With reference to internationally recognized statistical standards, this study classifies urban rail transit into three major modes—metro, light rail, and tram. In addition, commuter rail is incorporated to enable a broader-scale assessment of the global operational landscape of rail-based urban passenger transport and to inform China's rail transit development. By the end of 2025, 562 cities across 79 countries and regions worldwide had urban rail transit in operation, with a total operating length of 46,528.95 km. Metros, light rail, and trams accounted for 22,789.91 km (48.98%), 6,469.95 km (13.91%), and 17,269.09 km (37.11%), respectively. Over the same period, 65 cities in China (including Hong Kong, Macao, and Taiwan) operated urban rail systems totaling 13,796.06 km, of which the Chinese mainland accounted for 13,071.58 km and Hong Kong, Macao and Taiwan for 724.48 km. For metro ridership, data from 190 cities in 61 countries indicate that metro systems carried 76.051 billion passenger trips in 2024, with an average load intensity of 0.99×10⁴ passenger trips per (day·km). In China (including Hong Kong, Macao, and Taiwan), metro ridership reached 33.541 billion passenger trips in 2024, representing 44.10% of the global total. Overall, China's urban rail transit has continued to expand steadily and ranks first worldwide in both network length and passenger volume. With respect to commuter rail, 241 cities in 54 countries and regions had commuter rail services in operation by the end of 2025, with a total operating length of 70,001.16 km. China (including Hong Kong, Macao and Taiwan) had commuter rail services in 31 cities, totaling 4,345.73 km and accounting for 6.21% of the global total. These figures suggest that, as China enters the 15th Five-Year Plan period, there remains considerable room for enhancement in both the overall scale and network structure of commuter rail development.

The year 2025 marks the concluding year of the 14th Five-Year Plan period. By the end of this period, 58 cities across 28 provincial-level administrative regions in Chinese mainland have opened urban rail transit systems, with 382 operating lines totaling 13,071.58 km in length, of which subway lines account for 10,007.06 km, representing 76.56% of the total. By the statistical period, 1,976.41 kilometers of fully automated operating lines had been opened, accounting for 15.12% of the total operating length. In 2025 alone, 55 new urban rail transit operating lines (segments) were added, with a total new operational length of 910.80 kilometers. This includes 700.97 kilometers of new subway lines and 491.99 kilometers of new fully automated operating lines. It is projected that approximately 4,000 kilometers of new urban rail transit operating lines will be added during the 15th Five-Year Plan period, bringing the total operating length in Chinese mainland to around 17,000 kilometers by the end of this period.

Most existing studies have overlooked the impact of rail transit integration at the metropolitan scale on accessibility to tertiary hospitals and its spatial equity. To address this gap, this study uses the two-step floating catchment area (2SFCA) model and the adjusted mean absolute deviation to measure accessibility to tertiary hospitals and its spatial equity, sets two scenarios (the status quo and the rail transit integration scenario), and evaluates the impact of rail transit integration on accessibility to tertiary hospitals and its spatial equity in the Chengdu Metropolitan Area. The results show that rail transit integration has substantially improved accessibility to tertiary hospitals. As rail transit integration progresses, spatial polarization in accessibility has gradually weakened, and the number of high-accessibility areas has declined. However, the accessibility gap between the eastern and western sides of the Longquan Mountains has become increasingly pronounced. Rail transit integration has reduced spatial disparities in accessibility to tertiary hospitals and fostered spatial equity. These findings provide valuable insights for the rational allocation of high-quality medical and rail transit resources.

Addressing the lack of systematic review in existing literature on the theoretical aspects of four-network integration, particularly its connotation of four-network integration, this study first proposes that the connotation of four-network integration mainly includes multi-level rail transit classification and multi-level rail transit integration, which complement each other. The deep requirement of multi-level rail transit classification is to ensure efficient coordination of the "four networks," with a hierarchical structure that facilitates the balanced development of passenger flow. The focus of multi-level rail transit integration development can be subdivided according to the implementation path into planning and construction integration, operational service integration, and management mechanism integration, ultimately achieving temporal, spatial, and functional integration. Secondly, based on the connotation, this paper analyzes two domestic cases: the cross-platform interchange at Chengdu's Xipu Station and the cross-line operation between Chongqing Rail Transit Jiangtiao Line and Metro Line 5. Thirdly, it examines the challenges currently faced in promoting four-network integration in China, specifically including unreasonable network structure allocation, lagging planning and construction coordination, insufficient coordinated service levels, and deficient coordinated institutional mechanisms. Finally, targeted strategic recommendations are proposed in four aspects: rail transit service demarcation for the "four networks," planning and construction integration, operational service integration, and management mechanism integration, aiming to provide theoretical support and practical guidance for the sustainable and healthy development of four-network integration in China's rail transit.

