This study holds significant theoretical and practical importance for enhancing the safety management and operational efficiency of railway systems. Currently, there is a notable gap in standardized safety-level measurement methods across diverse railway operating entities. Conventional safety assessment approaches predominantly rely on qualitative analysis frameworks, which often fail to comprehensively address the multifaceted risk factors inherent in complex railway operating environments. To address these limitations, this study leverages historical operational data from participating railway companies to propose an advanced integrated quantitative methodology: the Global Safety Index (GSI)-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. This innovative approach quantifies railway safety conditions by systematically analyzing incident and accident data, integrating statistical modeling frameworks, data imputation algorithms, and comprehensive analytical protocols. The method enables detailed examination and interpretation of large-scale operational datasets within railway systems. The implementation of this quantitative framework has demonstrated substantial improvements in the accuracy of safety performance metrics. Furthermore, it offers robust technical support for developing risk mitigation strategies and optimizing safety performance in the railway sector. By employing systematic risk factor identification and data-driven safety quantification, this approach facilitates accident prevention, enhances risk early-warning systems, and provides evidence-based decision-making support. As research progresses and the accessibility of Chinese railway safety data increases, the analytical precision of this methodology can be further refined. Future applications may include in-depth analyses of Chinese railway risk event datasets, thereby offering strong technical support for the continuous elevation of safety standards in China’s railway operations.

For the safe functioning of rail transit systems, effective management of passenger flow is crucial. Nevertheless, existing formulation models are inadequate for simulating the entire passenger travel process due to their reliance on simple factors. Meanwhile, the strategy for controlling passenger influx generally concentrates solely on the entrance gate, which restricts its impact. To guarantee the safety and dependability of station operations, this research proposes a method for formulating and evaluating passenger influx control schemes for rail transit stations based on interactive simulations involving trains, passengers, and stations. Firstly, based on converted line-level passenger flow data, simulation intelligent agents for trains, passengers, and stations are constructed, and constraints of train capacity, station capacity, train interval, and arrival/departure time are presented. Then, the behavior and interactions of intelligent agents are described in detail, considering the diverse types of passenger flow. As a result, the micro travel simulation of passengers and passenger flow change display within the rail transit system are achieved. Next, a rolling adjustment method based on simulation results (RAM-BSR) is suggested to formulate and evaluate the passenger influx control schemes. Finally, the train delay of Shanghai Metro Line 13 on a certain workday is taken as a case study, where various passenger influx control schemes are comprehensively evaluated, validating the availability and reliability of the suggested simulation and formulation approach. The research results can well recreate the passengers’ travel process, formulate passenger influx control schemes, and provide rolling evaluation for the developed schemes.

Kobe's downtown area is currently planned for redevelopment projects that include the introduction of Light Rail Transit (LRT) and the construction of new facilities. This study advances the pedestrian model by developing a system that allows agents to choose between walking and LRT as the modes of transportation. We employ an MAS to analyze the effects of LRT implementation and redevelopment on pedestrian circulation, measured by five key indicators: average walking time, average walking distance, number of destinations visited, LRT travel time, and expenditure. Additionally, the impact on the local economy is assessed through the total visitor count and expenditure. The simulation results reveal that the introduction of LRT activates circulation behaviors, whereas the effects of redevelopment on circulation behaviors are limited. However, the results of the simultaneous introduction of LRT and redevelopment indicate increases in the five key indicators and total number of visitors, compared with the current state. Therefore, combining LRT and redevelopment can lead to increased pedestrian circulation and the revitalization of the local economy in the long term. The future challenge is to analyze the impacts of different routes, station locations, fares, service frequencies, and the characteristics of pedestrians on their movement patterns, in order to propose more effective planning strategies.

Shallow underground excavation techniques are widely employed in urban subway construction, and the pile–beam–arch (PBA) method is especially prevalent in subway stations’ construction. This method is primarily designed to address the challenges posed by complex geological conditions, dense underground utilities and the proximity of existing structures encountered during the comprehensive development process of urban subterranean environments. This research focuses on the Workers’ Stadium Station of Beijing Metro Line 17, utilizing MIDAS GTS NX computational simulation software to simulate the stratigraphic structure and construction processes associated with the station. A comparative analysis is conducted between the numerical simulation results and the corresponding field-measured data. By fitting the numerical simulation results and field-measured data with a Gaussian function, the coefficient of determination (R²) is determined to be 0.9723. This indicates an excellent agreement between the axial forces sustained by the CFST column in the model and the field-measured data across various excavation stages of the PBA method. This suggests that the numerical modeling effectively reflects the impact of actual construction activities on the CFST columns. Additionally, building upon this model and integrating principles from elastic mechanics theory, the paper investigates the impact of rising groundwater levels on the central column of the station during its operational phase. The analysis reveals that as the groundwater levels rise, both the central column’s axial force and axial displacement exhibit a gradual upward trend, with the rate of increase initially rising before subsequently declining. Notably, when the groundwater level reaches the top slab of the station, both parameters attain their maximum values. This research contributes to understanding the implications of groundwater level fluctuations on the stability of subway stations and offers recommendations for the ongoing operation of such facilities.

