To achieve the optimal coordinated evolution and development of the “tunnel-environment” composite system in mountain railways, a coupling element system is constructed from the perspective of micro-elements. The coordination level is analyzed using the coupling coordination degree model. Next, a double-layer complex network model is used to identify the main control elements for enhancing the greening level of tunnel engineering. Then, a Non-Linear Programming (NLP) model is established to quantify the coupling coordination connection of “tunnel-environment”, and the Simulated Annealing (SA) algorithm is further applied to regulate and optimize its evolution process, using the main control elements as the control variables. Finally, an analysis is conducted using a specific mountain railway tunnel project as a case study. The research results show that the initial coupling coordination degree of the “tunnel-environment” composite system is 0.675 8, indicating a primary coordination state. And the main control elements for coupling regulation are the tunnel section size, tunnel slag utilization rate, vegetation restoration rate at the tunnel entrance, permeability coefficient of the lining, permeability coefficient of the grouting circle, and fan power consumption. When the optimization ratios of these elements are 12.72%, 38.40%, 44.06%, 71.25%, 30.00%, and 23.56%, respectively, the optimal evolution value of coupling coordination is 0.799 0, achieving a well-coordinated state. The proposed model effectively explores the coordinated evolution path of the “tunnel-environment” system and provides new insights for optimizing green tunnel design and promoting the green and coordinated development of “tunnel-environment” in mountain railways.

To optimize the grouting parameters for tunnels in urban sand layers and improve grouting performance, a laboratory test system is independently developed in response to practical engineering challenges. The experimental design considers grouting pressure, soil porosity, and slurry water-cement ratio as independent variables, while the strength of the solidified body, slurry diffusion radius, and grouting volume are selected as dependent variables. Regression analysis is conducted on the test results to derive equations describing the relationships between these variables. Based on the resulting linear regression models, optimized on-site grouting parameters are determined. The effectiveness of these parameters is evaluated through observations of excavation and slurry distribution at the tunnel face, core sample strength of solidified body, and surface settlement behavior. The results indicate that the derived equations exhibit strong linear correlations and statistically significant regression coefficients, as confirmed by analysis of variance and correlation tests. Among the influencing factors, grouting pressure has the most significant impact on grouting volume, followed by slurry diffusion radius and solidified body strength. Soil porosity most strongly affects grouting volume, followed by strength, with the least impact on diffusion radius. The slurry water-cement ratio has the greatest effect on the strength of the solidified body, followed by grouting volume, and the least on diffusion radius. Field application of the optimized parameters reveals abundant and evenly distributed slurry veins during excavation. The tunnel face remains stable and upright, with no groundwater seepage. Core sample strength of solidified body meet design requirements, and surface settlement remains within acceptable limits. These outcomes indicate that the optimized grouting parameters significantly enhance grouting performance and effectively ensure the quality and safety of tunnel construction in sandy urban strata.

To address the issue of excessive ground settlement caused by improper design of tunneling parameters in slurry shield machines, this study proposes an intelligent optimization method for shield tunneling parameters that integrates machine learning algorithms. First, a five-step optimization framework is established, comprising geological information encoding, engineering data processing, shield response parameter prediction, ground settlement prediction, and control parameter optimization. Then, a data preprocessing pipeline tailored to the characteristics of shield tunneling data is developed to construct a sample database. Next, solution algorithms are respectively formulated for shield response prediction, ground settlement prediction, and control parameter optimization by applying Long Short-Term Memory (LSTM) neural networks and the Particle Swarm Optimization (PSO) algorithm. Finally, the proposed method is validated using a case study of the large-diameter slurry shield tunnel section between the Jingha Expressway and Luyuan North Street in the Beijing East Sixth Ring Road underground renovation project. The results indicate that geological encoding is an effective means of incorporating unstructured geological information into machine learning models. The shield response prediction model and ground settlement prediction model, both incorporating geological encoding as input, achieve an R² of 0.92 on the test data, demonstrating strong predictive performance. Under the settlement control standard of -10 to 5 mm, the excavation parameters generated by the intelligent optimization method reduce the average ground settlement by 29% compared to the measured data, offering valuable guidance for practical engineering applications.

To explore the magnitude, development, and patterns for displacement and deformation of operational underground utility tunnel structures under surface traffic-induced micro-disturbances, a case study is conducted using a soft-soil underground utility tunnel sub-project within the road network of Suqian. A detailed two-dimensional finite element model is developed in forward modeling tool Abaqus to simulate the pavement (including varying vehicular loads), soil strata, utility tunnel structure and its surrounding elements (e.g., grouting material, backfill, and tunnel base layer). The study investigates the impact of vehicular vibration forces under varying vehicle speeds v, lateral positions x, and load distribution symmetries τ on tunnel deformation behavior through numerical simulations. The results reveal the following: When vehicle loads are symmetrically distributed along the left and right lanes (τ=2), the main structure of the tunnel located directly below the central road divider can exhibit vertical oscillations of several millimeters. However, the peak horizontal dynamic displacement is only in the order of tens of micrometers, indicating a significant disparity between vertical and horizontal deformation scales. In the case of single-lane traffic (τ=1), where vehicular loads act solely on one side of the tunnel, the structure experiences differential settlement along its lateral direction, causing an inclination toward the loaded side. As the lateral distance x between the load application area and the mid-span section of the tunnel roof beam increases, the differential displacement between the two sides of the tunnel initially grows and then decreases. Moreover, such loads can induce horizontal displacements of approximately 1 mm in the tunnel structure. In general, regardless of single-lane or two-way traffic, an increase in vehicle speed v results in greater maximum vertical displacements at monitoring points on or near the tunnel structure. The influence of the independent variables on structural deformation ranks in the order of v<x<τ. This study generates a large dataset of deformation histories for operational utility tunnels through finite element simulations, providing a valuable reference for subsequent machine learning model training in related research.

To route planning problem for trains in station throat areas, this study investigates optimization algorithms under scenarios involving the actual station throat layout, train routing within the throat area, train length, and speed. First, a time-space network is constructed to represent train movements in the station throat area, framing the routing problem as a time-space allocation issue with limited resources. A network flow model is then established. Subsequently, an algorithm based on a discrete event model is developed to simulate train operation plans in the station throat area, given a predefined train priority sequence, resulting in feasible routing solutions. Furthermore, a train priority sequence optimization algorithm, utilizing the Tabu Search (TS) algorithm, is developed to minimize operational delays. Finally, the throat area of a specific station is analyzed as a case study. Results demonstrate that the proposed TS-based priority optimization algorithm effectively resolves train routing conflicts in the station throat area, optimizes delay and waiting times, and achieves convergence within 4 minutes to provide a satisfactory routing solution.

