Search Results (182)

To explore how the opening and closing of subway platform screen doors (PSD) affect particulate matter concentrations (PM₁₀, PM_2.5, and PM_1.0) across different times (morning peak, off-peak, evening peak), and three key locations (subway tunnels, platforms, and waiting areas), we studied the Xi’an subway using a systematic monitoring approach, and a total of 6 monitoring points were monitored at 3 locations for 60 consecutive days of testing. The sampling time for each measurement point was 20 min, and a total of three groups were tested. The relationship between the opening/closing status of PSD and changes in particulate matter concentrations was then analyzed using statistical methods. The results showed that the particulate matter concentrations followed a sequential pattern: tunnel concentrations were higher than those in the waiting area, which in turn were higher than those at the platform center. PM₁₀ concentrations exceeded China’s standards for indoor air quality (GB/T 18883-2022) at all three locations. For PM_2.5, concentrations in the tunnel and waiting area exceeded the standard, while those at the platform center remained within the limit. Particles smaller than 1.0 μm constituted the dominant fraction of particulate matter in the tunnel, waiting area, and platform center. After the PSD opened, the peak average concentrations of PM₁₀, PM_2.5, and PM_1.0 in the waiting area increased by 70.53%, 55.81%, and 42.41%, respectively, compared to the average concentrations before the train entered the station. PSD had a significant impact on fine particulate matter concentrations on the platform during the evening peak: PM₁₀ concentrations in the front and rear of the waiting area were 37.85% and 57.61% higher than those in the middle, while PM_2.5 concentrations in these two areas were 39.81% and 50.23% higher than in the middle. No obvious distribution pattern was observed for PM_1.0. These results provide reference data for optimizing indoor air quality in the Xi’an subway and regulating the operation of platform screen doors. Full article

Accurate prediction of subway-induced environmental vibrations for unopened lines remains a significant challenge due to the difficulty in determining appropriate excitation inputs. To address this issue, this study proposes an excitation modification method based on field measurements and numerical simulations. First, field measurements were conducted on a subway line in Shanghai to analyze vibration propagation characteristics and validate a two-dimensional finite element model (FEM). Subsequently, based on the validated model, frequency-band excitation modification formulas were derived. Distinct from existing empirical approaches that often rely on simple statistical scaling, the proposed method utilizes parametric numerical analyses to determine frequency-dependent correction coefficients for four key parameters: tunnel burial depth, tunnel diameter, soil properties, and train speed. The reliability of the proposed method was verified through theoretical analysis and an engineering application. The results demonstrate that the proposed method improves prediction accuracy for tunnels in similar soft soil regions, reducing the prediction error from 10.1% to 5.2% in the engineering case study. Furthermore, parametric sensitivity analysis reveals that ground vibration levels generally decrease with increases in burial depth, tunnel diameter, and soil stiffness, while exhibiting an increase with train speed. This study improves the reliability of vibration prediction in the absence of direct measurements and provides a practical tool for early-stage design and vibration mitigation for unopened lines. Full article

Energy-efficient Train Control (EETC) strategy needs to meet safety, punctuality, and energy-saving requirements during train operation, and puts forward higher requirements for online use and adaptive ability. In order to meet the above requirements and reduce the dependence on an accurate mathematical model of train operation, this paper proposes a train-speed trajectory-optimization method combining data-driven energy consumption estimation and deep reinforcement learning. First of all, using real subway operation data, the key unit basic resistance coefficient in train operation is analyzed by regression. Then, based on the identified model, the energy consumption experiment data of train operation is generated, into which Gaussian noise is introduced to simulate real-world sensor measurement errors and environmental uncertainties. The energy consumption estimation model based on a Backpropagation (BP) neural network is constructed and trained. Finally, the energy consumption estimation model serves as a component within the Deep Deterministic Policy Gradient (DDPG) algorithm environment, and the action adjustment mechanism and reward are designed by integrating the expert experience to complete the optimization training of the strategy network. Experimental results demonstrate that the proposed method reduces energy consumption by approximately 4.4% compared to actual manual operation data. Furthermore, it achieves a solution deviation of less than 0.3% compared to the theoretical optimal baseline (Dynamic Programming), proving its ability to approximate global optimality. In addition, the proposed algorithm can adapt to the changes in train mass, initial set running time, and halfway running time while ensuring convergence performance and trajectory energy saving during online use. Full article

