To address the issues of high reliance on manual experience, poor dynamic adaptability of emergency decision-making, and low efficiency of decision generation in urban rail transit emergency response, a decision-making model for emergency response is proposed, which integrates a "predicate-centered event representation framework" and a "causal graph-retrieval augmented generation (RAG) integrated architecture". Firstly, a non-static trigger word is adopted to construct a process-oriented knowledge structure for emergency response that is superior to the static knowledge graph. Then, by combining information grouping, situation assessment, and an "event-situation" two-stage query rewriting strategy, the proposed model enhances traditional RAG with a stronger ability to understand the logic of event chains, thereby generating emergency response decisions that are more actionable and better adapted to specific scenarios. Finally, evaluation indicators such as the decision compliance rate, decision generation time, and decision efficiency improvement are defined and applied to comparative experiments to evaluate the model's performance. The research results show that compared with the traditional RAG method, the proposed model improves key-node accuracy by 29.84% in the decision compliance evaluation and achieves a logical-relation correctness rate of 84.74%. Meanwhile, compared with traditional manual decision-making, the proposed model improves generation efficiency by 57.14% indicating that the model can shorten the response time while meeting compliance requirements, providing a reference for constructing an intelligent dispatch decision generation system for urban rail transit.

With the rapid development of large AI models, the demand for their application in urban rail transit is increasing rapidly. However, there is still a lack of evaluation standards and methods for large models specific to the urban rail transit industry. To address this, this paper establishes an evaluation system for large models tailored to the urban rail transit industry. This evaluation system covers the construction of evaluation datasets, the design of evaluation dimensions, the selection of evaluation methods, and the formulation of evaluation metrics. Through this system, the performance of urban rail transit large models for urban rail transit in tasks such as language understanding, knowledge reasoning, image-text alignment, and spatiotemporal consistency can be evaluated comprehensively and objectively. Meanwhile, a dedicated evaluation system for large models for urban rail transit is designed and developed, and the effectiveness of the evaluation system is verified through practical application. The verification results show that the established evaluation system can provide an evaluation basis for the optimization and application of large models in urban rail transit, and promote their implementation and application in the urban rail transit industry.

To promote equity and expand the spatial and social reach of multimodal rail systems, this paper examines the intersection of multimodal transit and social equity. It analyzes key research approaches, critical challenges, and influencing factors, and proposes future research directions and policy strategies. Findings suggest that two fundamental issues underpin the inequity of multimodal rail systems, namely spatial disparities in network coverage and insufficient attention to the diverse needs of users. Rail station and line densities tend to be lower in low-density urban neighborhoods and peri-urban or rural areas, making access more difficult. Vulnerable groups, including the elderly, people with disabilities, low-income populations, and individuals with lower educational attainment, are particularly affected by economic constraints, digital exclusion, and time-space coordination challenges in their daily mobility. Future research on social equity in multimodal transit systems should adopt a holistic perspective that integrates built environment characteristics, transport infrastructure provision, temporal factors, individual attributes, economic policies, intermodal connectivity, and digital literacy. Advancing equity in this context requires innovation in methodological frameworks, model development, as well as the identification and quantification of influencing mechanisms. As a capital-intensive foundational infrastructure, urban rail transit plays a critical role in shaping inclusive and equitable transport systems. Addressing social equity in multimodal transit is vital for enhancing population mobility, increasing employment opportunities, and promoting balanced and sustainable regional development.

