhibited the lowest driving force in the suburbs, which primarily served as an ancillary functional space for surface buildings. To improve UUS development quality, differentiated planning policies should be implemented in the main city and the suburbs. Research findings can provide insights for higher efficiency of UUS utilization and more effective planning policies in global megacities.

Metro systems serve as the backbone of urban mobility and are pivotal to the resilience of modern metropolises. However, metro networks are continuously evolving infrastructures, yet current resilience assessments predominantly rely on static snapshots, failing to capture how the network resilience evolves during expansion. Therefore, this study introduced an integrated resilience assessment framework characterized as dynamics of dynamics, evaluating the evolutionary trajectory of network resilience in the face of complex disruptions. We constructed temporal complex networks for 12 global cities, integrating serviceability with refined topological modeling. By subjecting these evolving networks to critical node, region, and line disruptions at bi-level intensities, we identified distinct patterns of resilience evolution. The results revealed the evolution of serviceability efficiency and dynamic resilience. Furthermore, the study provided a more nuanced perspective on the relationship between metro network configuration and resilience evolution. While connectivity is generally beneficial, our analysis suggested that it may not always guarantee enhanced robustness in all contexts. The findings underscore the value of looking beyond the static resilience of metro networks, and the proposed framework offers urban planners a prognostic tool to optimize metro network expansion to enhance resilience.

Metro-led underground spaces (MUS) have gained significant importance in addressing deteriorating urban issues in high-density built environments. However, existing planning techniques for MUS lack could enable the increasingly complex spatial morphology and function assignment, resulting in poor performance of MUS development in an unintegrated manner. To bridge the research gap, an enhanced layout planning approach for MUS (ELPA-MUS) was systematically formulated. ELPA-MUS incorporated a digital interpretation framework for MUS layout, enabling simultaneous analysis of spatial morphology and function. The model transformed the layout planning task into a multi-objective optimization (MOO) problem with nine objective functions. The non-dominant sorting genetic algorithm III (NSGA-III) was employed to find the Pareto front in high dimensions. To enhance the practicality of ELPA-MUS, an ensemble method was proposed, combining subjective expertise and objective computational analytics. The model was applied to a case study in Jinan, China to demonstrate its applicability and rationality. Overall, the ELPA-MUS model provided a modifiable paradigm for intelligent layout planning of complex underground spaces and expanded the data-driven planning toolkits towards a more

Urban underground space (UUS) development, guided by prudent planning, has emerged as a vital solution to the increasingly complex issues of urban built environments globally. Driven by the growing needs for human-centric urban design, low-carbon development, enhanced urban resilience, and alignment with sustainable development goals, UUS planning is rapidly shifting from experience-based approaches to evidence-based and data-driven methodologies. Yet, the broader landscape of this research field remains ambiguous, with the characteristics and future trajectories of such emerging planning technologies still to be clearly delineated. To this end, this systematic review delves into the burgeoning field of data-informed planning technologies for underground space (DIPTUS), examining how data-driven methods are revolutionizing the planning, design, and management of underground environments. Through a comprehensive bibliometric analysis of 134 articles published from 2014 to 2024, we identified key trends and mapped research themes within DIPTUS. Our narrative synthesis evaluated DIPTUS advancements across three dimensions: sensing and measurement, pattern and model, and planning and governance. The results indicate that DIPTUS exploits diverse data streams to quantitatively analyze UUS development. Utilizing advanced analytical tools such as spatial statistics, machine learning, and causal inference, these technologies uncover utilization patterns and planning optimization strategies. The review also underscores the increasing integration of planning and governance within DIPTUS, merging resource evaluation and demand forecasting, layout planning optimization, development benefits and spatial performance evaluation into a cohesive framework. Enhancements in 3D cadastral systems, innovative management models, and digital twin technologies further bolster this integrated approach. Despite significant strides, challenges in data integration, model complexity, and practical application persist. Lastly, we proposed a visionary framework to address these issues through interdisciplinary research and robust model development, aiming to fully harness DIPTUS’s transformative potential for sustainable, resilient, and human-centered urban environments.

