Search Results (45)

Nowadays, urban rail transit is expanding towards high-elevation zones, and the effect of the low air pressure environment on the pantograph-catenary system is becoming increasingly prominent. As a key indicator for evaluating the electrical contact performance of a pantograph-catenary system, research on the electrical characteristics of the pantograph-catenary arc is of great significance. For this reason, this paper established a plasma mathematical model applicable to the arc of the urban rail transit bow network based on the theory of magnetohydrodynamics. The mathematical model of the pantograph-catenary arc was used to set the relevant initial conditions. Based on COMSOL Multiphysics finite element simulation software, this study developed a multi-physics simulation model of the pantograph-catenary arc and systematically analysed its voltage characteristics and current density distribution under varying air pressure conditions. The results showed that as the air pressure decreases, the potential at the axial points declines, the pressure drop across the arc poles becomes more pronounced, and the current density decreases accordingly. This study provides theoretical and technical support for optimizing the design of and promoting the sustainable development of urban rail transit pantograph-catenary systems in high-altitude areas. Full article

Urban rail transit is a key enabler for optimizing urban spatial structures, and its interactive relationship with land use has long been a focus of attention. However, existing studies suffer from scattered methodologies, a lack of systematic analysis, and insufficient dynamic insights into global trends. This study comprehensively employs CiteSpace, VOSviewer, and Scimago Graphica to conduct bibliometric and knowledge map analysis on 1894 articles from the Web of Science database between 2004 and 2024, focusing on global research trends, collaboration networks, thematic evolution, and methodological advancements. Key findings include the following: (1) research on rail transit and land use has been steadily increasing, with a significant “US-China dual-core” distribution, where most studies are concentrated in the United States and China, with higher research density in Asia; (2) domestic and international research has primarily focused on themes such as the built environment, value capture, and public transportation, with a recent shift toward artificial intelligence and smart city technology applications; (3) research methods have evolved from foundational 3S technologies (GIS, GPS, RS) to spatial modeling tools (e.g., LUTI model, node-place model), and the current emergence of AI-driven analysis (e.g., machine learning, deep learning, digital twins). The study identifies three future research directions—technology integration, data governance, and institutional innovation—which provide guidance for the coordinated planning of transportation and land use in future smart city development. Full article

Jobs–housing balance is a critical concern in urban planning and sustainable economic development. Urban rail transit, as a key determinant of employment and residential location decisions, plays a pivotal role in shaping jobs–housing dynamics. Beijing, the first Chinese city to develop a subway system, offers a comprehensive rail network, making it an ideal case for exploring the effects of transit density on jobs–housing balance. This study utilizes medium-scale panel data from Beijing (2009–2022) and employs a fixed-effects model to systematically examine the impact of rail transit station density on jobs–housing balance and its underlying mechanisms. The results indicate that increasing transit station density tends to aggravate jobs–housing separation overall, with pronounced effects in central and outer suburban areas but negligible effects in near suburban areas. Mechanism analysis reveals two primary pathways: (1) improved accessibility draws employment toward transit-rich areas, reinforcing the attractiveness of central districts; (2) rising housing prices elevate residential thresholds, pushing lower-income populations toward outer suburbs. While enhanced transit density improves commuting convenience, it does not effectively reduce jobs–housing separation. These findings offer important policy implications for optimizing transit planning, improving jobs–housing alignment, and promoting sustainable urban development. Full article

High-speed rail (HSR) station areas play a vital role in shaping urban form, stimulating economic activity, and enhancing spatial vitality. Understanding the factors that influence this vitality is key to supporting sustainable urban development and transit-oriented planning. This study investigates 66 HSR station areas in 35 Chinese cities by integrating multi-source data—Sina Weibo check-in records, urban support indicators, station attributes, and built environment variables—within a city–node–place analytical framework. Using Multiple Linear Regression (MLR) and Light Gradient Boosting Machine (LightGBM) models, we identify key drivers of spatial vitality, while SHAP analysis reveals nonlinear and interaction effects. The results show that city population size, urbanization level, commercial land use, transit accessibility, and parking facilities significantly enhance station area vitality. However, diminishing returns are observed when commercial land and bus stop densities exceed certain thresholds. The station location index shows a negative correlation with spatial vitality. The analysis of interaction effects highlights strong synergies between urban development and functional configuration, as well as between accessibility and service infrastructure. Different station types exhibit varied spatial patterns and require differentiated strategies. This study offers empirical insights for aligning transport infrastructure and land use planning, supporting the development of vibrant, accessible, and sustainable HSR station areas. Full article