With the increasingly prominent role of bike-sharing in the "last mile" of urban transportation, the issue of service equity has attracted extensive attention. Based on tshared bike usage data and metro station passenger flow data in Shenzhen in August 2021, this research evaluates the equity characteristics of bike-sharing services in Shenzhen metro catchments within 15 min cycling catchments and analyzes their spatial patterns and temporal differences. By constructing two types of indicators, namely the Gini coefficient of per capita bicycle use, and the Gini coefficient of supply-demand matching, this study quantifies the imbalance in bike-sharing use in space and crowd. A higher Gini coefficient indicates a greater degree of inequity across spatial and demographic dimensions. The results show that there is a certain inequity in the use of shared bicycles in Shenzhen, with significant differences across spatial and temporal contexts. The distribution of bike-sharing use within metro catchments in central urban areas is relatively balanced, and the use of bicycles in the suburban new city is highly concentrated. Some metro lines (such as those connecting the central urban area and the outer new towns) have significantly lower levels of bike-sharing use equity than those in the central area. Bike-sharing usage is closer to the distribution of commuting demand during the morning and evening rush hours on weekdays, while there is a mismatch between supply and demand during weekends. Finally, based on the results, this paper proposes targeted recommendations for optimizing the layout and scheduling of bike-sharing services in metro catchments to improve the equity of bike-sharing feeder services for metro stations.

To address the limitations of existing research on land value premiums of urban rail transit, which are characterized by short time spans and insufficient exploration of the long-term dynamic impacts of economic cycles, this study, based on data from the Tokyo Metropolitan Area spanning 1984-2023, for the first time, constructs a long-time-series framework covering the bubble period, recession period, and recovery period. It employs the Geographical and Temporal Weighted Regression (GTWR) model to quantitatively capture the spatiotemporal heterogeneity of land value premiums. The results indicate that during the bubble period, the rail transit-induced land value premium exhibits a core-periphery gradient attenuation, with commercial land demonstrating the highest sensitivity; during the recession period, the premium effect reverses direction, leading to strip-shaped depreciation along rail transit corridors in urban areas while exurban land maintains stronger stability in its premium levels; during the recovery period, the overall premium elasticity weakens, yet peri-urban commercial land shows prominent growth potential, with elasticity approximately 1.5 times that of urban areas. The study reveals that there exists a dynamic interaction between macroeconomics and transit accessibility, necessitating the establishment of a cross-cycle response mechanism. This research uncovers the nonlinear dynamic interaction mechanism between macroeconomic cycles and rail transit accessibility, fills the research gap in the quantitative study of cross-cycle spatiotemporal heterogeneity of rail transit- induced land value premiums, provides data support for quantifying such premiums in metropolitan areas under economic fluctuations, and offers theoretical support for mitigating systemic risks and enhancing planning resilience.

Based on a case study of Beijing's Chaoyang District, this research employs a multiscale geographically weighted regression (MGWR) model to systematically examine the spatial heterogeneity of housing prices around subway stations and the mechanisms through which multiple factors affect them. Using second-hand housing listing data from Lianjia in 2023, the study incorporates both internal estate characteristics (e.g., average housing age, property fees, greening rate, and plot ratio) and external location-based attributes (e.g., proximity to subway stations, bus stops, commercial centers, schools, hospitals, and parks) to construct the MGWR model, which is compared against ordinary least squares (OLS) model and the geographically weighted regression (GWR) model. The results show that the MGWR model achieves the highest goodness-of-fit (R²=0.691) and effectively captures the scale-varying effects of different factors. Distance to subway stations exhibits a significant negative correlation with housing prices, with a stronger effect in peripheral areas than in the urban core. Proximity to top-tier schools shows a heterogeneous pattern, positive in clustered areas but negative in peripheral zones, reflecting a localized saturation effect of educational resources. The influences of commercial, park, and hospital accessibility also demonstrate notable spatial variations, highlighting the complex interplay and contextual dominance of transportation, education, commerce, ecology, and medical services in the capitalization of housing prices. This study provides a multiscale analytical framework for understanding the formation mechanisms of housing prices in the context of urban rail transit, offering valuable insights for spatial governance and public service allocation.