Under the most unfavorable loading conditions in the negative moment region of a typical continuous beam, this study investigates the mechanical performance differences between an integral bridge deck and a key-tooth glued joint bridge deck. To analyze the load-bearing behavior and flexural capacity of key-tooth glued joint composite beams under various influencing factors, an expression for flexural capacity is proposed that incorporates the steel beam, epoxy resin adhesive, prestressed reinforcement, and key-tooth geometry. To validate this expression, two steel-concrete composite beams with detachable high-strength bolt shear keys and bridge decks are designed and fabricated, including one with an integral bridge deck (N1 composite beam) and one with a segmental precast key-tooth glued joint bridge deck (N2 composite beam). Static load tests are conducted on a test platform, and finite element modeling is performed in Abaqus to study the crack development, load-displacement and load-strain relationships, and failure modes of both composite beams. Results indicate that, in the post-failure loading phase, the N1 composite beam exhibits bending failure, while the N2 composite beam shows bending-shear failure, with respective bearing capacities of 675 kN and 605 kN. The ultimate bearing capacity of the N2 composite beam in the negative moment region decreases by approximately 11%. The N1 composite beam demonstrates a curvature of 20.5, ductility deflection coefficient of 5.57, and section curvature of 42.46×10^-6, while the N2 composite beam has a curvature of 30.7, ductility deflection coefficient of 6.20, and section curvature of 98.42×10^-6, showing an approximate 10% increase in ductility and a 130% increase in rotational capacity compared to the N1 composite beam. The cracking load of the N1 composite beam is 33% lower than that of the N2 composite beam, with cracks in the N1 beam distributed widely, closely spaced, and numerous, whereas the N2 beam has cracks concentrated near the key teeth, with wider spacing and fewer occurrences. Comparing the calculated values of the proposed flexural capacity expression with test and finite element values, differences are found to be within 5%, demonstrating the expression’s feasibility.

Cracks in high-speed railway track slabs pose a severe threat to the safety of vehicle operations. To address the issue of ineffective crack repairs in current maintenance practices, this study proposes a multi-class crack detection method based on the YOLOv8-DSC model. First, the Dynamic Snake Convolution (DSC) module is incorporated into the backbone network. Based on this, the Bottleneck structure in C2f is reconstructed and established as the C2f-v1 module, which replaces certain C2f modules in the YOLOv8 backbone network to enhance the extraction of multi-scale detailed features related to ineffective crack repairs. Second, the CBAM attention mechanism is introduced into the neck network to improve the model’s focus on critical features, enhancing the transmission of small crack features within the neural network. Third, the SIoU loss function is employed to replace CIoU, reducing the excessive penalization caused by geometric factors and minimizing training interference, thereby increasing the model'‍‍s generalization capability for similar cracks. Finally, the proposed method is validated and evaluated in four dimensions: network structure, crack data, classification methods, and environmental conditions. Experimental results demonstrate that, compared to the original YOLOv8 model, the YOLOv8-DSC model significantly reduces both missed and false detections of ineffective repaired cracks in track slabs. The model achieves a 4.6% increase in mean average precision (mAP) and a 4.0% improvement in recall, demonstrating strong robustness and adaptability under adverse environmental conditions. The method effectively enables accurate detection of ineffective crack repairs in track slabs.

Current collaborative robots typically adopt a serial rigid structure, which limits their compatibility with human environments and constrains their range of applications. To address the shortcomings of collaborative robots in terms of flexibility and environmental adaptability, a 6-degree-of-freedom hybrid serial-parallel robotic arm, SoftArm-6, is designed based on the flexible parallel driving mechanism of human arm muscles. The robotic arm consists of three serial degrees of freedom in the arm section and three parallel degrees of freedom in the wrist section, with flexible joints driven by Series Elastic Actuators (SEA). By establishing a kinematic model for the robotic arm and applying the principle of mechanism equivalence, the posture decoupling of the hybrid serial-parallel robotic arm is realized. Furthermore, an online trajectory teaching method based on Kinect human motion capture is proposed. To address issues such as reduced positioning accuracy and susceptibility to oscillations caused by the SEA flexible joints, a feedforward gravity compensation algorithm based on the virtual displacement principle is designed. Finally, the trajectory tracking and teaching-based grasping experiments are conducted on the SoftArm-6 prototype. The results show that the deviation in trajectory tracking tasks is less than 1.5%, and the grasping success rate reaches 98%, significantly improving both operational precision and environmental adaptability. This design provides new technical support for the application of collaborative robots in complex and uncertain environments.

To improve the efficiency of loading and unloading large-sized cargo in freight train EMUs operations and meet energy-saving and emission-reduction goals, this study proposes the design of a super-large opening door made from carbon fiber composite materials, along with an automated opening and closing mechanism. This design aims to reduce vehicle weight and energy consumption while meeting the high strength, high stiffness, and low weight requirements of large-opening doors. First, the structural design of the carbon fiber composite door draws on mature application cases from aviation and rail freight transportation. Then, the HyperWorks commercial simulation software is used to conduct a numerical simulation study on the structural design of the door body. Through an in-depth analysis of the effects of longitudinal beams, ring beams, and skin structure on the stiffness and strength of the carbon fiber composite ultra-large loading door, the structural design and material layup are optimized. Finally, a large-scale test rig is designed and loaded to simulate internal and external air pressure loads for structural testing, verifying the mechanical properties and locking performance of the ultra-large loading door. The results indicate that the integration of 7 ring beams and 16 peripheral locking devices within a plate-beam structure of the door leaf effectively satisfies the requisite strength and stiffness criteria. Furthermore, the manufacturing process has been demonstrated to be feasible and operational, yielding significant optimization outcomes and meeting practical technical specifications.

This study addresses the evolution law of wheel wear in heavy-haul freight wagons and the rolling contact fatigue behavior of worn wheel profiles across various operational mileages. Utilizing the vehicle-track coupling theory, the Kik-Piotrowski wheel-rail contact algorithm, the Archard wheel wear model, and the Dang Van rolling contact fatigue criterion, a dynamic model of a heavy-haul freight wagon is developed in UM software to investigate evolution patterns of wheel profile wear and rolling contact fatigue damage under different mileages. The results show that when the running mileage of the vehicle reaches 35 000 km, the flange wear of the first and second wheelset is 2.04 and 0.75 mm, respectively. The flange wear of the first wheelset is more severe. The cumulative damage distribution of the first wheelset is broader than that of the second wheelset, while the cumulative damage of the second wheelset is higher than that of the first wheelset, which is concentrated at 6~9 mm on the tread. As mileage increases, the wear width of the first wheelset expands, reaching-35~36 mm on the tread. The maximum cumulative damage contact point of the first wheelset from the inner to the outer edge and subsequently toward the wheel root. Conversely, then the maximum cumulative damage contact point of the second wheelset moves from the outer to the inner edge, then toward the wheel end. The first wheelset exhibits the highest wear, but its rolling contact fatigue damage is lower than that of the second wheelset. An inhibitory relationship between wheel wear and rolling contact fatigue damage promotes rolling contact encourages fatigue damage to migrate toward the wheel end. It is recommended to apply wheel or rail lubrication to reduce flange wear in early operations. After the vehicle runs to 30 000 km, attention should focus on damage around the 5~15 mm region of the middle wheel tread. If necessary, a “grinder” can be employed to increase wheel wear and thereby reduce overall wheel damage.