In view of the difficulties in emergency evacuation caused by the increasing complexity of underground spaces in subway stations, and based on an extensive review of domestic and international literature, this paper first analyzes the evacuation characteristics and common features of different types of underground spaces, It examines evacuation scenarios, pedestrian behavior, and evacuation analysis methods in disaster contexts, and summarizes the evacuation behavior traits across station types. Secondly, the study delves into recent advancements in evacuation experiments and simulation technologies, covering physical and simulated experiments, including core algorithms, simulation processes, and inherent limitations of mainstream simulation platforms. It also highlights the impact of spatial features on simulation experiments and identifies current research hotspots. Finally, focusing on layout and path optimization for evacuation, the paper explores related methodologies and practical applications. On this basis, it points out that existing research is predominantly centered on theoretical modeling and quantitative analysis, with a lack of field validation that limits practical effectiveness. There is also a deficiency in interdisciplinary integration, especially concerning psychological and social dynamics of crowd behavior, which constrains the effectiveness of evacuation strategies in emergency practice. Therefore, the study suggests enhancing model realism through virtual reality, big data, and artificial intelligence technologies to improve simulation accuracy; and encourages interdisciplinary collaboration among psychology, sociology, urban planning, and safety engineering to comprehensively analyze crowd evacuation behaviors in different types of underground spaces in subway stations during emergencies, thereby providing a solid theoretical foundation and practical reference for future research.

To overcome the limitations of the standalone architecture of traditional smart stations and promote the transformation of urban rail transit toward networked and intensive operation, this paper proposes and verifies a cloud-based deployment paradigm for intelligent switching stations utilizing a line-level cloud platform. First, guided by the Development outline for smart urban rail transit in China, a hierarchical and decoupled construction framework for intelligent switching stations is established, focusing on the flexibility and scalability of the system architecture. Taking Changsha Metro Line 6 as a case study, this paper elaborates on the implementation of cross-service collaboration and data model sharing among stations by leveraging the line-level urban rail transit cloud platform, including the design of data transmission protocols, the construction of platform architecture, and the optimization scheme of information flow. The deployment of the cloud platform has successfully connected the service data links of all stations along the line, enabling centralized monitoring and intelligent dispatching. Practical results show that this paradigm significantly improves the utilization efficiency of hardware resources, reduces operational costs and labor input, and optimizes the business processes of switching stations, cutting the operation time by approximately 80%. Meanwhile, the system effectively reduces the staffing requirement by 3-4 members. This study provides a replicable and scalable cloud migration path for the digital transformation of the rail transit industry. The infrastructure based on the line-level cloud platform can support the core capabilities of intelligent switching stations, realizing an integrated closed loop from "platform construction" to "operational deployment".

Addressing the challenges of surging urban logistics demand, low efficiency of road freight, and environmental concerns, this study analyzes typical cases and summarizes three urban rail freight modes: incidental transport, mixed passenger-freight operations, and dedicated freight trains. It identifies key challenges in operational coordination, facility compatibility, demand matching, and safety management. To resolve these, we propose a "peak-valley" spatiotemporal resource allocation mechanism, flexible facility retrofitting, intelligent scheduling with standardized loading units, and an enhanced safety management system. Furthermore, three tailored operational schemes are designed: an off-peak dynamic incidental transport mode for small parcels, a flexible mixed mode for hub-to-hub batch transport, and a nighttime dedicated freight corridor mode for bulk cargo. This research offers theoretical and practical insights for improving logistics efficiency, reducing traffic congestion, and supporting carbon reduction goals.

To address the issue of urban rail transit network operations being disconnected from local urban development (the "two-skin" problem), this study analyzes the collaborative development characteristics of the "node-place" model in Beijing's rail transit stations. The goal is to provide a decision-making basis for station- city integration. Based on the node-place model theoretical framework, this study adopts multi-source big data analysis methods to construct an evaluation system that incorporates transportation functions (node value) and urban functions (place value). This study takes Beijing's rail transit stations at the end of 2023 as the research objects, divides them into four types of development through spatial clustering, and compares them longitudinally with 2019 data to form five types of evolution trend patterns. The results indicate that the overall development characteristics of the city's stations show a balance between node and place values, with node values slightly higher than place values. Therefore, station-city integrated development should be strengthened at the city level. The 29 stations located on the suburban lines show a continuous trend of deteriorating place value, which is closely related to the lagging land development around the stations. Therefore, it is necessary to strengthen guidance and promote land development around such stations. The research results will help form an improvement project library for different site development issues, providing support for achieving station-city integration. Subsequent research will expand the dimensions of dynamic monitoring and deepen the analysis of the interaction mechanism between rail transit and urban space.