This study provides a comprehensive review of advancements in the field of train–track–subway system interaction dynamics and suggests future directions for research and development. Mathematical modeling of train–track–subway interaction system is addressed, including wheel–track contact mechanics and wear, train multibody dynamics, train–track system coupling dynamics, track slab subsystem dynamics, subway tunnel–ground interaction models, building vibration excited by ground-borne seismic waves, and noise. Advanced computing and simulation techniques used for numerical studies of the dynamics of train–track–subway system interaction in the past two decades are also addressed, including high-performance computing with efficient algorithms, multi-physics and multi-scale simulation, real-time hardware-in-the-loop simulation, and laboratory and field validation. The study extends the applications of train–track–subway interaction dynamics to subway route planning, structural and material design, subway maintenance, operations safety and reliability, and passenger comfort. Emerging technologies and future perspectives are also reviewed and discussed, including artificial intelligence, smart sensing and real-time monitoring, digital twin technology, and sustainable design integration. Full article

The vibrations induced by urban rail transit are exerting an increasingly prominent influence on the surrounding buildings and human health. As a prevalent track defect, rail corrugation can exacerbate the vibrations generated during train operation. In this study, on-site measurements were carried out to investigate the characteristics of rail corrugation in the small-radius curve segments of subways. The differences in rail corrugation with and without vibration mitigation measures were analyzed. Additionally, the vibration responses of adjacent buildings in the steel spring floating slab track segments with rail corrugation were examined. The findings of this study indicate that in the small-radius curve segments of the steel spring floating slab track, there exists a rail corrugation phenomenon with a wavelength of 200 mm. This leads to inadequate vibration attenuation in the 80 Hz frequency band, allowing some vibration energy to still be transmitted to adjacent buildings. Nevertheless, the vibration responses of buildings are predominantly governed by their own structural vibration modes. Full article

The DC traction power system adopts the track as the return rail. When the track-to-earth insulation in the subway tunnel deteriorates, stray currents will cause electrochemical corrosion to tunnel steel structures and seriously affect the service life and safety of metro tunnels. Stray currents cannot be directly measured and can only be calculated. Therefore, a calculation model with a hollow circular cross-section structure was proposed, and the stray current distribution in tunnel steel structures was calculated. In addition, the effects of different rail-to-ground transition resistances and adjacent buried metallic pipelines on the stray current distribution of the tunnel steel structures were taken into account. The results show that the total amount of stray current dispersed into the tunnel steel structures and soil is similar. The stray current density distribution in each steel tunnel is related to its location. The total stray current carried by the steel structures of the bottom tunnel segment is 102, 15.7 and 3.1 times higher than that of the top, upper and lower side tunnel segments, respectively. The reduction in the transition resistance and increase in the distance of the train from the traction substation increase the total rail leakage current and have a small effect on the percentage distribution of stray current in tunnel structures. The buried metal pipeline parallel to the tunnel has a lower impact on the total stray current leakage, but can reduce the total stray current in steel structures and drainage net, enlarging the positive stray current scope of some tunnel steel bars, further increasing the stray current density on tunnel steel bars. The results of this study can be used to determine the degree of corrosion of the underground steel tunnels and thereby provide support for corrosion prevention. Full article

With accelerating urbanization, subway stations, as high-energy-consumption sectors, face significant challenges in maintaining power supply stability and ensuring power quality. This paper proposed a novel voltage compensation solution utilizing superconducting magnetic energy storage (SMES) to suppress voltage fluctuations in the traction system of a large subway station with multiple lines, which was caused by frequent acceleration and regenerative braking of multiple subway trains. Using the MATLAB/Simulink platform, a model of the traction power system with SMES for a large subway station with multiple lines was constructed. Appropriate control methods and hierarchical control strategies were used to suppress voltage fluctuations in both single-line and multi-line configurations at subway stations. The technical advantages of SMES in rapid response and efficient charging/discharging were explored. Overall, results show SMES with the novel control strategies can effectively suppress voltage fluctuations on both single- and triple-line configurations, validating the feasibility in mitigating voltage fluctuations and enhancing regenerative braking energy utilization. Full article

As the operational speeds of subway trains in China incrementally increase to 160 km/h, the enclosed nature of tunnel environments poses significant challenges by restricting free airflow. This limitation leads to intense airflow disturbances and turbulence phenomena within tunnels, consequently exacerbating aerodynamic noise issues. This study utilizes compressible Large Eddy Simulation (LES) and acoustic finite element methods to construct a computational model of aerodynamic noise for subway trains within tunnels. It employs this model to compare and analyze the near-field noise characteristics of subway trains traveling at 120 km/h on open tracks versus in infinitely long tunnels. The findings indicate that the distribution of sound pressure levels on the surfaces of trains within tunnels is comparatively uniform, overall being 15 dB higher than those on open tracks. The presence of a high blockage ratio in tunnels intensifies the cavity flow between two air conditioning units, making it the region with the highest sound pressure level. The surface sound pressure spectrum within the tunnel shows greater similarity across different segments, with low-frequency sound pressure levels notably enhanced and high-frequency levels attenuating more rapidly compared to open tracks. It is recommended that in tunnels with high blockage ratios, the positioning of subway train air conditioning should not be too high, overly concentrated, submerged, or without the use of sound-absorbing materials. Such adjustments can effectively reduce the sound pressure levels in these areas, thereby enhancing the acoustic performance of the train within the tunnel. Full article