Against the backdrop of rapid rail transit development, this study takes Urumqi Metro Line 1 as a case, using geographic information system (GIS) spatial analysis and space syntax Pearson correlation coefficient methods. Focusing on an 800 m radius around station areas, the research investigates the distribution characteristics of commercial facilities and the impact of metro development on commercial patterns through the quantitative analysis and distribution trends of points of interest (POI) data across different historical periods. The study reveals that following the opening of Urumqi Metro Line 1, commercial facilities have predominantly clustered around stations including Erdaoqiao, Nanmen, Beimen, Nanhu Square, Nanhu Beilu, Daxigou, and Sports Center, with kernel density values surging by 28–39%, indicating significantly enhanced commercial agglomeration. Metro construction has promoted commercial POI quantity growth and commercial sector enrichment. Surrounding commercial areas have developed rapidly after metro construction, with the most significant impacts observed in the catering, shopping, and residential-oriented living commercial sectors. After the construction of the subway, the distribution pattern of commercial facilities presents two kinds of aggregation patterns: one is the original centripetal aggregation layout before construction and further strengthened after construction; the other is the centripetal aggregation layout before construction and further weakened after construction, tending to the site level of face-like aggregation. The clustering characteristics of different business types vary. Factors such as subway accessibility, population density, and living infrastructure all impact the distribution of businesses around the subway. The impact of subway accessibility on commercial facilities varies by station infrastructure and urban area. The findings demonstrate how transit infrastructure development can catalyze sustainable urban form evolution by optimizing spatial resource allocation and fostering transportation–commerce synergy. It provides empirical support for applying the theory of transit-oriented development (TOD) in the urban planning of western developing regions. The research not only fills a research gap concerning the commercial space differentiation law of metro systems in megacities in arid areas but also provides a scientific decision-making basis for optimizing the spatial resource allocation of stations and realizing the synergistic development of transportation and commerce in the node cities along the “Belt and Road”. Full article

Modern cities are facing increasing challenges such as traffic congestion, high energy consumption, and poor air quality, making rail transit systems, known for their high capacity and low emissions, essential components of sustainable urban infrastructure. While numerous studies have examined how the built environment impacts transit ridership, the complex interactions among these factors warrant further investigation. Recent advancements in the reasoning capabilities of large language models (LLMs) offer a robust methodological foundation for analyzing the complex joint influence of multiple built environment factors. LLMs not only can comprehend the physical meaning of variables but also exhibit strong non-linear modeling and logical reasoning capabilities. This study introduces an LLM-based framework to examine how built environment factors and station characteristics shape the transit ridership dynamics by utilizing DeepSeek-R1. We develop a 4D + N variable system for a more nuanced description of the built environment of the station area which includes density, diversity, design, destination accessibility, and station characteristics, leveraging multi-source data such as points of interest (POIs), road network data, housing prices, and population data. Then, the proposed approach is validated using data from Qingdao, China, examining both single-factor and multi-factor effects on transit peak-hour ridership at the macro level (across all stations) and the meso level (specific station types). First, the variables that have a substantial effect on peak-hour transit ridership at both the macro and meso levels are discussed. Second, key and latent factor combinations are identified. Notably, some factors may appear to have limited importance at the macro level, yet they can substantially influence the peak-hour ridership when interacting with other factors. Our findings enable policymakers to formulate a balanced mix of soft and hard policies, such as integrating a flexitime policy with enhancements in active travel infrastructure to increase the attractiveness of public transit. The proposed analytical framework is adaptable across regions and applicable to various transportation modes. These insights can guide transportation managers and policymakers while optimizing Transit-Oriented Development (TOD) strategies to enhance the sustainability of the entire transportation system. Full article

With the gradual increase in the total volume of underground commerce in cities, underground commercial spaces are increasingly becoming a key carrier for breaking the constraints of land resources and reconfiguring the relationship between people and land. This paper quantifies and visualizes the layout and scale of underground commercial spaces in the central urban area of Qingdao by using kernel density, multi-distance spatial clustering, and spatial autocorrelation analysis and analyzes the influencing factors by using the geographical detector and MGWR model. The research results show that the underground commercial spaces in the central urban area present a “multi-core–multi-level” layout pattern, and high-density areas are more likely to cluster, with the most significant clustering scale being 3.39 km. Commercial supporting facilities, development of underground space, and population heat value are the core driving factors. The impact of rail transit, centrality, commercial supporting facilities, and development of underground space on the east coast urban area is much greater than that on the west and north urban areas. Finally, corresponding strategies are proposed from the perspectives of business districts, station areas, supply and demand, and planning and management to optimize the development and layout of underground commercial spaces, so as to promote the organic integration of underground commercial spaces and urban spaces. Full article