This paper focuses on the operational organization issues of metro logistics systems, drawing on domestic and international practices and taking literature from 2016 to 2025 in databases like Web of Science and CNKI as samples. By integrating bibliometric and content analysis methods, it examines research progress in this field. Subsequently, it provides an in-depth review of studies using quantitative analysis methods from perspectives such as research objects, stakeholders, transportation organization modes, train scheduling, capacity allocation, freight flow distribution, modeling strategies, and solution algorithms. The findings show that international practice and research started earlier but have not been continuously promoted, while domestic research, despite considerable attention, remains in a small-scale exploratory phase. Current research lacks systematic connections when analyzing key issues like transportation organization modes, train scheduling, capacity allocation, and freight flow distribution, highlighting the urgent need to construct an integrated optimization framework for multi-element collaboration. The research objects need to be further extended from single-line scenarios to complex networks to enhance the practicality and applicability of the theoretical findings. Under the green and low-carbon objective, incorporating energy consumption indicators into scheduling optimization and integrating low-carbon strategies such as regenerative braking energy utilization is of great significance. Meanwhile, moving beyond fixed demand assumptions and strengthening the ability to respond to uncertainty and elastic demand can improve the alignment between research and reality. Additionally, developing optimization algorithms suitable for large-scale networks remains a key direction for future research.

To address the significant limitations of using urban rail transit for freight transport and the lack of large-scale operation despite the gradual launch of pilot urban rail freight projects in some cities, this paper presents a systematic statistical analysis of the current status of urban rail freight in China based on a synthesis of existing practices and research. First, three typical operation modes are summarized: mixed passenger-freight transport, shared-line trailer operation, and shared-line separated operation. Second, using statistical data from 18 pilot cities, a quantitative analysis is conducted from three perspectives: spatial distribution, temporal evolution, and comparison of pilot projects in representative cities. Finally, the main challenges in the development of urban rail freight are examined, and corresponding development strategies are summarized. The results indicate that urban rail freight projects in China exhibit a spatial pattern radiating from the eastern coastal regions to hub cities in central and western China, with high concentrations in the Beijing-Tianjin-Hebei region and the Yangtze River Delta; urban rail freight is transitioning from the initial exploration stage to large-scale pilot implementation, with the number of pilot projects increasing year by year; most pilot cases adopt the mixed passenger-freight mode, a few use the shared trailer mode, and operations are mostly scheduled during off-peak passenger hours. Urban rail freight can effectively utilize idle capacity, alleviate urban logistics pressure, and promote green development, but it still faces challenges such as incompatibility of existing infrastructure, operational scheduling complexity, poor connectivity, inadequate safety control, data barriers, and profitability difficulties. The development trend of urban rail freight points toward the coordinated advancement of line retrofitting, market-oriented mechanisms, the construction of an integrated ground-air distribution system, and technological innovation.

To address the current issues such as lower-than-expected passenger intensity of medium-capacity rail transit and the fiscal pressure on local governments, this study, based on an analysis of the main underlying causes, proposes an optimized approach to determining the construction conditions of medium-capacity rail transit lines through an efficiency and benefit competitiveness analysis. First, it analyzes the situation confronting medium-capacity rail transit: urban population growths tends to stabilize, and rail transit is facing strong competition from private cars, electric bicycles, and other transportation modes. Second, it proposes an optimized approach to determining the construction conditions of medium-capacity rail transit lines and conducts an efficiency and benefit competitiveness analysis of medium-capacity rail transit in relation to other transportation modes. In terms of "door-to-door" time efficiency, the analysis aims to ensure that medium-capacity rail transit is comparably competitive with private cars, electric bicycles, and other modes of transportation; in terms of input-output economic benefits, it aims to ensure that medium-capacity rail transit is more competitive than metro systems. Finally, it quantitatively proposes the construction conditions for medium-capacity rail transit lines. The results suggest that the terminal stations of medium-capacity rail transit lines should be located within 30 km of the city center, the travel speed should exceed 35 km/h, the total access and egress time at both ends should be kept within 20 min, the initial passenger intensity should exceed 5,000 passengers/(km·d), and the current population and employment density within 1 km² along the lines should exceed 10,000 persons and jobs/km². These findings can provide technical reference for local governments to scientifically planning medium-capacity rail transit lines.