Cracks in the wall of railway tunnel portals pose a serious threat to the operational safety of rail transport. To address the limitations of manual inspection, such as long inspection cycles and low accuracy, this study proposes a crack detection method for railway tunnel portal walls based on Unmanned Aerial Vehicle （UAV） inspection and the RFA-YOLOv8 model. First, high-resolution image data of railway tunnel portals are captured through UAV inspection. The collected images are preprocessed, and cracks in the tunnel portal wall are annotated to construct a training dataset for wall cracks. Second, the YOLOv8 object detection model is enhanced to better accommodate the characteristics of tunnel portal cracks. The Receptive Field Attention Convolution (RFAConv) is integrated into a newly designed C2f_RFA module, replacing the original C2f module in the backbone network to improve the model’s focus on crack-prone areas. The BiFPN structure is introduced in place of the original feature fusion network to enhance the model’s detection effect for targets of different scales. Additionally, the EIoU loss function is adopted to replace the CIoU, minimizing the differences in height and width between predicted box and ground-truth bounding boxes, thereby improving the model’s detection accuracy. Finally, the RFA-YOLOv8 model is validated and evaluated from three aspects: Comparative experiments, ablation experiments, and visualization of detection results. Experimental results demonstrate that, compared to the original YOLOv8 model, the RFA-YOLOv8 model reduces the missed detection of small cracks, increasing recall by 3.8% and mean average precision by 2.5%. The proposed method effectively leverages UAV-captured tunnel portal images for accurate crack detection.

To address the challenges associated with the harsh operating conditions of train axle box bearings, where fault signals are often obscured by noise and extracting fault features remains difficult, this study proposes a Variational Mode Decomposition (VMD) parameter optimization method. The method integrates envelope entropy and kurtosis into a Harmonic Mean Index (HMI) fitness function. The algorithm is validated using fault data from a proportional test bench. First, to ensure that the synthetic function effectively captures both the periodicity and impulsiveness of signals at comparable magnitudes, the harmonic mean index is introduced. This index, combining kurtosis and envelope entropy, serves as the fitness function, and the Pelican Optimization Algorithm (POA) is employed to perform a global search for optimal values. Second, the crucial parameters of VMD are optimized and determined using the HMI-POA algorithm, including the optimal decomposition layer number K and punishment factor α. These crucial parameters are then applied to decompose fault signals into K Intrinsic Mode Function (IMF), with the optimal component identified based on the Weighted Kurtosis (WK) index. Finally, the envelope demodulation of the optimal component signal is performed to extract the fault characteristic features of the rolling bearings. The proposed HMI-POA-VMD algorithm is validated using fault data from a proportional test bench. Its superiority is further demonstrated through comparison with traditional methods, using the Fault Feature Coefficient (FFC) as the evaluation criterion. Experimental results show that the proposed method significantly enhances the extraction of fault frequencies. Compared to single fitness function optimization and traditional VMD, the FFC improves by 49.1% and 62.5% respetively. This highlights the method’s capability to extract richer fault frequency information and accurately identify features in noisy environments.

In response to the insufficient research on the electromagnetic characteristics of the Eddy Current Braking System (ECBS) for high-speed maglev trains in engineering, an electromagnetic model is proposed. The relationship between braking force and parameters such as train operational speed, number of magnetic poles, excitation current, secondary plate thickness, number of turns in excitation coil, and structure of iron core cogging is established. First, the ECBS is divided into three solution regions based on the equivalent current layer method, and the magnetic field distribution expressions for each region is calculated. Second, a model for the distribution of induced current density is developed, accounting for the skin effect’s impact on the conductivity of the secondary plate material, and parameter corrections are made. The conductivity correction coefficient for the secondary plate material is then introduced, and the magnetic flux density expression at the interface between the secondary plate and the air gap is calculated using the boundary conditions in each solution region. Finally, using the Maxwell stress tensor method, the braking force expression is derived, and a three-dimensional finite element simulation model is built based on the parameters of the ECBS for the maglev train. The results show that, in the analysis of braking force variations with respect to speed, excitation current, and secondary plate thickness, the average relative error between the analytical calculations and finite element simulation results is within 10%, validating the mathematical model’s effectiveness. The braking force first increases and then decreases with the increase of train speed, peaking at 50 km/h. It increases with the rise in excitation current, excitation coil turns, and other parameters. The braking force first increases and then stabilizes as magnetic pole pitch, secondary plate thickness, and other parameters increase. The braking force remains stable with increasing primary groove depth and decreases rapidly after surpassing the critical point.

Time series anomaly detection, however, faces numerous challenges due to the complexity of data characteristics, algorithmic requirements, and diverse application scenarios. To address this, this paper presents a comprehensive survey of time series anomaly detection. First, the paper systematically analyzes the complexity and challenges of time series anomaly detection tasks from three dimensions: data characteristics, algorithm requirements, and application scenarios. Second, it categorizes anomalies in time series into point anomalies, subsequence anomalies, and inter-variable correlation anomalies, providing a detailed exposition of the definitions and detection methods for each type. Third, the paper reviews and analyzes the use of traditional statistical methods, machine learning techniques, and deep learning approaches in time series anomaly detection, evaluating their applicability and limitations. Subsequently, it compiles widely used time series anomaly detection datasets, analyzing the application scenarios and unique features of each dataset. Finally, it discusses future research directions in time series anomaly detection from five perspectives: anomaly localization, anomaly classification, precursor forecasting, interpretability, and integration with large-scale models. The review highlights that current challenges, including data scarcity, anomaly diversity, and concept drift, remain unresolved. Future anomaly detection research is expected to evolve toward more granular tasks such as anomaly localization and prediction.

With the increasing demand for urban public transportation, existing bus stations struggle to provide adequate vehicle docking services. This study addresses the optimization of berth allocation in bus stations. First, to depict the complete process of selecting berths and completing bus docking within a hub station, four types of spatiotemporal network arcs are constructed: entrance waiting arc, berthing travel arc, berth docking arc, and unparking travel arc. Next, based on these spatiotemporal network arcs, an integer programming model for bus berth allocation is developed, integrating the number of berths, characteristics of bus routes, and aiming to minimize entrance waiting time and balance berth resource utilization. A linearization model with auxiliary 0-1 decision variables is introduced to enhance decision-making. Finally, taking the Sihui Bus Hub as an example, the model's accuracy and effectiveness are verified through a Python-implemented program and solved using the Gurobi optimization software. The results demonstrate that the reallocating bus parking platforms and berths can maximize existing resources and significantly enhance bus system operational efficiency, reducing bus waiting time costs and berth utilization variance by 26 minutes and 724 minutes, respectively, representing an overall improvement of nearly 48% compared to the original scheme. When new bus routes are added, optimizing the berth allocation scheme for the existing routes yields superior outcomes, particularly in achieving balanced berth usage.