Modern subway platforms are generally equipped with platform screen door systems to enhance safety, but the gap between the platform screen doors and train doors may cause passengers or objects to become trapped, leading to accidents. Addressing the issues of excessive parameter counts and computational complexity in existing foreign object intrusion detection algorithms, as well as false positives and false negatives for small objects, this article introduces a lightweight deep learning model based on YOLOv11n, named GA-YOLOv11. First, a lightweight GhostConv convolution module is introduced into the backbone network to reduce computational resource waste in irrelevant areas, thereby lowering model complexity and computational load. Additionally, the GAM attention mechanism is incorporated into the head network to enhance the model’s ability to distinguish features, enabling precise identification of object location and category, and significantly reducing the probability of false positives and false negatives. Experimental results demonstrate that in comparison to the original YOLOv11n model, the improved model achieves 3.3%, 3.2%, 1.2%, and 3.5% improvements in precision, recall, mAP@0.5, and mAP@0.5: 0.95, respectively. In contrast to the original YOLOv11n model, the number of parameters and GFLOPs were reduced by 18% and 7.9%, respectfully, while maintaining the same model size. The improved model is more lightweight while ensuring real-time performance and accuracy, designed for detecting foreign objects in subway platform gaps. Full article

As an important traffic and transportation roadway, tunnel engineering is widely used in important fields such as highways, railways, water conservancy, subways and mining. It is limited by complex geological conditions, harsh construction environments and poor robustness of the monitoring system. If the construction process and monitoring method are not properly designed, it will often directly induce disasters such as tunnel deformation, collapse, leakage and rockburst. This seriously threatens the safety of tunnel construction and operation and the protection of the regional ecological environment. Therefore, based on distributed fiber optic sensing technology, the full–cycle spatiotemporally continuous sensing information of the tunnel structure is obtained in real time. Accordingly, the health status of the tunnel is dynamically grasped, which is of great significance to ensure the intrinsic safety of the whole life cycle for the tunnel project. Firstly, this manuscript systematically sorts out the development and evolution process of the theory and technology of structural health monitoring in tunnel engineering. The scope of application, advantages and disadvantages of mainstream tunnel engineering monitoring equipment and main optical fiber technology are compared and analyzed from the two dimensions of equipment and technology. This provides a new path for clarifying the key points and difficulties of tunnel engineering monitoring. Secondly, the mechanism of action of four typical optical fiber sensing technologies and their application in tunnel engineering are introduced in detail. On this basis, a spatiotemporal continuous perception method for tunnel engineering based on DFOS is proposed. It provides new ideas for safety monitoring and early warning of tunnel engineering structures throughout the life cycle. Finally, a high–speed rail tunnel in northern China is used as the research object to carry out tunnel structure health monitoring. The dynamic changes in the average strain of the tunnel section measurement points during the pouring and curing period and the backfilling period are compared. The force deformation characteristics of different positions of tunnels in different periods have been mastered. Accordingly, scientific guidance is provided for the dynamic adjustment of tunnel engineering construction plans and disaster emergency prevention and control. At the same time, in view of the development and upgrading of new sensors, large models and support processes, an innovative tunnel engineering monitoring method integrating “acoustic, optical and electromagnetic” model is proposed, combining with various machine learning algorithms to train the long–term monitoring data of tunnel engineering. Based on this, a risk assessment model for potential hazards in tunnel engineering is developed. Thus, the potential and disaster effects of future disasters in tunnel engineering are predicted, and the level of disaster prevention, mitigation and relief of tunnel engineering is continuously improved. Full article

To enhance the understanding and management of fire risks in subway stations, this study aims to identify critical fire risk transmission paths using an integrated PSR–DEMATEL–ISM approach. A comprehensive evaluation framework is first constructed based on the Pressure–State–Response (PSR) model, systematically categorizing 22 influencing factors into three dimensions: pressure, state, and response. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is then employed to analyze the causal relationships and centrality among these factors, distinguishing between cause and effect groups. Subsequently, Interpretive Structural Modeling (ISM) is applied to organize the factors into a multi-level hierarchical structure, enabling the identification of risk propagation pathways. The analysis reveals five high-centrality and high-causality factors: fire safety education and training, completeness of fire management rules and regulations, fire smoke detection and firefighting capability, operational status of monitoring equipment, and effectiveness of emergency response plans. Based on these key drivers, six major transmission paths are derived, reflecting the internal logic of fire risk evolution in subway environments. Among them, chains originating from Fire Safety Education and Training (S6), Architectural Fire Protection Design (S7), and Completeness of Fire Management Rules and Regulations (S16) exhibit the most significant influence on system-wide safety performance. This study provides theoretical support and practical guidance for proactive fire prevention and emergency planning in urban rail transit systems, offering a structured and data-driven approach to identifying vulnerabilities and improving system resilience. Full article