To address the issues of low efficiency, significant environmental disturbance, and difficulties in attitude control in shaft construction in urban areas with dense buildings and limited space, this study proposes an innovative vertical mechanized construction technology centered on a cutting-type shaft boring machine, prefabricated segment structures, and a sinking construction method for isolated-boulder-bearing strata and high-strength rock strata. The effectiveness of this technology has been verified through its practical application in the No. 2 shaft project on the Jingxi Road-Baiyun Dongping section of the Guangzhou-Huadu intercity railway. The engineering practice results indicate that the adoption of undrained excavation with cutting bits and prefabricated assembly technology inside the shaft has significantly reduced the construction difficulty of the retaining structure, minimized stratum disturbance, and improved construction efficiency. The advance shaft advance excavation mode can effectively reduce stratum settlement and shear stress. The alternate excavation mode is suitable for strata with good mechanical properties, while the synchronous excavation mode is applicable to general strata (including those containing boulders), which can effectively control stratum settlement and deformation. Through the optimized design of drum picks, the drum grille and the slurry circulation system, the modified shaft equipment demonstrates stronger adaptability under complex geological conditions such as boulder-bearing strata and high-strength rock formations. The research results can provide a reference for the optimization of shaft equipment and construction technology in similar soft-hard alternating granite composite strata.

With the rapid advancement of urban rail transit construction in China, the networked layout of lines has been continuously improved, and the demand for intelligent operation has been steadily rising. However, the shortcomings of the existing traditional signaling system in terms of resource utilization and scalability have become increasingly prominent, making it difficult to meet the operational requirements of modern urban rail transit, such as high density, high reliability, and efficient operation and maintenance. In response to this problem, the core purpose of this study is to clarify the development path of the next-generation signaling system empowered by cloud platforms, break through the application bottlenecks of the traditional signaling system, and provide theoretical support and practical reference for the upgrading and iteration of urban rail transit signaling systems. In the research process, the comparative analysis method was adopted to systematically sort out the architectural limitations and application pain points of the traditional signaling system. This study. This study focuses on analyzing the overall architectural composition of the cloud-based signaling system, and proposes targeted architectural design ideas and key technology application schemes for the cloud-based signaling system in in combination with the actual operational needs of urban rail transit. In addition, in-depth research is conducted cloud-based SIL 4 safety technology, safe real-time train-to-ground communication technology, and adaptation technology between traditional signaling service applications and secure cloud platforms. The research results indicate that the cloud-based signaling system can effectively make up for the deficiencies of the traditional signaling system, improve the operational efficiency and maintenance level of the signaling system, and provide strong support for the networked and intelligent development of urban rail transit. The findings provide a useful reference for the design, optimization, and engineering implementation of cloud-based signaling systems, and may support the transition of urban rail transit signaling systems toward cloud-based and intelligent architectures.

To address train rescheduling and speed management under section disruptions for urban rail transit, this study constructs a mixed integer nonlinear programming (MINLP) model with decision variables including section running time, section acceleration time, additional dwell time, and whether a train is affected by a disruption, aiming to minimize train traction energy consumption and terminal arrival delay times, providing a coordinated multi-objective optimization framework for restoring train operations in emergency scenarios, and thereby enabling rapid optimization of train rescheduling and energy-efficient speed profiles and ensuring the safe and stable operation of urban rail transit systems. Considering the quadratic non-convex constraints in the model, we reformulate the MINLP into a mixed integer quadratic programming (MIQP) model through Taylor approximation method, enabling effective solutions via commercial solvers. Utilizing operational data from Beijing Subway Yizhuang Line, numerical experiments validate the effectiveness of the MIQP in reducing train terminal delay time and traction energy consumption. This research provides a theoretical basis for rail transit operators to optimize train dispatching and operational control during disruptions. Keywords: urban rail transit; train rescheduling; speed management; energy-efficient train operation; taylor approximation