To further enhance the durability of high-toughness repair mortar under aggressive environmental conditions, this study investigates the effects of Polyvinyl Alcohol (PVA) fibers on the mechanical properties and durability of high-toughness repair mortar, along with the underlying microscopic mechanisms. First, high-toughness repair mortar specimens with PVA fiber contents of 0.0%, 0.5%, 1.0%, 1.5%, and 2.0% are prepared and cured under standard conditions for 3 and 28 days. Their compressive strength, flexural strength, bond strength, shrinkage performance, and frost resistance are evaluated. Subsequently, composite specimens of high-toughness repair mortar with varying PVA fiber contents are fabricated. The compressive and flexural strengths of these specimens are evaluated undergoing a 56 days period of wet-dry cycling. Finally, mercury intrusion porosimetry and scanning electron microscopy are employed to characterize the pore structure and micro-morphology of the mortar. Results indicate that with increasing PVA content, the compressive strength of the mortar initially increase and then basically stabilizes, while the flexural strength and bond strength gradually increase. The frost resistance rises initially but slightly declines at higher PVA contents. In a sulfate-rich environment, PVA fibers act as bridges and modify the pore structure, mitigating expansion stress caused by ettringite and other erosion products filling the pores, thereby enhancing the mortar’s erosion resistance. However, when the PVA fiber volume reaches 2.0%, excessive filling of erosion products induces microcracks in the matrix, and compressive strength corrosion resistance coefficient ceases to improve.

The stability analysis of new energy grid-connected systems typically employs impedance analysis method, which necessitates determining the dq impedance of grid-connected converters. However, traditional impedance measurement methods often suffer from low measurement efficiency and high requirements for measurement equipment. To address these challenges, this study proposes a dq impedance measurement method for grid-connected converter based on the principle of single-phase harmonic voltage injection. First, the principle of dq impedance measurement for grid-connected converters based on harmonic injection is analyzed, clarifying that two independent sets of disturbance voltages and currents are required for dq impedance measurement. Then, the principle of single-phase harmonic injection dq impedance measurement is given. Combined with the analysis of the relationship between injection frequencies during multi-frequency point measurement, the study designs the measurement steps for single-phase harmonic injection dq impedance measurement for both single-frequency point and multi-frequency point impedance measurements of grid-connected converters. Finally, simulations and experimental measurements are conducted to evaluate the impedance of grid-connected converters under single current loop control and voltage-current double loop control. The results show that, compared with the traditional measurement method of three-phase harmonic injection, the single-phase harmonic injection dq impedance measurement method reduces the requirements for harmonic injection equipment while maintaining measurement accuracy. Additionally, when measuring at multiple frequency points, the measurement times of the single-phase harmonic injection method are less than that of the traditional three-phase harmonic injection method, which reduces the measurement amount and improves the measurement efficiency.

To address the challenges posed by fully enclosed freeway segments, high vehicle speeds and the substantial damage caused by traffic accidents, this study proposes a freeway accident risk assessment method that integrates the Random Forest (RF) algorithm for feature selection with the eXtreme Gradient Boosting (XGBoost) algorithm. First, by filtering private vehicle trajectory data from freeway accident segments, a data foundation for accident risk assessment is established under four different spatiotemporal conditions (30 km upstream and 30 minutes before the accident, 10 km upstream and 15 minutes before the accident, 10 km upstream and 10 minutes before the accident, and 10 km upstream and 5 minutes before the accident). Next, a combined accident risk assessment method based on the RF and XGBoost is constructed. It evaluates accident risk after selecting various operational indicators for vehicles on the freeway. Finally, the algorithm’s performance is assessed using five metrics: accuracy, precision, recall, balanced F Score (F1), and Area Under Curve (AUC). Results indicate that the RF-XGBoost combination algorithm outperforms the Decision Tree (DT), Support Vector Machine (SVM), and traditional XGBoost algorithms in accident risk assessment. Compared to the traditional XGBoost algorithm, the average accuracy of the RF-XGBoost algorithm is increased by 11.1%, the average precision is increased by 8.9%, and the average recall rate is increased by 7.625%. Under the spatiotemporal condition of 10 km upstream and 10 minutes before the accident, the algorithm achieves an accuracy of 80%, demonstrating optimal overall assessment performance. These findings provide theoretical and methodological support for freeway accident risk assessment and dynamic warnings for private vehicles.

The configuration of hot standby EMUs in high-speed railway networks directly affects emergency response efficiency and operational costs. To address issues such as coarse management and inadequate risk coverage in existing configuration approaches, this study proposes an optimization method based on comprehensive accident risk coverage across the network. First, considering both the timeliness and cost-effectiveness of emergency response, a multi-objective planning model is constructed with dual objectives to minimize emergency response time and configuration costs for hot standby EMUs. The model integrates point coverage and arc segment coverage methods to accurately represent the requirements for sequential tasks and inter-section rescue scenarios. It incorporates constraints related to emergency rescue point demands, response time limits, and full coverage of risk points, enabling effective adaptation to complex railway networks and the uncertainty of sudden incidents. Second, the ε-constraint method is used to solve the model, overcoming limitations associated with traditional weighted coefficient methods such as dimension normalization and parameter setting. This approach generates multiple sets of Pareto-optimal solutions, providing flexible options for decision-making. Finally, a case study is conducted on the high-speed railway network managed by a specific railway bureau. The research results indicate that the model produces reasonable configuration schemes aligned with various optimization objectives. As the number of hot standby EMUs increases, the maximum reduction in emergency response time reaches 29.3%, while configuration costs remain effectively controlled. Sensitivity analysis confirm the model’s strong adaptability to varying response time constraints and risk point distribution characteristics. This method provides a theoretical basis and practical guidance for the scientific configuration of hot standby EMUs and serves as a reference for enhancing the emergency management system of high-speed railways.