Subway train operators face the risk of cumulative cognitive stress due to factors such as visual fatigue from prolonged high-speed tunnel driving, irregular shift patterns, and the monotony of automated operations. This can lead to cognitive decline and human error accidents. Current monitoring of cognitive stress risk predominantly relies on single-modal methods, which are susceptible to environmental interference and offer limited accuracy. This study proposes an intelligent multimodal framework for cognitive stress monitoring by leveraging the symmetry principles in physiological and behavioral manifestations. The symmetry of photoplethysmography (PPG) waveforms and the bilateral symmetry of head movements serve as critical indicators reflecting autonomic nervous system homeostasis and cognitive load. By integrating these symmetry-based features, this study constructs a spatiotemporal dynamic feature set through fusing physiological signals such as PPG and galvanic skin response (GSR) with head and facial behavioral features. Furthermore, leveraging deep learning techniques, a hybrid PSO-CNN-GRU-Attention model is developed. Within this model, the Particle Swarm Optimization (PSO) algorithm dynamically adjusts hyperparameters, and an attention mechanism is introduced to weight multimodal features, enabling precise assessment of cognitive stress states. Experiments were conducted using a full-scale subway driving simulator, collecting data from 50 operators to validate the model’s feasibility. Results demonstrate that the complementary nature of multimodal physiological signals and behavioral features effectively overcomes the limitations of single-modal data, yielding significantly superior model performance. The PSO-CNN-GRU-Attention model achieved a predictive coefficient of determination (R²) of 0.89029 and a mean squared error (MSE) of 0.00461, outperforming the traditional BiLSTM model by approximately 22%. This research provides a high-accuracy, non-invasive solution for detecting cognitive stress in subway operators, offering a scientific basis for occupational health management and the formulation of safe driving intervention strategies. Full article

This study presents an AI-enhanced pedestrian simulation model for subway stations, combining the Social Force Model (SFM) with LiDAR trajectory data from Samseong Station in Seoul. To reflect time-dependent behavioral differences, RMSProp-based optimization is performed separately for the morning peak, leisure hours, and evening peak, yielding time-specific parameter sets. Compared to baseline models with static parameters, the proposed method reduces prediction errors (MSE) by 50.1% to 84.7%. The model integrates adaptive learning rates, mini-batch training, and L2 regularization, enabling robust convergence and generalization across varied pedestrian densities. Its accuracy and modular design support real-world applications such as pre-construction design testing, post-opening monitoring, and capacity planning. The framework also contributes to Sustainable Urban Mobility Plans (SUMPs) by enabling predictive, data-driven evaluation of pedestrian flow dynamics in complex station environments. Full article

Previous research has demonstrated that the Train Wave Signature (TWS) method enables rapid calculation of pressure waves in straight tunnels. However, its application to subway tunnels with complex structural features remains insufficiently explored. This study proposes a generalized mathematical method integrating TWS with graph theory for the simulation of pressure wave generation, propagation, and reflection in complex tunnel systems. A computational program is implemented using this method for efficient simulation. The proposed method achieves high-accuracy prediction of pressure waves in tunnels with complex geometries compared with field measurements conducted in a high-speed subway tunnel with two shafts. We discuss the impact of iteration time intervals on the results and clarify the minimum time interval required for the calculation. Moreover, the sin-type definition of TWSs enhances the precision of pressure gradient prediction, and omitting low-amplitude pressure and reflected waves from the train can improve computational efficiency without compromising accuracy. This study advances the application of TWSs in tunnels with complex structures and provides a practical solution for aerodynamic analysis in high-speed subway tunnels, balancing accuracy with computational efficiency. Full article

Assessments of subway train fires were conducted based on full-scale experiments and numerical simulations. The experimental platform and simulation model were established according to a real subway train in China. The results show that there was no obvious flame spread, and all the electrical circuitry maintained its integrity during a standard luggage fire. The maximum HRR (heat release rate) of the luggage fire obtained through the full-scale experiment was 155.5 kW, which was almost the same as the standard HRR curve provided in EN 45545-1. However, the fire only lasted approximately 180 s, which was much shorter than a standard fire (600 s). Through numerical simulations of an entire subway train, the side wall and roof ignited quickly, and the fire continually spread to the adjacent compartment under the extreme scenario with a gasoline pool fire and exposed winterproof material. The maximum HRRs of the luggage and gasoline pool fires were 179.7 and 17,800.0 kW, respectively. According to the experimental and simulation results, the Duggan method, which assumes that all combustibles inside a train compartment burn at the same time, was not appropriate for assessing the fires in the subway train, and a simple revised frame was proposed instead. Full article