Understanding how land use affects urban rail transit (URT) ridership is essential for facilitating URT usage. While previous studies have explored the way that land use impacts URT ridership, few have figured out how this impact evolves over time. Utilizing URT turnstile and land use data in Beijing, we employed panel data analysis methods to verify the existence of the temporal heterogeneity of the impact and capture this temporal heterogeneity. The results identified time-varying impacts of land use on the URT boarding and alighting trips on weekdays and non-weekdays and also demonstrated the rationality of the mixed effects time-varying coefficient panel data (TVC-P) model in capturing this temporal heterogeneity accurately. The TVC-P model revealed how land use density appealed to URT commuting during weekday morning peak times, and how it triggered the generation of URT commutes during the weekday evening rush hours. The land use diversity promoted URT trips over an extended period on non-weekdays. Additionally, the study identified the time-varying impacts of specific land use on URT ridership. These insights provide both theoretical and empirical support for developing policies and actions that improve the efficiency of transportation systems and foster alignment between land use and transport. Full article

As urbanization accelerates, megacities are facing challenges such as inefficient land use and traffic congestion, particularly in the context of rail transit-oriented development, where land use optimization remains a significant research gap. Current urban planning still relies heavily on the experience and intuition of government planning departments, without achieving quantitative, intelligent, and scientific decision making. This study takes Panda Avenue Subway Station as a case study to analyze the evolution of land use patterns around subway stations and explore optimization strategies to enhance land development efficiency and spatial utilizationTo fill this research gap, this paper proposes a CNN-AIMatch model based on machine learning algorithm and an enhanced PLUS-Markov prediction model using the increase and decrease of floor area ratio as a control measure, which adopts an increase in plot ratio as a control measure to improve the accuracy of the Kappa coefficient in different plot ratio scenarios and the prediction of 3D urban spatial growth trends. The model effectively overcomes the limitations of the conventional 2D perspective in predicting urban expansion. By simulating urban renewal and ecological preservation scenarios, it provides an innovative solution for land use pattern optimization and plot ratio control at the block level in subway station areas. The goal of this study is to optimize land use and floor area ratio control strategies through the application of this model, intelligently respond to the challenges of high-density development and quality of life assurance, achieve the best use of land, and promote sustainable urban development and the construction of smart cities. Full article

Against the backdrop of the newly constructed urban rail transit network and the ongoing urbanization of strip-shaped cities, this study investigates the distribution and evolution of commercial points of interest (POIs) in the central urban area of Lanzhou. The research analyzes data from three distinct years (2016, 2018, and 2020) to observe the temporal changes in commercial entities before and after the establishment of metro stations. Stable explanatory variables influencing the distribution and evolution of commercial POIs are identified, including rail transit passenger flow, demographic characteristics of the working and residential populations surrounding stations, as well as building and road densities in their vicinity. Through statistical analysis and model construction, these influencing factors are systematically evaluated to establish a relatively stable linear regression equation that quantifies the weights assigned to each factor. This study enhances our understanding of how urban rail transit impacts urban vitality within belt-shaped cities while elucidating its positive role in shaping development patterns unique to such areas. It clarifies the relationship between changes in urban vitality and spatial configuration, thereby providing valuable insights for urban planners and decision-makers. Furthermore, this research can serve as a reference model for other strip-shaped cities seeking to optimize their distribution of urban vitality through the effective utilization of urban rail transit systems. Full article

Urban rail network vulnerability assessment has become the core of the urban public transport system. Identifying and quantifying the vulnerability of urban rail transit is the key to coping with the crisis situation of the urban rail transit system. The article uses the Space L method based on complex network theory to establish a topological model of the Chongqing rail transit line network, analyze the topological properties of the network, and use MATLAB 2020b software to conduct deliberate attacks on each station to assess the vulnerability of the Chongqing rail transit network in terms of changes in network efficiency. The results show that the density of Chongqing’s rail transit network is low and the network level needs to be improved; there is no significant correlation between the node vulnerability and the degree value of nodes in the network. The identification of important stations can provide a basis for the decision-making of urban rail transit operation managers and has a strong practical value. Full article