In urban rail flexible traction power supply system (FTPSS), conventional energy-saving strategies for reversible converter (RC) predominantly rely on offline optimization with fixed parameters. However, inherent uncertainties in train operations, such as timetable deviations and stochastic load fluctuations, result in energy consumption volatility, rendering traditional approaches suboptimal. To address this, we propose a multi-timescale model predictive control (MPC) framework that integrates day-ahead scheduling and intraday rolling optimization. Second, we propose a novel data processing method for neural network training in the intraday to construct a neural network-based load prediction model, which is used as the model prediction control (MPC) input for rolling optimization. Validated on Qingdao Metro Line 11 datasets, the prediction model achieves a correlation coefficient (R²) value of 95.2%, and the mean squared error (MSE) is 0.078, outperforming conventional prediction methods. By integrating MPC-based rolling optimization with day-ahead scheduling, the proposed strategy improves the energy-saving rate by 2.00% over traditional offline optimization methods. Demonstrating robustness against timetable perturbations and load uncertainties.

The unique physical characteristics and travel preferences of disabled individuals may lead to specific spatiotemporal characteristics of subway travel. This difference may also be temporally heterogeneous on holidays versus every day, and its association with the built environment may be significantly different. However, few studies have investigated such differences in depth. This study explores the differences in the spatiotemporal characteristics of subway travel for people with disabilities using subway swipe data from Wuhan, China, on the May 1 holiday and every day, and analyzes the effects of the built environment on holiday and everyday travel for people with disabilities compared to non-disabled people using XGBoost and SHAP models. The results show that compared with non-disabled people, the spatial and temporal changes of passenger flow of disabled people are less affected by holidays, and the spatial distribution of passenger flow and traveling time is reduced and contracted, respectively, based on weekdays. Second, the relative importance of each element of the built environment on metro passenger flows for the disabled and the non-disabled differed significantly, with medical facilities and network density being the most important variables for the disabled, both on holidays and weekdays. Comparatively, non-disabled people are more affected by holidays, with the highest contribution to disabled metro patronage on weekdays being made by food and beverage facilities, which changes to attractions on holidays. In addition, all built environment elements show nonlinear and significant threshold effects on disabled and non-disabled metro ridership. Finally, the built environment has an interaction effect on disabled subway passenger flow, for example, the closer to the sub-center, the higher the number of firms, the more inhibitory effect on disabled weekday and holiday subway passenger flow. The results of the study will contribute to an in-depth understanding of the spatial and temporal characteristics of metro travel for among disabled individuals, thereby developing disability-friendly measures for rail transit systems.

Under the broad consensus on reinforcing flood resilience in underground spaces, the hydraulic properties of metro tunnels have not been thoroughly examined. V-slope configurations are widely adopted as a standard feature in metro tunnel systems. This study aims to enhance the understanding of water propagation mechanisms in such tunnels to optimize the response of metro systems to upcoming floods. Through a combination of scaled physical model experiments and VOF numerical simulations, the research reveals key stages and patterns of water accumulation in V-shaped slope tunnels. The flood propagation process is divided into four stages: downhill flow on a single slope, uphill flow undergoing deceleration and accumulation, emergence of hydraulic jump, and wave reflection and oscillation. By investigating hydraulic jump characteristics and the evolution of submersion under varying conditions, the research highlights the local flow field discontinuity and identifies the incompatibility of existing hydraulic models with metro tunnel flooding prediction. It emphasizes the importance of considering detailed flood front movements and the surge of water depth for early flood warning in metro tunnels. The findings enhance predictive accuracy for inundation timing and dynamic flood progression.

In the context of transit-oriented development (TOD), the comprehensive commercial development around metro stations has become a new trend. However, increased commercial attractions can alter passenger behavior, intensifying space congestion and flow conflicts within metro stations. Consequently, commercial service areas can become critical risk zones. This study explored a recognition and evaluation method for consumption behavior based on passenger trajectory data from TOD metro stations. First, the characteristics of both transfer and consumption behaviors are detailed, distinguishing between strong- and weak-purpose consumption. A dynamic model of the “transfer–consumption–transfer” behavior process is also developed. Second, a machine learning-based method for recognizing and evaluating consumption behavior, grounded in passenger trajectory analysis, is proposed. This method employs machine learning method to detect and track passenger movements. Simultaneously, a coordinate transformation model is constructed to correct data deviations from the pixel-to-world coordinate system. Several analytical indicators, including deviation angles, distances, and conflict points, are introduced to mathematically describe the consumption behavior characteristics at TOD metro stations. Finally, the proposed machine learning-based method is applied to a comprehensive metro station in Shanghai, China. The experimental results validate the effectiveness of the proposed method.