To address low passenger flow efficiency in mega-cities' urban rail transit networks, this study takes Wuhan as a case, develops a multi-level framework to explore flow formation mechanisms, and proposes refined enhancement and prediction strategies. By comparing passenger flow evolution across different stages of networked operation, the study reveals a pattern of diminishing marginal returns in passenger flow growth, analyzes spatiotemporal characteristics at multiple levels, and identifies formation rules via correlation analysis. Based on the findings, a tiered strategy of "tapping the potential of core areas, improving the quality of central areas, and cultivating passenger demand in peripheral areas" is proposed, along with an optimized forecasting system that integrates macro-level multi-scenario prediction, corridor identification, and full-cycle dynamic calibration. The proposed framework provides systematic technical support for alleviating insufficient passenger flow and resource mismatch, offering practical value for sustainable urban rail transit development.

With the rapid development of cities and continuous progress in rail transit construction, the alignment of newly-built rail transit projects has become increasingly complex. It is inevitable for transition curves at stations in central urban areas to intrude into the end section of effective platforms. Excessive intrusion of transition curves into the scope of effective platforms will directly lower the speed limit standards for curved track sections, restricting train speed increase and the optimization of the overall line travel time. Against this background, this paper thoroughly analyzes the key influencing factors of transition curve intrusion into the length of effective platforms, and determines the controllable and allowable intrusion length in combination with actual operation conditions. On this basis, this paper innovatively puts forward three optimization schemes, namely sectional arrangement of the superelevation gradient for transition curves, reduction of transition curve length, and reduction of curve radius, to increase curve superelevation and further raise the maximum speed limit of curved track sections. Through comprehensive comparison and selection analysis combined with practical engineering cases, the research draws the following conclusion: the scheme of reducing the curve radius is prioritized on the premise of meeting the requirements of relevant specifications; for sections constrained by engineering conditions, the schemes of sectional arrangement of the superelevation gradient for transition curves and reduction of transition curve length can be adopted as alternative options.

Targeting the problems of low efficiency, poor coverage, and insufficient accuracy in shield tunnel segment misalignment measurement, an automatic measurement algorithm integrating laser point cloud grayscale conversion with deep learning is proposed. This method can accurately measure segment misalignment, effectively reduce the risk of water leakage caused by misalignment, and maintain the service performance of tunnel structures. First, the collected three-dimensional laser point cloud is converted into grayscale images with enhanced edge features. Then, the lightweight EfficientNet network is utilized to efficiently identify the bounding boxes of inter-ring and intra-ring joints of segments. The bounding box coordinates are mapped to the three-dimensional point-cloud space to achieve precise segmentation and extraction of point clouds on both sides of segment joints. Finally, the average radial distances from the point clouds on both sides of joints to the center of the fitted ellipse of the tunnel segment structure are calculated respectively, with their difference representing the segment misalignment, enabling non-contact, high-precision measurement of shield tunnel segment misalignment. A tunnel structure inspection vehicle equipped with a three-dimensional laser scanner was developed, with the above algorithm integrated and supporting data acquisition and processing software developed. An engineering pilot application was conducted in a shield tunnel section of Hangzhou Metro Line 1, with laser scanning at a moving speed of 20 km/h. Comparative analysis between the inspection vehicle measurement results and manual measurement results shows that the two measurement results are close, with a maximum absolute deviation of 1.6 mm. This method can maintain high measurement accuracy even under high-speed movement conditions, meeting the industry demand for rapid, comprehensive detection of shield tunnel segment misalignment.