In response to the growing complexity of passenger flow organization and the unclear decision-making mechanism of passengers' path selection behavior during the peak period of urban rail transit stations, this paper collected data through RP (revealed preference) and SP (stated preference) surveys. It constructed an ordered Logit regression model to analyze factors influencing passengers' trust in guidance information, and identified key influencing factors of passengers' path selection behavior based on the mixed Logit regression model. The results show that passengers' gender, age, and preference for guidance information have significant effects on their trust; the recommendation strength of guidance information and the herd effect of group choice behavior have positive effects on passengers' path decision-making. This study provides a new perspective for understanding the information processing mechanism and behavioral decision-making mode of passengers in complex transit environment. The findings provide a theoretical basis for optimizing the efficiency of passenger flow organization in rail transit transfer stations.

Predicting and controlling the surface settlement induced by shield tunneling is a critical engineering issue in underground space development. With the development of artificial intelligence technology, machine learning algorithms have demonstrated significant effectiveness in surface settlement prediction and control. We summarize the theory of machine learning, focusing on four key aspects: database construction, data preprocessing, model selection and evaluation, and shield tunneling parameter optimization under settlement control. Additionally, it critically examines the limitations of existing research in this domain. Current research often neglects the additional impacts of surface structure loads, traffic flow, and pedestrian activities, lacks model interpretability, and struggles to balance prediction accuracy with practical applicability. Future research should focus on developing a comprehensive measurement and evaluation system that incorporates the dynamic effects of surface buildings, traffic, and ground surcharge into settlement prediction models. Furthermore, future work should enhance model interpretability and promote the dynamic updating of machine learning models with real-time monitoring data, thereby providing more scientifically sound guidance for safety control in shield tunneling construction.

To address the lack of a well-established design framework for reducing groundwater level rise caused by metro station construction through diversion measures, this study combines theoretical analysis and physical model tests to investigate groundwater flow rate calculation in karst areas, determination of diversion design parameters, and the relationships of groundwater rise with hydraulic gradient and with diversion area ratio. A site case study is incorporated, and numerical simulation was used to verify the effectiveness of the diversion design scheme. Results show that: after metro station construction, the groundwater flow path lengthens due to station obstruction, causing a rise in groundwater; after implementing diversion measures, the flow path shortens and the rise dissipates; the groundwater rise exhibits a linear correlation with hydraulic gradient, increasing correspondingly with higher hydraulic gradients; under constant permeability coefficient and hydraulic gradient conditions, groundwater rise decreases exponentially with the proportion of diversion channel area, with a critical threshold occurring at approximately 20% proportion, leading to the recommendation that the optimization coefficient for diversion design parameters should be no less than 0.2; both gravel replacement diversion schemes and culvert diversion schemes can essentially restore the elevated hydraulic head to its original state. Numerical simulation results verify the correctness of diversion design parameter values, confirming that the parameter determination method is feasible and meets the requirements for reducing groundwater level rise.

To investigate the structural damage characteristics and deformation mechanisms of in-service metro tunnels in collapsible loess regions, this study conducted long-term structural deformation monitoring on a metro tunnel in Xi'an, China. Safety assessments of tunnel sections were performed based on monitoring data, with numerical simulations further revealing the causes of deformation in high-risk zones. The monitoring data show that the study line tunnels present a composite deformation pattern of arch subsidence and structural expansion dominated by arch subsidence. The Z5–Z6 interval was evaluated as a high-risk section due to the seriousness of the structural distress. Numerical simulations demonstrate distinct deformation mechanisms: saturation of the soil above the tunnel crown predominantly induces horizontal ovalization deformation ("horizontal egg-shaped" distortion), while saturation below the invert mainly triggers overall subsidence with minimal impact on structural dilation or surrounding rock pressure. By combining the geological investigation and deformation monitoring data, the monitored structural-expansion values match well with the predicted values for a 4-8 m flooding depth in simulation condition 3 (groundwater level rise), confirming that the compressive deformation triggered by the flooding of old loess beneath the invert is the main cause of structural deformation in this section. The findings provide valuable insights for the construction and operation and maintenance of urban rail transit systems in similar collapsible loess regions.