In recent years, with the expansion of rail transit networks and increasing complexity, along with the growing passenger demand, the operational load of the system has risen, while operational resilience has decreased. The negative impacts of emergencies, such as train delays and operational disruptions, have become more severe. There is an urgent need to study timetable rescheduling optimization methods to assist in decision-making, improve the quality of rescheduling schemes, and reduce the negative impacts of emergencies. This paper applies the bibliometric method to analyze the research hotspots related to timetable rescheduling. It categorizes studies on single-line rescheduling under disturbances or disruptions, focusing on scenarios involving minor disturbances and severe disruptions. Related research is further categorized based on train operation or passenger behavior, track equipment or train failures, static or dynamic input parameters, and micro or macro modelling perspectives. Regarding rescheduling problems under multi-line conditions, the paper first reviews studies that reschdule the timetable of only the affected lines. It then discusses multi-line collaborative rescheduling for failure and non-failure lines, based on both split-line operation and cross-line operation modes. Future research directions are suggested in five areas: enhancing the typicality of emergency scenarios, integrating more deeply with actual operational needs, improving the robustness of rescheduling solutions, flexible combining and applying multiple strategies, and enhancing the model-solving efficiency. The results indicate that the research on timetable rescheduling began with train delay propagation theory and has evolved from single-line rescheduling to multi-line collaborative rescheduling. Among them, the optimization methods for single-line rescheduling are widely applied, However, most existing multi-line collaborative rescheduling methods are focused on intercity rail systems, and are challenging to implement in urban rail transit systems due to their unique characteristics.

The operation plan of Electric Multiple Units (EMUs) is a critical component in fulfilling transportation tasks for high-speed railways. It plays a crucial role in reducing operational costs and enhancing EMU utilization efficiency, with its rationality directly determining the overall quality of railway operations. Currently, formulation of EMU operation plans in railway practice heavily relies on manual experience, leading to inefficiencies and prolonged planning cycles. Consequently, improving the efficiency and quality of EMU operation planning through mathematical models and algorithms has become a focal point of academic research. This paper provides a comprehensive review of domestic and international studies on EMU operation planning. First, it defines the concept of EMU operation planning and examines the distinctions and interconnections among routing, allocation, and maintenance plans. Second, it systematically reviews the current state of research on EMU routing plans from three perspectives: operational conditions, models and algorithms for routing plan formulation, and EMU rescheduling under emergency scenarios. Additionally, it summarizes the modeling methodologies for EMU allocation planning and analyzes the latest advancements in models and algorithms for high-level maintenance scheduling. Finally, the paper identifies existing challenges in the formulation of EMU routing, allocation, and maintenance plans and outlines potential directions for future research. The research results indicate that early studies on EMU operation optimization primarily focused on improving routing and allocation plans to enhance utilization efficiency. With the expansion of railway network and the increase in the number of EMUs, issues related to real-time rescheduling and high-level maintenance planning have gained increasing attention. Future research should explore the collaborative optimization of maintenance and routing plans, the coordinated adjustment of train schedules and routing plans during emergencies, and precise prediction methods for high-level maintenance scheduling windows.

To address the limitations in current research on the heat transfer characteristics and mechanisms of energy diaphragm walls, this study examines the influence of various factors on heat transfer efficiency. First, a three-dimensional numerical model is established, with simulation data validated against measured data to ensure model accuracy. Based on this model, a 90-day operational period is simulated, during which the effects of branch pipe spacing, inlet water temperature, circulating water flow rate, initial ground temperature, and soil thermal conductivity on the heat transfer efficiency of energy diaphragm walls are analyzed using single-factor tests. Finally, the influence of each factor on thermal efficiency of energy diaphragm walls is compared through orthogonal tests. The results indicate that increasing branch pipe spacing, inlet water temperature, circulating water flow rate, and soil thermal conductivity, as well as reducing initial ground temperature, all contribute to enhanced heat transfer rates. Notably, increased branch pipe spacing and circulating water flow rate yield better improvements in heat transfer efficiency during the early stages of system operation than later stages, while higher soil thermal conductivity proves more beneficial in the later stages. The ranking of factor influence on heat transfer efficiency for energy diaphragm walls is as follows: inlet water temperature>initial ground temperature>branch pipe spacing>soil thermal conductivity>circulating water flow rate.

To fully utilize the transport capacity of high-speed railways, improve operational efficiency, and enhance economic benefits, this study addresses the vehicle-cargo matching optimization problem in high-speed rail express freight services. The research comprehensively considers three transport modes, passenger-freight mixed operation, reserved carriages, and dedicated EMU trains, and four categories of goods: fresh produce, urgent documents, electronic products, and valuables. A two-stage stochastic programming model is proposed. In the first stage, train operation plans are determined, including the selection of transport modes for each train and decisions on whether to dispatch dedicated EMU trains. In the second stage, based on the train plans from the first stage, optimal vehicle-cargo matching schemes are determined under various demand scenarios. To solve the model, a Mixed-Integer Programming-based Genetic Algorithm (MIP-GA) is designed. Finally, case studies are conducted using data from the Beijing-Shanghai high-speed railway, including four types of goods with stochastic demand, 5 500 containers, and 24 trains comprising a total of 254 carriages. The model is validated through analysis of the value of stochastic solutions and further tested across 12 case sizes to evaluate algorithm performance. The results show that the stochastic programming model effectively manages demand fluctuations, reducing costs by 3.8% and increasing the on-time delivery rate by 13% compared to the average-demand model. The MIP-GA significantly accelerates computation, saving an average of 75.88% in solving time, with an average optimality gap of only 0.20% compared to Gurobi, thereby enhancing computational efficiency without compromising solution quality.

To address challenges such as passenger crowding, excessive waiting times, and inefficient use of transportation resources in metro-to-high-speed rail transfer scenarios, this study proposes an optimization method for metro-to-high-speed rail passenger flow dispersion based on Multi-Agent Reinforcement Learning (MARL). The method dynamically adjusts metro timetables to enhance passenger dispersion efficiency, alleviate crowding, and improve the utilization of transportation resources. First, the metro-to-high-speed rail passenger flow dispersion optimization problem is formulated as a Markov game by integrating the spatiotemporal information of metro operations and the spatiotemporal characteristics of passenger transfers, with general state features, action space, and a reward function specifically designed. Second, a multi-agent decision-making model is then developed using the Actor-Critic (AC) framework, and an asynchronous action coordination mechanism is introduced within a centralized training and distributed execution architecture to enhance training efficiency. Finally, an optimization study is conducted using the Tianjin West railway station as a case study. Results indicate that the proposed method significantly reduces passenger waiting times and improves metro operational efficiency. The average passenger waiting time decreases by 26.80%, while the average metro operational efficiency increases by 14.11%.

Existing research on regional railway network characteristics often overlooks the degree of connectivity between stations and tends to adopt a singular analytical perspective. To address these limitations, this study examines the railway passenger transport network of the Chengdu-Chongqing urban agglomeration from the perspective of community division, exploring the complex structural and functional characteristics of the railway network. First, considering both railway infrastructure and train operations, the Space L and Space P methods are employed to construct models of the railway passenger physical network and service network, respectively. Next, the overall network characteristics are analyzed through topological statistical indicators such as degree distribution and average path length. Finally, community structure theory is introduced to investigate the internal network characteristics. A community division method based on an improved particle swarm optimization algorithm is proposed to further analyze the community composition, geographical distribution, and internal connectivity of the Chengdu-Chongqing railway passenger transport network. The results indicate that the railway passenger physical network of the Chengdu-Chongqing urban agglomeration exhibits scale-free characteristics, while the service network demonstrates small-world characteristics. The physical network is divided into 12 communities, whose spatial distribution exhibits obvious geographical patterns closely aligned with the main railway lines. The service network is divided into 8 communities, with a spatial distribution that transcends geographical constraint, where stations within the same community display long-distance interactions. Additionally, each community network retains small-world characteristics. The study’s conclusions provide valuable insights for promoting the integrated development of the Chengdu-Chongqing railway passenger transport network and enhancing its overall reliability.