Metro systems are essential for urban functionality worldwide, and their resilience is a growing concern. While network analysis has offered insights into structural resilience, a comprehensive understanding of dynamic resilience, particularly its temporal evolution in expanding networks and response to multifaceted disruptions, remains underdeveloped. Previous research has often focused on large systems in a few megacities, neglecting smaller networks. This study addresses these gaps by introducing a novel framework to evaluate the dynamic resilience of evolving metro networks. We compiled data for twelve global cities, modeling their metro systems as evolving complex networks, incorporating geographic coordinates, station opening dates, and catchment area population data. A key contribution is a comprehensive resilience metric that integrates network serviceability, based on population-weighted global efficiency, and quantifies vulnerability as the rate of efficiency loss per disrupted node. We formulated three universally applicable disruption scenarios, including critical node, critical region, and critical line disruptions. Each disruption was simulated at both light (10% node removal) and heavy (20% node removal) intensities, reflecting diverse real-world disruption scenarios. These strategies leverage a comprehensive node centrality index derived from degree, closeness, and betweenness centralities, with edge weights based on reciprocal Euclidean geodesic distances. Applying this framework, we analyzed the resilience evolution across the 12 case study cities, uncovering distinct and common patterns. Findings indicate that dynamic resilience provides critical insights complementary to static efficiency measures and that resilience trajectories are highly dependent on disruption size, intensity, and city-specific network characteristics. This study offers a robust methodology for assessing metro network resilience evolution, providing data-driven insights to enhance the robustness of critical public transport systems and inform strategies for developing more resilient cities.

Urban underground public spaces are increasingly recognised as crucial for enhancing urban functionality and liveability. However, effectively integrating public cognition into underground public space planning remains challenging. This study addresses this gap by proposing a novel approach integrating natural language processing, knowledge graphs and complex network theory to systematically mine public cognition from WeChat official account articles and Web of Science abstracts. Integrating the principles of linguistics and semiotics, we developed a novel approach for knowledge elements extraction and vectorised knowledge graph construction. This yielded 15,335 Chinese and 10,589 English effective knowledge elements, which were used to construct and compare knowledge graphs representing the cognitive structures of social and academic communities. Findings reveal distinct priorities, with the social community emphasising experiential aspects while the academic community focuses on theoretical concepts. Network analysis underscores the scale-free nature of both graphs, with higher centrality in the social network. These insights offer valuable implications for developing tailored public participation strategies in underground space planning, promoting more inclusive and human-centred urban development.

Metro-led underground public spaces (MUPS) have become integral components of modern urban environments, particularly in high-density cities. However, their enclosed nature and artificial characteristics present various challenges affecting user experience and well-being. While traditional surveys and questionnaires have provided valuable insights into public perception of these spaces, such methods are often resource-intensive and limited in scale. This study proposes an innovative framework for evaluating public perception in MUPS by leveraging social media data and Large Language Model (LLM) technology. We developed a six-dimensional perception indicator system termed “FEPICS” (Functionality, Engagement, Pleasurability, Inclusiveness, Comfort, and Safety), encompassing 37 distinct indicators. Using Google Maps review data from eight metro stations along Hong Kong’s Tsuen Wan Line, we employed LLM-based classification methods to extract and quantify public perception information, achieving a semantic recognition accuracy of 91,4%. Our analysis revealed that the “Functionality” dimension received the highest public attention, while “Inclusiveness” and “Comfort” garnered relatively less focus. The indicator “transfer” demonstrated the highest positive perception value, whereas “crowd congestion” exhibited the strongest negative sentiment. Through our perception evaluation index, we identified Jordan and Tsim Sha Tsui stations as best performers, attributable to their superior environmental design elements despite high crowding levels. These findings highlight the importance of balancing functional efficiency with environmental quality in MUPS design. The proposed FEPICS framework and LLM-based methodology offer a systematic approach for understanding and quantifying public perception in underground spaces, contributing to evidence-based planning practices. This study demonstrates the potential of integrating social media analytics with advanced language models for urban perception research, while providing practical insights for optimizing underground public space development.

Since China’s reform and opening-up, expanding urban areas has been mainstreaming to meet the demands of society in the face of rapid urbanization. Many of these contemporary new districts still face common urban problems such as disorderly land construction, negative built environment, and severe traffic congestion. Against this backdrop, the underground space presents a valuable urban resource that can significantly influence sustainable urban development and alleviate complex urban issues. To illustrate this point, this paper examines the case study of Jinan sub-central city, covering an area of approximately 35 km2. This paper will generalize the Chinese modern garden city concept and integrate it into the preparation of objectives, methods, schemes, and construction modes, establishing the green, ecological, and sustainable underground space in the new district. These advanced concepts and practice work from the case study will provide potential support and assistants for the future underground space construction in new districts.

