Search Results (162)

Urban rail transit networks’ rapid expansions have led to increasing intersections between existing and new lines, particularly in dense urban areas where new stations must underpass existing infrastructure at zero distance. Deformation controls during construction are critical for maintaining the operational safety of existing stations, especially in soft soil conditions where construction-induced settlement poses significant risks to structural integrity. This study systematically investigates the influence mechanisms of different construction schemes on base plate deformation when an open-top UBIT (underground bundle composite pipe integrated by transverse pre-stressing) underpasses existing stations. Through precise numerical simulation using PLAXIS 3D, the research comparatively analyzed the effects of 12 pipe jacking sequences, 3 pre-stress levels (1116 MPa, 1395 MPa, 1674 MPa), and 3 soil chamber excavation schemes, revealing the mechanisms between the deformation evolution and soil unloading effects. The continuous jacking strategy of adjacent pipes forms an efficient support structure, limiting maximum settlement to 5.2 mm. Medium pre-stress level (1395 MPa) produces a balanced deformation pattern that optimizes structural performance, while excavating side chambers before the central chamber effectively utilizes soil unloading effects, achieving controlled settlement distribution with maximum values of −7.2 mm. The optimal construction combination demonstrates effective deformation control, ensuring the operational safety of existing station structures. These findings enable safer and more efficient urban underpassing construction. Full article

Establishing a high-precision passenger flow prediction model is a critical and complex task for the optimization of urban rail transit systems. With the development of artificial intelligence technology, the data-driven technology has been widely studied in the intelligent transportation system. In this study, a neural network model based on the data-driven technology is established for the prediction of passenger flow in multiple urban rail transit stations to enable smart perception for optimizing urban railway transportation. The integration of network units with different specialities in the proposed model allows the network to capture passenger flow data, temporal correlation, spatial correlation, and spatiotemporal correlation with the dual attention mechanism, further improving the prediction accuracy. Experiments based on the actual passenger flow data of Beijing Metro Line 13 are conducted to compare the prediction performance of the proposed data-driven model with the other baseline models. The experimental results demonstrate that the proposed prediction model achieves lower MAE and RMSE in passenger flow prediction, and its fitted curve more closely aligns with the actual passenger flow data. This demonstrates the model’s practical potential to enhance intelligent transportation system management through more accurate passenger flow forecasting. 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

Urban rail transit (URT) systems are critical for sustainable urban mobility but are increasingly vulnerable to disruptions and emergencies. While extensive research has examined the built environment’s influence on transit demand under normal conditions, the nonlinear causal mechanisms shaping URT passenger flow during emergencies remain understudied. This study proposes an artificial intelligence-based causal machine learning framework integrating causal structure learning and causal effect estimation to investigate how the built environment, network structure, and incident characteristics causally affect URT station-level ridership during emergencies. Using empirical data from Shanghai’s URT network, this study uncovers dual pathways through which built environment attributes affect passenger flow: by directly shaping baseline ridership and indirectly influencing intermodal connectivity (e.g., bus connectivity) that mitigates disruptions. The findings demonstrate significant nonlinear and heterogeneous causal effects; notably, stations with high network centrality experience disproportionately severe ridership losses during disruptions, while robust bus connectivity substantially buffers such impacts. Incident type and timing also notably modulate disruption severity, with peak-hour incidents and severe disruptions (e.g., power failures) amplifying passenger flow declines. These insights highlight critical areas for policy intervention, emphasizing the necessity of targeted management strategies, enhanced intermodal integration, and adaptive emergency response protocols to bolster URT resilience under crisis scenarios. Full article