Traditional mechanical displacement sensors are often expensive and highly sensitive to environmental factors. To address these issues, a novel displacement sensorless control strategy for Bearingless Switched Reluctance Motors (BSRM) is proposed by applying the Model Reference Adaptive System (MRAS) to the observation of radial displacement. First, the mathematical model of the bearingless switched reluctance motor with shared suspension windings is presented, and reference and adjustable models are established using winding currents as state variables. Second, a proportional-integral adaptive law is chosen to construct the displacement sensorless control system, and the stability of the system is verified using Popov’s hyperstability theory. Finally, simulation and experimental studies are conducted under a wide speed range with external disturbances. The results demonstrate that the system is capable of real-time tracking and accurate estimation of the rotor’s radial displacement under varying speeds and external disturbances. The displacement signal obtained from the displacement sensorless observation scheme is fed back into the motor’s suspension control system, maintaining the radial displacement within the range of -0.08 mm to 0.08 mm at three typical speeds of 500, 1 000, 1 500 rpm and under external disturbances. This system successfully achieves stable suspension of the motor without displacement sensor.

To enhance traffic operation efficiency and improve driver and passenger comfort in highway merging areas while ensuring safety, this study proposes an optimization method for highway merging order and trajectory planning for Connected and Autonomous Vehicles (CAVs) in a heterogeneous traffic flow environment where Human Driven Vehicles (HDVs) and CAVs coexist. First, vehicle travel time and delay are used as performance indicators to characterize traffic operation efficiency in the merging area, and a merging order optimization function is established. The Monte Carlo Tree Search (MCTS) algorithm is used and adjusted to determine the optimal merging order. Then, based on the optimized merging order, a CAV merging trajectory planning function, referred to as Minimize Acceleration and Jerk Trajectory Planning(MAJTP), is established. By applying optimal control theory, the analytical solution for the longitudinal optimal trajectory of CAVs is derived, forming a cooperative control strategy for highway merging. Finally, traffic simulations are conducted using the SUMO software and PYTHON libraries to validate the proposed method. Simulation results demonstrate that, at CAV penetration rates of 0.2, 0.4, 0.6 and 0.8, the MCTS-based merging order optimization method reduces cumulative delay by 5.75%, 8.84%, 12.24%, and 11.06%, respectively, compared to the First In First Out (FIFO) algorithm. Additionally, compared to the Minimize Acceleration Trajectory Planning (MATP) method, the MAJTP approach results in an average jerk value closer to zero, thereby enhancing ride comfort and verifying the effectiveness of the method. These findings provide theoretical support for traffic management and control strategies in highway merging areas.

To address issues arising from operational interference between mainline and cross-line trains, specifically the “long-distance but short-flow” problem for cross-line trains and the “passenger flow without sufficient train supply” issue for mainline trains, this study investigates the coordinated optimization of stop patterns and passenger flow allocation for both train types. The goal is to improve the matching between train capacity and passenger demand under existing operational conditions. First, the physical high-speed rail network is simplified to focus on a corridor where mainline and cross-line trains operate concurrently. The impact of different train operation schemes on passenger flow allocation along this corridor is analyzed. Subsequently, a service network is constructed based on train operation segments, and a mixed-integer programming model is developed to jointly optimize the operation schemes of mainline and cross-line trains, minimizing both railway operational costs and passengers’ travel time loss. To enhance computational efficiency for large-scale problems, a heuristic algorithm based on Lagrangian relaxation is designed, which decomposes the original problem into simpler subproblems by relaxing coupling constraints. Finally, the Wuhan-Guangzhou section of the Beijing-Guangzhou high-speed railway is used as a case study. High-quality feasible solutions are obtained within a short computation time, validating the proposed model’s and algorithm’s effectiveness. The research results indicate that compared to the sequential optimization approach, the coordinated optimization scheme reduces the number of train services by one. Analysis of the proportion and seat occupancy rate of cross-line passengers further confirms the improved passenger flow allocation under coordinated optimization. The division of roles between mainline and cross-line trains becomes more distinct, enhancing the match between train capacity and passenger flow under current operating conditions. These findings provide valuable insights for railway operators.

To fully utilize the interconnectivity advantages of the inner loop and connecting lines in high-speed railway hubs, aimed at serving both transfer passengers and urban travelers, this study explores an optimization method for adding extra train paths in mass transit operations with a high-speed railway hub loop. First, the organizational model for Electric Multiple Unit (EMU) trains on the hub loop is analyzed in terms of train scheduling, EMU sourcing, and routing schemes. Second, with the objective of increasing high-speed train operation frequency on the loop line, a comprehensive optimization model is developed for scheduling mass transit-type train paths, factoring in station routes, track utilization, and EMU routing schemes. Finally, a high-speed railway hub is selected as a case study. The research results demonstrate that, without altering original train assignments and using the same number of EMUs as the initial plan, the service frequency between hub loop stations can increase by 120%, and the minimum average departure interval between stations can be reduced to 14 minutes. The established model allows flexible application of various hub loop train scheduling plans, maximizing transportation capacity and advancing mass transit operations. These findings provide an organizational strategy for implementing mass transit-type operations of EMUs in high-speed railway hubs.

To address the requirements of the “Road to Rail” policy and account for the choice behavior of shippers, a bi-level decision model for railway freight subsidies is constructed. The upper-level model incorporates both economic and environmental benefits and establishes a multi-objective nonlinear subsidy decision framework, with the single OD pair railway subsidy amount as the decision variable. The lower-level model considers the game behavior among shippers and develops a freight flow distribution model aimed at minimizing generalized transportation costs. Considering the characteristics of the model, the affiliation function is used to transform the model into a single-objective problem, and a simulated annealing algorithm alongside an iterative weighting method is designed for its solution. The model and algorithm are validated through an empirical analysis of the gravel aggregate freight network in Beijing. The results show that the OD pair subsidy amounts range between 0 and 0.12 yuan/(t·km), exhibiting significant spatial and individual differences. Under the subsidy scheme, 24% of freight shift from road to rail and 17% carbon emission reduction can be achieved with an economic expenditure of 2.6 million yuan. Furthermore, under the differentiated subsidy scheme, the economic expenditure required to achieve the same scale of freight shift and carbon emission reduction is only 52% of that under a uniform subsidy strategy. The weighting coefficients for economic and environmental benefits have a significant impact on subsidy decisions, suggesting that local governments should determine these weighting coefficients based on local characteristics. Increasing the upper limit of the total amount of railway freight subsidies can enhance the effectiveness of modal shift promotion and environmental benefits, although the marginal growth rate of environmental benefits continues to decline over time. A higher railway freight turnover ratio substantially increases financial subsidy pressures, and the adoption of complementary incentives beyond subsidies is recommended. Additionally, there is a clear negative correlation between total railway freight subsidies and highway freight rates, highlighting the need for the government to establish a flexible subsidy adjustment mechanism to accommodate freight market dynamics.