In light of the rapid expansion of underground rail transit systems and the growing requirements for tunnel operation and maintenance, the longitudinal structural resilience of shield tunnels has emerged as a critical research topic in the field of engineering. In this paper, the longitudinal differential settlement is employed as a performance index, and the ratio of residual performance to normal performance of differential settlements is defined as the resilience metric Re, thus a new model using longitudinal relative differential settlement as the index of tunnel structural resilience performance is proposed. Based on a case study in which a newly constructed shield tunnel of Shanghai suburban railway airport link line undercrosses the existing Metro Line 15 tunnel, the variations in tunnel deformation with respect to tunneling distance and construction schedule are analyzed. Then, the degradation characteristics of structural performance during both the disturbance stage and the recovery stage are effectively captured. Finally, the proposed model is applied to evaluate the structural resilience of existing metro tunnels. The results show that 32.3% of the performance loss is attributed to the new tunnel undercrossing during the first 38 days. After the completion of the grouting reinforcement, the tunnel performance is improved from 0.677 to 0.868; this improvement accounts for approximately 59.1% of the performance loss during the crossing period. Eventually, the resilience metric Re is calculated as 0.764, indicating a high level of resilience for the existing metro tunnel in this case. Moreover, other performance indices related to the longitudinal settlement of the tunnel are compared to validate the rationality and applicability of the proposed performance index.

With the increase of service life of urban rail transit, the stability of subway tunnel lining is getting worse and worse. Aiming at the problem of difficulty in detecting voids behind lining, impact echo method has been developed as a new means to detect such diseases. Among them, effectively extracting echo oscillation characteristics from complex signals of detection data has become the key to detecting voids behind lining. Based on the voids detection experiment behind "segment-surrounding rock" full-scale model, this paper proposes a denoising method based on Butterworth bandpass filter signal coarse screening strategy combined with local noise smoothing based on Daubechies8 wavelet transform. This method uses 4th-order bandpass filter and sets high and low frequency cutoff frequencies of 1 kHz and 50 kHz to effectively suppress signals and noise outside the frequency band; wavelet transform based on improved adaptive threshold method smoothes signal spikes and finely removes residual noise within the frequency band. The results show that after denoising the two-dimensional time-frequency diagram of experimental data by this method, high-frequency spikes are suppressed in the frequency domain, low-frequency discrete noise is removed, and echo signals in the passband are smoothed; the distribution characteristics of echo signals are retained in the time domain to reduce noise interference. The joint denoising method proposed in this paper significantly improves the extraction effect of the echo signal oscillation characteristics, and provides a preliminary research basis for the impact echo method to detect voids behind the lining of subway tunnels.

In order to study the influence of vehicle conditions on wheel-rail system vibration in surburban railways, a vertically coupled dynamic vehicle-track-tunnel model is established, and the influence mechanism of operating speed, train passenger load, wheelset mass and wheel polygonalization on the vibration response of vehicle, rail and tunnel is analyzed in detail. The research results show that with the train speed increasing from 100 km/h to 160 km/h, tunnel vibration source strength increases from 68.05 dB to 72.06 dB. With the increase of train passenger load, the vertical ride comfort index of vehicles gradually decreases, while the vertical wheel-rail force, rail vertical displacement and acceleration, maximum Z vibration level of the rail, and tunnel vertical displacement gradually increase, while the tunnel vertical acceleration and maximum Z-vibration level remain basically unchanged. Passenger load mainly affects the tunnel vibration response below 10 Hz. Above 10 Hz, the response is governed primarily by wheel-rail irregularities, and the tunnel acceleration level shows no significant differences among no-load, full-load, and overload conditions. Increasing wheelset mass increases the maximum Z vibration level of the rail and tunnel. During routine vehicle maintenance, the excitation frequency of wheel polygonalization should be prevented from coinciding with the wheel-rail system P2 resonance frequency.

To address bottlenecks and challenges caused by scarce fault samples, unclear failure modes, and insufficient methods for analyzing the reliability performance of regenerative braking energy feedback devices in urban rail transit, this study analyzes equipment reliability by collecting and examining fault event data for these devices. The paper establishes a fault tree model for the regenerative braking energy feedback devices and explores Gaussian kernel density estimation (KDE) to obtain a fitted failure probability distribution function, thereby constructing a reliability modeling and analysis method for such devices. This approach transforms discrete monthly mean time between failures (MTBF) data into a smooth probability density function curve, effectively narrowing the fluctuation range of reliability estimates. Furthermore, it calculates the core MTBF value range with a 95% confidence interval, significantly enhancing both the intuitiveness and scientific understanding of fault data and its distribution for such equipment. This methodology assists in precisely determining the optimal timing for scheduled maintenance, enabling dynamic maintenance adjustments, and supports future equipment selection and lifecycle management decisions.