As urbanization has accelerated, rail transit has evolved from being a mere means of transportation to a public area that houses the city’s cultural memory and serves as a crucial portal for the public to understand the culture of the city. As an urban public space with huge passenger flow, the metro (or subway) cultural space has also become a public cultural space, serving communal welfare and representing the image of the city. It is currently attracting more and more attention from the academic community. Wuhan, located in central China, has many subway lines and its engineering construction has set several national firsts, which is a typical sample of urban subway development in China. In this study, we use Python 3.13.0 crawler technology to capture the public’s comments on cultural space of Wuhan metro in social media and adopt SnowNLP sentiment score and LDA thematic clustering analysis to explore the overall quality, distinct characteristics, and deficiencies of Wuhan metro cultural space construction, and propose targeted design optimization strategies based on this study. The main findings are as follows: (1) The metro cultural space is an important window for the public to perceive the city culture, and the public in general shows positive perception of emotions: among the 16,316 data samples, 47.7% are positive comments, 17.8% are neutral comments, and 34.5% are negative comments. (2) Based on the frequency of content in the sample data for metro station exit and entrance space, metro train space, metro concourse and platform space, they are ranked as weak cultural spaces (18%), medium cultural spaces (33%), and strong cultural spaces (49%) in terms of the public’s perception of urban culture. (3) At present, there are certain deficiencies in Wuhan metro cultural space: the circulation paths in concourses and platforms are overly dominant, leaving little space for rest or interaction; the cultural symbols of metro train space are fragmented; the way of articulation between cultural and functional space in the metro station exit and entrance space is weak, and the space is single in form. (4) Wuhan metro cultural space needs to be based on locality landscape expression, functional zoning reorganization, innovative scene creation to optimize the visual symbol system and behavioral symbol system in the space, to establish a good image of the space, and to strengthen the public’s cultural identity and emotional resonance. Full article

As a pivotal node in urban spatial restructuring, the evolution of three-dimensional (3D) compactness in high-speed rail station areas is crucial for sustainable development. However, the existing research predominantly focuses on two-dimensional forms and lacks dynamic analysis and models that are adaptable to complex terrains. This study develops an enhanced 3D gravitational model that integrates satellite imagery and Gaode building data to quantify the spatiotemporal heterogeneity and carry out multidimensional classification of the compactness across 16 stations in the Chengdu-Chongqing urban agglomeration (2015–2025), with driving factors being identified through correlation and regression analyses. The key findings reveal the following: (1) The mean compactness increased by 22.41%, exhibiting nonlinear heterogeneity characterized by high initial values with low growth rates versus low initial values with high growth rates. Spatially, the southern line evolved from a dual-core pattern at the terminals to multigradient development, while the northern line maintained stable growth despite gradient discontinuities. These spatial differentiations resulted from synergistic effects of urban sizes (station hierarchy), terrain features, administrative divisions, and the line affiliation. (2) The built-up land area (under equal study conditions) and vertical development emerged as key drivers, with the building height diversity demonstrating dual spatial effects (enhancing both compactness and aesthetic richness). Complex terrain characteristics were found to promote clustered urban land use and compact efficiency during initial development phases. This study proposes a planning framework that integrates morphology-adaptive zoning control, ecology-responsive compactness principles, and urban–rural integrated settlement patterns, providing quantitative tools for mountainous station development. These findings offer theoretical and practical support for achieving urban sustainability goals and meeting the 3D compactness and transit-oriented development requirements in territorial spatial planning. 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

This study proposes a novel effective improved whale optimization algorithm via angle penalized distance (IWOA-APD) for automatic train operation (ATO) to effectively improve the ATO quality. Specifically, aiming at the high-quality target speed curve of urban rail trains, a target speed curve multi-objective optimization model for ATO is established with energy saving, punctuality, accurate stopping, and comfort as the indexes; and the comprehensive evaluation strategy utilizing angle-penalized distance as the evaluation index is proposed to enhance the assessment’s rationality and applicability. On this basis, the IWOA-APD is proposed using strategies of non-linear decreasing convergence factor, solutions of out-of-bounds eliminating via combination of reflection and refraction, mechanisms of genetic evolution with variable probability, and elite maintenance based on fusion distance and crowding degree distance. In addition, the detailed design scheme of IWOA-APD is given. The test results show that the proposed IWOA-APD achieves significant performance improvements compared to traditional MOWOA. In the optimization scenario from Lvshun New Port Station to Tieshan Town Station of Dalian urban rail transit line No.12, the IGD value shows a remarkable 69.1% reduction, while energy consumption decreases by 12.5%. The system achieves a 64.6% improvement in punctuality and a 76.5% enhancement in parking accuracy. Additionally, comfort level improves by 15.9%. 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