This study investigates the mechanical characteristics of full-section fiber-reinforced concrete sealed structures under different temperature loads. A simulation model of the CRTS Ⅲ slab ballastless track and full-section fiber-reinforced concrete sealed structure is established using finite element software. The accuracy of the model is validated by field data from the Weifang-Yantai high-speed railway. The effects of overall thermal load, temperature gradient load, and temperature cyclic load on the stress and strain cloud maps of full section fiber-reinforced concrete sealed structures are analyzed. Additionally, the variation in stress distribution along the longitudinal and lateral directions under these loads is studied. The results indicate that thermal loads have a significant impact on the full-section waterproof sealed structure of high toughness concrete. Under the overall thermal loads, the central area of the base plate is the primary temperature-sensitive region, while the expansion joint position of the track structure is highly sensitive to temperature changes. The deformation of the track structure due to temperature gradient loads is a major influencing factor on the stress distribution of the full-section fiber-reinforced concrete sealed structure. The edge of the base plate exerts a lateral shear effect on the structural layer, highlighting the need for stringent construction quality control to minimize interlayer contact damage. Strain hysteresis is observed in the full-section fiber-reinforced concrete sealed structure under cyclic temperature loading. The hysteresis loop size indicates that the structural layers at the shoulder and base plate edge are prone to fatigue failure. Under cyclic temperature loads, stress and temperature gradients vary with depth and time, with the temperature gradient being notably larger within 0.02 m of the structural floor, marking this region as an unfavorable area for stress distribution.

This study develops a mechanical model for a complex rotor system supported by long bearings, focusing on a double-disc rotor system with asymmetric nonlinear spring-damping coupling. First, nonlinear forces, including the lubricating oil-film force and the rub impact force due to rotor-stator contact, are calculated. The interaction between the bearings at both ends and the base is modeled as a nonlinear elastic damping coupling. Second, employing a multi-objective, multi-parameter collaborative coupled simulation analysis and the variable step size fourth-order Runge-Kutta method for numerical computation, the study investigates the modal types, distribution areas, and bifurcation characteristics of periodic rub-impact vibrations in a long-bearing double-disc rotor system. Finally, the research reveals the relationship between the system’s dynamical properties and its model parameters. The results show that the rotor system exhibits typical nonsmooth characteristics as the stator stiffness ratio or speed ratio increases. The system frequently experiences the coexistence of multiple cycle types, and its nonlinear vibration characteristics display complexity and diversity when parameters such as stator stiffness ratio, gap threshold, and speed ratio are varied in a coupled manner. As the eccentricity ratio increases, both the maximum rub-impact force and the rub-impact duty cycle within the system rise. Changes in the system’s friction coefficient have a relatively minor impact on the mode types, distribution patterns, maximum rub-impact forces, and duty cycle values of the system’s periodic vibrations. Subharmonic periodic vibrations and chaotic behaviors, among other complex periodic rub-impact vibrations, are observed to persist as the speed ratio increases.

To address the challenges of complex backgrounds, deep placement and difficulty in recognizing and measuring cracks in plate rubber bearings of beam bridges, this study proposes an automatic crack detection and parameter calculation method based on dual-stage semantic segmentation using the YOLOv8-ESF framework. First, the EfficientViT backbone is integrated into the backbone network. Based on this, the Bottleneck structure within the C2f module is reconstructed to form the C2f-Faster-EMA module, which replaces part of the original C2f modules in the YOLOv8n backbone and incorporates decoupled heads, thereby enhancing the model’s ability to capture multi-scale detail features of bearing cracks. Second, the improved YOLOv8n model is employed to segment and extract the entire bearing region. Subsequently, the same model is used to further segment crack regions within the extracted bearing image. Crack parameters are then obtained by extracting the skeleton centerline and searching for the maximum outer rectangle method. Finally, the model is validated and evaluated from three aspects: network architecture, crack dataset, and segmentation accuracy. Experimental results show that YOLOv8-ESF model achieves over 85% accuracy in terms of mPA, DSC, and IoU for both bearing region and crack recognition. In field tests on real bridges, the maximum deviation between the crack parameters calculated via the dual-stage semantic segmentation method and manual measurements is less than 0.1 mm, meeting practical engineering requirements.

To address the complex and dynamic spatiotemporal features in traffic flow prediction, this paper proposes an Attention-based Spatiotemporal UNet with Graph Convolutional Network (AST-UNet-GCN) model for long-term traffic flow forecasting. First, for temporal modeling, a U-Net-based feature pyramid architecture is introduced to capture multi-scale temporal features. The encoder-decoder structure enables effective extraction of features across different time scales. To enhance adaptability to sudden events, a short-term temporal feature extraction module based on attention mechanisms is designed. Furthermore, a global temporal feature extraction module utilizing deformable attention mechanisms is constructed to capture long-term temporal dependencies. Then, a Squeeze-and-Excitation (SE) feature fusion module is developed to improve the expressiveness of the convolutional neural network, enabling dynamic weighting of features at different time scales and enhancing the fusion of multi-scale temporal information. This approach effectively highlights key features while suppressing redundant information. Finally, for spatial modeling, a Graph Convolutional Network (GCN) is employed. By constructing the topological graph structure of the traffic network, the model captures spatial dependencies. A sigmoid-based feature fusion mechanism is further designed to explore the intricate dynamic relationships between spatial and temporal features, enabling comprehensive spatiotemporal modeling. Experimental results demonstrate that compared to other mainstream models, the AST-UNet-GCN model achieved reductions of 8.5% and 9.4% in MAE and RMSE metrics for short-term prediction, while reductions of 10.4% and 6.5% were observed for long-term prediction, respectively. These results demonstrate the model’s strong performance in traffic flow forecasting, particularly in immediate prediction accuracy and the stability of long-term trend forecasting.