Urban rail transit passenger flow forecasting often relies on the traditional “four-step” method, where the division of traffic analysis zones (TAZs) is critical to ensuring prediction accuracy. As the fundamental units for describing trip origins and destinations, TAZs also encompass socioeconomic attributes such as land use, population, and employment. However, traditional TAZs, typically based on administrative boundaries, fail to reflect evolving urban travel behavior, particularly when transit stations are located near TAZ boundaries. Additionally, the emergence of urban big data allows for more refined spatial analyses based on individual travel patterns, addressing the limitations of administrative divisions. This study proposes an innovative TAZ aggregation model based on travel similarity, integrating public transit smart-card data and GIS data from bus networks. First, individual spatiotemporal travel patterns are mapped and discretized in both the spatial and temporal dimensions. Travel characteristic data are then extracted for spatial grid units. The TAZ division problem is defined as a multiobjective optimization problem, including factors such as travel similarity, the homogeneity of travel intensity, the statistical accuracy of the area, geographic information preservation, travel ratio constraints, and shape constraints. Multiple TAZ division schemes are produced and assessed using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), resulting in the selection of the optimal scheme. The proposed method is implemented on bus passenger travel data in Beijing, showing that the optimized scheme significantly reduces the number of zones with travel ratios exceeding 10%. Compared with existing schemes, the optimized division yields more uniform distributions of travel ratios, area, and travel density, while significantly minimizing the number of zones with a high travel concentration. These results demonstrate that the proposed method better reflects residents’ actual travel behaviors, offering a notable improvement over traditional approaches. This research provides a novel and practical framework for data-driven TAZ optimization. Full article

The revitalization and renewal of existing urban space is a primary objective in the redevelopment of high-density transit-oriented development (TOD) areas. In this context, offering incentive zoning bonuses is a critical tool for optimizing urban space. However, in some subway-adjacent plots with high building densities, traditional incentive zoning methods face limitations due to insufficient horizontal space. These areas increasingly rely on multi-ground public spaces to balance density with public services. This study investigates new methods of incentive zoning between commercial areas and public spaces in multi-ground public spaces within subway-adjacent plots, using 33 rail transit complexes in Shanghai, China, as the research subject. The findings are presented in the form of visual guidelines to provide guidance on architectural control, with the goal of enhancing the quality of urban public spaces. In this research, a multiple linear regression model is employed, using GNCS_AR (the ratio of ground non-commercial stay to area), which captures both efficiency and equity in public space quality, as the dependent variable. A model is developed in SPSS, incorporating independent variables such as TCA (total commercial area), POS (public open space area), PIS (public indoor space area), and MGZFs (multi-ground zoned floors). This model provides a framework for developers to manage and control public space in multi-ground settings within rail transit complexes. Research has found that MGZFs alone cannot be included as the independent variable in the model, as their absence leaves the model unable to explain three-dimensional spaces. However, incorporating the ratio of RIOPS (the ratio of indoor to outdoor public space) to MGZFs significantly improves the model’s correlation and explanatory capacity. The resulting model demonstrates that, under different POS and MGZF tiers, the influence of PIS and TCA on public space quality varies. Using a typological approach, the study categorizes these complexes into five tiers based on POS and MGZFs. Within the same tier, changes in PIS and TCA types lead to variations in public space quality. The empirical results are translated into diagrams that link data, forms, and indicators to guide the development of three-dimensional spaces. These diagrams, which can be named visual guidelines, provide practical guidelines for optimizing public spaces in these subway-adjacent plots. Full article

Over the last two decades, the United Arab Emirates (UAE) has experienced significant urban growth, prompting the Dubai Roads and Transport Authority (RTA) to advocate for sustainable transport solutions. This led to the implementation of the Light Rail Transit (LRT) to address urban mobility, environmental sustainability, and energy efficiency. Dubai has strategically prioritized infrastructure and transportation network expansion to support its rapid development. This paper aims to examine the critical role of the LRT system, particularly the metro and tramway, in steering Dubai towards sustainability. Metro and tramway systems offer crucial high-capacity public transport, enhance connectivity, stimulate economic growth, and contribute to a sustainable environment. The study assesses the transformative impact of the Dubai Metro on urban development, focusing on key stations like Jabal Ali, Al-Barsha First, and Business Bay. Using qualitative research methods, including GIS, spatial maps, interviews, case studies, and land use investigations, the research analyzes population density, connectivity, accessibility, and urban land use patterns around these stations. Results indicate a positive impact of the Dubai Metro on both commercial and residential land use, improved connectivity, and enhanced accessibility, reinforcing its role in cultivating a sustainable urban environment. Full article

The design of transportation networks is generally performed on the basis of the division of a metropolitan region into communities. With the combination of the scale, population density, and travel characteristics of each community, the transportation routes and stations can be more precisely determined to meet the travel demand of residents within each of the communities as well as the transportation links among communities. To accurately divide urban communities, the original word vector sampling method is improved on the classic Deepwalk model, proposing a Random Walk (RW) algorithm in which the sampling is modified with the generalized travel cost and improved logit model. Urban spatial community detection is realized with the K-means algorithm, building the F-Deepwalk model. Using the basic road network as an example, the experimental results show that the Deepwalk model, which considers the generalized travel cost of residents, has a higher profile coefficient, and the performance of the model improves with the reduction of random walk length. At the same time, taking the Shijiazhuang urban rail transit network as an example, the accuracy of the model is further verified. Full article