The rapid expansion of urban rail networks has resulted in increasingly complex passenger flow patterns, presenting significant challenges for operational management, especially during incidents and emergencies. Disruptions such as power equipment failures, trackside faults, and train malfunctions can severely impact transit efficiency and reliability, leading to congestion and cascading network effects. Existing models for predicting passenger origin–destination (OD) matrices struggle to provide accurate and timely predictions under these disrupted conditions. This study proposes a deep counterfactual inference model that improves both the prediction accuracy and interpretability of OD matrices during incidents. The model uses a dual-channel framework based on multi-task learning, where the factual channel predicts OD matrices under normal conditions and the counterfactual channel estimates OD matrices during incidents, enabling the quantification of the spatiotemporal impacts of disruptions. Our approach which incorporates KL divergence-based propensity matching enhances prediction accuracy by 4.761% to 12.982% compared to baseline models, while also providing interpretable insights into disruption mechanisms. The model reveals that incident types vary in delay magnitude, with power equipment incidents causing the largest delays, and shows that incidents have time-lag effects on OD flows, with immediate impacts on origin stations and progressively delayed effects on destination and neighboring stations. This research offers practical tools for urban rail transit operators to estimate incident-affected passenger volumes and implement more efficient emergency response strategies, advancing emergency response capabilities in smart transit systems. Full article

Under the backdrop of urban renewal, harmonizing transit-oriented development (TOD) with urban renewal to maximize rail value has emerged as a critical focus in contemporary planning. Based on this, this paper proposes the node–place–revenue (NPR) model, which constructs evaluation indexes from the three dimensions of the node, place, and revenue. It determines the weights of each index by using expert scoring and the Analytic Hierarchy Process (AHP). Taking Shenyang Metro Line 1 as an example, the study first used the model to measure the node value, place value, and revenue value of each sample TOD station area. Secondly, K-means clustering analysis was used to form a spatial classification of five station areas. Finally, this paper proposes one differentiated urban renewal strategy for each type of station area. It is found that (1) the NPR model classifies stations into five categories: stress and high revenue, balanced, unbalanced node, unbalanced place, and dependence and low revenue and (2) the differentiated urban renewal strategies for each type of station area can be explored in terms of precise decongestion, node upgrading, function expansion, endogenous optimization, and infill quality improvement. This paper examines the economic driving effect of Shenyang Metro Line 1 stations on the renewal of the surrounding areas from the perspective of the economic balance of payments, providing a new reference for Shenyang-rail-transit-guided urban renewal work. 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

The construction of subways exacerbates the non-uniformity of surface deformation, which in turn poses a potential threat to the safe construction and stable operation of urban rail transit systems. Beijing, the city with the most extensive subway network in China, has long been affected by land subsidence. Utilizing data from Envisat ASAR, Radarsat-2, and Sentinel-1 satellites, this study employs PS-InSAR technology to monitor and analyze land subsidence within a 2 km buffer zone along Beijing Metro Line 22 over a span of 20 years (from January 2004 to November 2024). The results indicate that land subsidence at Guanzhuang Station and Yanjiao Station along Metro Line 22 is particularly pronounced, forming two distinct subsidence zones. After 2016, the overall rate of subsidence along the subway line began to stabilize, with noticeable ground rebound emerging around 2020. This study further reveals a strong correlation between land subsidence and confined groundwater levels, while geological structures and building construction also exert a significant influence on subsidence development. These findings provide a crucial scientific foundation for the formulation of effective prevention and mitigation strategies for land subsidence along urban rail transit lines. Full article

With the intensification of global climate change, the underground rail transit system of Guangzhou, as a major coastal city, faces severe flood risks. Through field investigations of 313 metro stations, this study identified 472 flood-related risk points, primarily involving water backflow at low-lying stations, insufficient elevation of structural components, and the threat of overbank flooding from adjacent rivers. By integrating GIS-based spatial analysis with the SCS-CN runoff model, an extreme rainfall scenario (534.98 mm) was simulated, revealing a maximum runoff depth of 484.23 mm. Based on these results, it is recommended to raise the flood protection design elevation to 582 mm and install additional waterproof barriers. Optimization strategies include establishing flood protection standards for new stations based on site topography and runoff volume, elevating station platforms or adding waterproof structures at existing stations, and upgrading drainage systems with real-time monitoring and early-warning mechanisms. This study emphasizes the necessity for Guangzhou’s metro system to integrate climate-adaptive urban planning and technological innovation to enhance flood resilience and promote sustainable urban development. Full article