Metro-led underground space (MUS) plays a crucial role in urban underground utilization. Extensive engineering cases have shown that optimizing the disaster resistance ability of MUS has practical significance. However, current research mostly focused on qualitative exploration from the perspective of structural disaster prevention, which proposed the strategies typically targeting the interior of single buildings, lacking coupling to the surrounding space. In order to fill the gap, this study models the MUS into a topological network, measures the robustness of MUS, formulates a MUS robustness evaluation model, and selects two disturbance modes namely random attack and deliberate attack. Then this study sets People's Square Station and Wujiaochang Station in Shanghai, China as cases, computes the classic indexes, analyzes the performance of each case, and summarizes the layout indications.

The rapid expansion of urban underground space (UUS) has become increasingly popular in densely populated urban areas worldwide. Although data-driven technology has facilitated the planning process successfully, the implementation mechanism of UUS planning remains obscure, potentially undermining the spatial performance. To address this gap, this study employs fuzzy-set qualitative comparative analysis (fsQCA) to analyze the causality of UUS development. Three primary models of UUS development-separate, interconnected, and integrated models-are categorized. Causal conditions and outcomes along with their quantitative metrics are proposed. Subsequently, 30 Chinese cases are analyzed using fsQCA to assess the necessity and sufficiency of UUS development. Three distinct causal paths are identified, namely strong economic strength, robust policy support, and advanced construction technology, which play critical roles in integrated development, while weak economic strength and inadequate policy support lead to separate development. The study underscores the importance of implementing UUS development models and provides valuable insights for UUS planning management.

As a crucial component of urban development and territorial space resources, the urban underground space plays an increasingly important role in the new development stage. This study analyzes the new demand and challenges in the new development stage and proposes development strategies of the urban underground space accordingly. Based on the characteristics and requirements of the new development stage, this study analyzes the strategic demand for urban underground space utilization from the perspectives of territorial space planning system, green and low-carbon resilience concepts, and urban renewal modes. Subsequently, it proposes a series of new challenges to be addressed for urban underground space development in the new development stage. These challenges include the deficiency in territorial resource investigation and assessment, ambiguity in underground bearing capacity, deficiency in exploiting the low-carbon and resilient potentials of the urban underground space, and imbalance in resource allocation and ambiguity in development mechanism of the infrastructure. Furthermore, eight priority tasks and corresponding development suggestions are proposed from the aspects of legal and administrative system development, smart management of resources, planning theories and methods, data-driven paradigm, deep space utilization, spatial reconfiguration of built space, integral development of new areas, and regulation of metro-led space. This study is expected to provide insights for the legislation, planning, construction, and management of the urban underground space.

Metro-led underground space (MUS) use has gained unprecedented popularity in China over the past decade. Empirical studies indicated that the development of MUS, which primarily aligns with transit-oriented development, is likely to boost urban vitality, enhance spatial efficiency, and optimize spatial configuration. However, the development regularities remain unclear for MUS in high-density built environment of Chinese cities, resulting in a lack of theoretical foundation for MUS planning. To bridge the research gap, we adopted a set of spatial statistics techniques and formulated a quantitative indicator system to decipher the underpinning patterns of MUS use based on multisource big data. We probed into the driving forces of MUS in China at multiscale from a spatiotemporal perspective. Finally, the driving patterns of MUS in different spatiotemporal conditions were identified and compared in a systematic manner. The study provides insights into the increasingly growing MUS use, and supports the optimization of planning theory for MUS in China.

Urban underground infrastructures (UUIs) are a vital component of built capital for urban sustainability. However, many cities are now home to a multitude of disused or underutilized UUIs, not least aged purpose-built underground facilities, causing a waste of valuable underground space resource assets. In the process of urban renewal, adaptive reuse can be an attractive solution to breathe new life into underutilized UUIs, while addressing some of the modern problems of the built environment by an economically feasible means. Nevertheless, there is a prevalent absence in the current literature of the overarching planning and decision-making approaches for an adaptive reuse development of underutilized UUIs. With the intention of addressing this shortfall, this paper first lays out development strategies, then sets the generic patterns for adaptive reuse of disused or underutilized UUIs. Taking the city of Qingdao, China as a case study, detailed planning and decision-making approaches with the aid of multi-source data and spatial analysis tools are presented. It is anticipated that the findings of this research will assist the adaptive reuse development of UUIs in providing theoretical guidance and empirical evidence, thereby enhancing the role of urban underground space use in contributing to urban revitalization and urban sustainability.