To address the core problems of difficult evaluation of photovoltaic (PV) absorption and the poor alignment between energy storage (ES) configuration and life-cycle benefit maximization after integrating PV-storage hybrid systems into urban rail transit, this study first simulates the dynamic fluctuation characteristics of traction loads and the hourly PV power output characteristics throughout the year. On this basis, a multi-dimensional mathematical model incorporating PV characteristics, ES operation strategies, and life-cycle benefits is constructed. The model is then used to systematically analyze the annual PV absorption level, optimal ES capacity configuration, and ES charging and discharging strategy design, and proposes an optimal configuration scheme for urban rail transit PV-storage hybrid systems that accounts for time-of-use (TOU) electricity prices. The results indicate the following: Accurate assessment of PV absorption requires analyzing annual PV generation characteristics and load variation patterns; ES capacity selection should integrate PV absorption rate, investment payback period, and annual average benefits to minimize the static investment payback period while improving PV absorption, thereby achieving full-life-cycle profitability; the design of ES charging and discharging strategies needs to fully consider annual TOU electricity price differences, maximizing the use of peak-to-off-peak price spreads to enhance benefits. This research method provides a reference for the scientific planning and implementation of PV-ES integrated projects in urban rail transit.

To address the issues of poor real-time performance and low accuracy in existing subway protection area supervision models, an intelligent inspection system was developed that integrates video surveillance with image recognition and edge computing technologies. Firstly, an edge computing system for protection area inspection was constructed using edge computing terminals and surveillance cameras, effectively reducing reliance on cloud servers. Secondly, the YOLOv8l algorithm was selected as the image recognition model for the intelligent inspection system and deployed on the edge computing terminals. Finally, a B/S architecture -based inspection cloud platform was developed for managing inspection equipment, emergencies, and operations. Through training and testing with approximately 100,000 images of 6 categories of mechanical risk sources under various scenarios, the model achieves a consistent recognition rate exceeding 90% in both daytime and nighttime conditions. Field tests conducted along Guangzhou Metro Line 5 demonstrate an overall system recognition rate of 85.42%, indicating the substantial potential of this video-based intelligent inspection system. This advancement holds significant practical implications for ensuring the sustainable development of urban rail transit systems.

Focusing on the flood prevention safety assessment for rail transit operating lines during their operational period, this study takes the Nanjing rail transit operating lines as an example to establish a key technical system for flood prevention safety assessment during the operational period of these lines. This system encompasses five types of sitess along the operating lines, including subway stations, vehicle depots, substations, section ventilation shafts, and tunnel portals for vehicle access. The key technical system primarily involves dividing flood prevention zones, analyzing hazard factors, establishing a risk assessment matrix, and proposing flood prevention risk control measures. Based on this system, the Nanjing rail transit operating lines are divided into 34 level-I zones and 47 level-II zones. The hazard factors faced by the rail transit operating lines are categorized into six aspects: external floods, waterlogging, flash floods, local drainage, entrances and exits, and drainage/blocking facilities. Results show that there are 21 critical risk points, 112 significant risk points, 201 moderate risk points, and 61 minor risk points along the lines. The critical risk points are primarily concentrated in Lines 1, 2, 3, and 10. Corresponding risk control measures are proposed for different risk levels and types of points. The research findings provide theoretical guidance for ensuring flood prevention safety in urban rail transit operating lines.

Bolts on rail transit evacuation platforms are often concealed, making comprehensive manual inspection difficult and increasing the risk of loosening or detachment that could compromise operational safety. This paper proposes an intelligent inspection system integrating machine vision and laser scanning to achieve precise detection of bolt defects and clearance-intrusion trends. The system employs a dual-track autonomous robot equipped with a high-resolution camera and LiDAR, enabling comprehensive inspection through image recognition and point-cloud processing. First, high-resolution image acquisition combined with Adaptive Contrast Enhancement (ACE) is used to improve image quality. Optimized YOLOv11 and Mask R-CNN models are then applied for small-object detection and segmentation, enabling automatic identification of missing bolts, loose nuts, and insufficient bolt protrusion, with an accuracy of 94.4%. Furthermore, laser point-cloud modeling combined with a vector cross-product algorithm is employed to provide clearance-intrusion warnings with a precision of 2 cm. Field tests conducted on the Hangzhou-Haining Intercity Railway, across both elevated and tunnel sections, demonstrate an F1-score of 92.6% for bolt defect recognition and a 0% false alarm rate for clearance detection. The proposed system significantly enhances inspection efficiency and provides technical support for intelligent maintenance of rail transit infrastructure.