Large-diameter shield tunnels for metro systems exhibit greater mass compared to conventional shield tunnels, and their vibration source strength is generally considered lower. Consequently, they are regarded as a potentially effective measure for mitigating environmental vibration induced by metro operations. However, large-diameter shield tunnels have limited applications in existing metro lines, resulting in a lack of measured vibration source data. Moreover, their frequency-dependent source characteristics and influencing factors remain unclear, leaving vibration and noise reduction designs utilizing such tunnels inadequately supported. Field measurements were conducted on large-diameter and conventional shield tunnels along the same metro line to compare their vibration source strength and spectral characteristics. A "tunnel-soil" finite element model was established and validated using the measured data. Subsequently, the effects of geological conditions and lining thickness on the vibration source strength were investigated. The results indicate that, under identical train operating conditions, the source strength of large-diameter shield tunnels is at least 7 dB lower than that of conventional shield tunnels, confirming their efficacy in reducing metro-induced environmental vibration. The vibration source amplitude of large-diameter shield tunnels is lower than that of conventional shield tunnels within the low-frequency range (1-125 Hz) but higher at frequencies above 125 Hz. Stiffer surrounding soils lead to increased vibration source strength and predominant frequencies. Additionally, each 10 cm increase in tunnel lining thickness reduces vibration source strength by approximately 1.5 dB, and each 1 m increase in tunnel diameter reduces the vibration source strength by about 1.9 dB. These findings provide a theoretical basis for evaluating metro environmental vibrations and designing vibration and noise mitigation measures.

To investigate the vibration isolation and fatigue performance of steel spring isolators in floating slab systems for existing subway lines, this study evaluates their long-term service capability through field tests and fatigue tests, using the floating slab system of Chongqing Metro Line 1—which has been in operation for eight years—as a case study. First, train-induced vibration tests were conducted to measure and analyze the natural frequency and damping ratio of the current floating slab system. Next, vibration reduction performance tests were performed on both the floating slab track and conventional track to compare their vibration reduction effects. Finally, a steel spring isolator was removed for fatigue testing to investigate how the isolator stiffness, damping ratio, and height changed under fatigue loading. Results indicate that the natural frequency and damping ratio of the current floating slab system remain within reasonable ranges. Compared to conventional tracks, the floating slab track absorbs substantial vibration energy by increasing its own vertical acceleration, thereby reducing vibration interference from subway operations to the environment and meeting vibration reduction requirements. During 3 million fatigue cycles, changes in the isolator stiffness, damping ratio, and height remained below specification limits. No cracks were observed in the steel springs or sleeve welds, and no leakage occurred in the damping material. A comprehensive evaluation indicates that the mechanical and vibration damping performance of this floating slab system continues to meet design requirements, suggesting that the system can remain in service and has a long service life.

To evaluate the threat posed by groundwater level fluctuations to the structural safety of subway systems, a dynamic early warning system for anti-floating failure of subway structures was developed. Taking the Beijing subway as an example, its structural types and geological environment characteristics were systematically analyzed. Station structures were classified into four categories based on burial depth and geological conditions, and section tunnels were further subdivided into eight categories based on excavation methods. Representative standard cross-sections from each category were selected as the basis for numerical modeling. Utilizing the suction stress-based effective stress principle and the Mohr-Coulomb constitutive model, a hydro-mechanical coupling numerical model was established to systematically simulate the stress and deformation responses of subway structures under various hydrological conditions, and a numerical simulation database covering multiple working conditions was constructed. By quantitatively evaluating the stability of subway structures under different hydrological conditions, structural internal forces, displacement, bearing capacity, and maximum crack width were selected as four early warning indicators. The contribution of each indicator to anti-floating failure was analyzed. Combining expert scoring data, the analytic hierarchy process (AHP) was used to determine the indicator weights, thereby constructing a dynamic early warning system for anti-floating failure of subway structures. The results show that the established database can effectively reveal the mechanism of ground bearing capacity weakening caused by rising water levels. Moreover, the proposed early warning system achieves accurate stability assessments and risk warning, while implementing water discharge and pressure reduction as primary control measures significantly enhances its real-time performance and engineering applicability. This study provides a theoretical basis and technical support for the safe operation and maintenance of underground structures such as subways under changing hydrological conditions.