Urban underground space (UUS) development has been acknowledged as a positive contribution to urban resilience (UR). Such contribution has been qualitatively addressed in recent years, but only quantitatively discussed in few studies. Quantitative evaluation methods for UR are widely used in China and around the world, but the role of underground space is barely included. This paper provides a way to bridge this gap on the city scale. A UR evaluation framework was carefully constructed that covers the basic aspects and elements of UR. The contributions of UUS to UR were identified and integrated into the UR evaluation framework, and the measurement methods for each indicator related to UUS were determined. A case study of 19 sample cities in China were conducted using the integrated evaluation model. Correlation analysis and clustering analysis were further adopted to interpret the evaluation results, mainly with three indicators reflecting the level of UUS development, namely UUS area (m2), UUS density (104 m2/km2) and UUS area per capita (m2/person). The results showed a strong correlation between UUS area and UR. The average proportion of UR provided by UUS in the 19 sample cities was 16.46%, while the maximum figure reached 29.20%. The sample cities were clustered into four categories based on the relationship between the proportion of UR provided by UUS, UUS area, and GDP per capita, where both high and low UUS area tend to provide less proportion of resilience than the medium UUS area. Corresponding suggestions for UUS utilization were proposed to assist cities in achieving urban resilience.

Nowadays, the use of metro-led underground spaces (MUS) has become imperative in high-density urban environments. However, a comprehensive and systematic understanding of MUS development patterns in densely-populated countries like China is still lacking. Therefore, this study employed a data-driven spatiotemporal analytical framework based on multisource big data to investigate the development mechanism of Chinese MUSs using a full sample. The multiscale analysis, covering MUS, urban, and regional scales, was conducted to identify the driving forces of MUS development over five temporal stages. The results revealed that the development characteristics of Chinese MUSs varied across different spatiotemporal contexts. At the micro level, the primary driving factors, including density, diversity, distance to transit, and destination accessibility, had a strong explanatory power, which is consistent with the mainstream transit-oriented development (TOD) theory. At the macro level, the main drivers included economic factors, environmental factors, and development scale factors. This study provides new insights for MUS development in high-density regions worldwide, and the obtained regularities could be effectively adopted in the planning and design for MUS.

城市韧性是一个持续的研究热点,在我国城市地下空间蓬勃发展的背景下,城市地下空间韧性成为一个新兴的研究焦点,如何评价地下空间韧性是一个亟待解决的关键问题.基于城市韧性评价指标的构建准则,建立面向城市地下空间韧性的指标体系,包含10个分项指标,囊括组织机构韧性、基础设施韧性、环境韧性、社会韧性和经济韧性5个维度,并基于熵权法形成了城市地下空间韧性评估模型.利用该模型对我国具有代表性的7个城市进行评估并分级,探索影响城市地下空间韧性的关键因素.结果表明,组织机构韧性是对韧性影响最大的维度,法律法规是权重最高的单项评价指标.在接受评估的7个城市中,上海市具有最高的地下空间韧性,并且是唯一属于地下空间高韧性的城市.将该模型结果与已有的地下空间综合实力模型结果对比,验证了本模型的合理性,同时表明地下空间的韧性发展与综合发展并不完全同步,需要针对性地制定韧性策略.根据计算结果,提出3个方面提升地下空间韧性的策略,即推进韧性地下空间立法、加强地下基础设施建设以及强化地下空间事故学习.研究得出的模型适用于评价一组地下空间开发水平较为接近的城市,评价结果能够为决策者展示地下空间韧性薄弱环节,为制定韧性提升政策与方案提供依据.

In recent years, the comprehensive and extensive development of urban underground space (UUS) has gained substantial popularity with the efficient guidance of UUS planning. This study discussed the research trends and paradigm shift in UUS planning over the past few decades. Bibliometric and comparative studies were conducted to identify the contributions of the research in this field. The analysis identified the overall temporal development trend of UUS planning and the research hot spots, namely, the primary use of UUS and UUS planning technology. Additionally, the study identified academic collaborative relationships through country and institution co-occurrence network analysis. The diversified development philosophy, planning systems, key planning scenarios, and data-driven technology pertaining to UUS planning have been extracted through keyword co-occurrence network analysis. Moreover, the planning systems, planning management, and planning practices for UUS in various countries, including Singapore, Japan, Finland, Canada, and China, were also systematically reviewed. By doing so, the worldwide UUS planning evolution has been identified. The paradigm shift for UUS planning has been clarified, involving technical method, result form, control mode, and control elements. Furthermore, the conceptual data-driven framework for UUS planning, which orients multiple development concepts, has been proposed to meet the requirement of next frontier development.