To enhance the efficiency of bus-metro connectivity, this study conducts a coordinated optimization of the spatial-temporal distribution of passenger flows and transfer efficiency for feeder bus routes serving metro station clusters. A multi-objective optimization model is proposed, aiming to minimize the total system cost while maximizing network transfer demand. A penalty mechanism for both transfer time and number of transfers is incorporated to constrain cases involving two or more transfers, encouraging the system to reduce unnecessary transfers during route design. To solve the model, a hybrid genetic particle swarm optimization algorithm is developed, integrating an adaptive elite retention strategy and a dynamic inertia weight adjustment mechanism. Results indicate that, compared with the original bus network, the optimized system increases bus carrying capacity by approximately 23%, reduces average passenger travel cost by about 9%, and improves algorithm efficiency with a 15.4% faster runtime compared to conventional genetic algorithms. The proposed model demonstrates superior performance across multiple key indicators, including transfer appeal and travel cost, thereby validating its effectiveness in improving the operational efficiency and service quality of feeder bus networks. This work offers valuable insights for the refined management and intelligent upgrading of urban public transportation systems.

To explore the relationship between community identification and the carbon reductionbenefits of shared bicycles, this study examines the identification of carbon reduction benefit communities and their influencing factors in Xi’an, China. First, based on the Hello Bike order data in Xi’an in 2020, the spatiotemporal distribution characteristics of shared bicycle trips are analyzed. Second, the carbon reduction benefits of shared cycling are quantified, and their temporal variation characteristics are analyzed. Then, the Louvain algorithm is employed to identify communities in the central urban area of Xi’an based on carbon reduction benefits, followed by classification using the K-means clustering algorithm. Finally, the Gradient Boosting Decision Tree (GBDT) model is applied to explore the impact of the built environment on carbon reduction benefits. The results indicate that shared bicycle usage exhibits distinct morning and evening peak periods, with hotspots concentrated along subway lines and subway transfer stations. Shared bicycles significantly contribute to carbon reduction, with evident peak-hour effects. A total of 16 communities are identified based on carbon reduction benefits, with minimal overlap between these active communities and administrative divisions. The identified communities exhibit a “low-coupling, high-cohesion” structural pattern, where the city center contains a greater number of smaller communities, while peripheral areas have fewer but larger communities. Central communities demonstrate more significant carbon reduction benefits. Based on the weighted average degree, graph density, and average clustering coefficient of the community, the 16 communities are categorized into three categories: low, medium, and high carbon reduction areas. All built environmental factors positively influence carbon reduction benefits, though to varying extents. The findings provide valuable insights for the management and policy formulation of shared bicycle carbon reduction initiatives in Xi’an.

To address the high cost, long duration, and low efficiency associated with identifying mud pumping defects, this study proposes a novel identification method that integrates Grey Wolf Optimization (GWO) with Variational Mode Decomposition (VMD). First, statistical analysis of mud pumping occurrences is conducted to determine the typical length of affected sections in ballastless tracks. Then, based on track irregularity data collected by dynamic inspection vehicles, the performance of Empirical Mode Decomposition (EMD), Wavelet Decomposition (WD), and VMD in decomposing irregularity signals in mud-pumping areas is compared. Additionally, the effectiveness of several intelligent optimization algorithms, including Sparrow Search Algorithm (SSA), Artificial Bee Colony (ABC), Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and GWO, in adaptively selecting the key parameters k and α of VMD is evaluated. The proposed GWO-VMD method is employed to decompose the measured track irregularity data, and the characteristics of the resulting Intrinsic Mode Functions (IMFs) are analyzed. The kurtosis values of the IMFs are used as feature vectors, and a maximum likelihood function based on envelope spectrum entropy is calculated to determine the defect identification threshold, which is found to be 3.51. Finally, the effectiveness of the GWO-VMD model is validated through a case study. The results indicate that each IMF component derived from GWO-VMD decomposition of track irregularity data exhibits distinct frequency and amplitude characteristics, corresponding to different spatial scale information. Compared with on-site defect data, the GWO-VMD method achieves an identification accuracy exceeding 90%, enabling effective localization and detection of mud pumping defects in ballastless tracks. The findings support refined service condition management of ballastless tracks and provide technical guidance for “condition-based maintenance” of high-speed railway lines.

The video surveillance system is a crucial euqipment in ensuring the safety of railway passenger stations. Existing studies on optimizing video surveillance layouts at railway passenger stations often overlooks the specific monitoring requirements of these stations, leading to potential inefficiencies in the collected image data. To address this issue, this study first analyzes the video surveillance coverage requirements of different areas within the stations, focusing on coverage clarity and orientation requirements. Subsequently, the impact of these coverage needs on the effective range of surveillance cameras is examined. Based on this, a 0-1 integer programming model is developed to optimize video surveillance layout, aiming to minimize the total deployment cost while ensuring effective coverage of target areas. This model is solved using GUROBI software and validated through a case study at Suzhou Railway Station. The results of the study indicate that, after a thorough analysis of the surveillance requirements in railway passenger stations, the proposed layout achieves a 129.3% improvement in overall effective coverage compared to layouts that do not consider these requirements. Specifically, areas requiring target discovery and identification see coverage improvements of 86.5% and 148.1%, respectively, with full coverage achieved in areas necessitating target confirmation. These results provide valuable insights for planning and formulating video surveillance layouts in railway passenger stations.

With the continuous increase in train operating speeds, higher demands are placed on operational safety. This study conducts an in-depth investigation into the operational response of high-speed trains traveling at 350 km/h and above under the influence of crosswinds to solve the safety issues. First, based on fluid dynamics and vehicle dynamics theories, computational fluid dynamics CFD software and UM multibody dynamics software are respectively employed to establish aerodynamic and dynamic models of the high-speed train. Second, aerodynamic loads are applied as external excitations to the train body to calculate the full process of train operation on straight tracks under crosswind conditions. Finally, the influence of steady-state wind load patterns and wind speed on the operational safety of high-speed trains are comprehensively analyzed. The results indicate that under crosswind conditions, the lateral displacement of car body, lateral wheelset forces, vertical wheel-rail forces, derailment coefficients, and wheel load reduction rates increase significantly compared to no-wind scenarios. When the wind speed is 5 m/s and the train speed ranges from 350 to 400 km/h, safety indicators remain relatively low, with gradual increases that stay within safe limits. When wind speeds reach or exceed 10 m/s and train speeds range from 350 to 420 km/h, rail compression by the wheels occur, and there is significant wheel-rail lateral interactions, accompanied by transient separation on the windward wheel-rail interface. These effects are especially pronounced at speeds above 400 km/h. These findings provide important references for ensuring the operational safety of high-speed trains under crosswind conditions.

In addressing the impact of various uncertain factors, including transport time and freight volume, on multimodal transport path selection, a fuzzy robust regret model is proposed. This model integrates fuzzy opportunity constraint programming and robust optimization to comprehensively manage uncertain variables. First, triangular fuzzy numbers are used to represent the uncertainty in transport time parameters, while the robust optimization scenario method is applied to handle fluctuations in freight volume. Hybrid time window constraints are introduced, and fuzzy time parameters are clarified using fuzzy opportunity constraint programming. The model optimizes total cost, total carbon emissions, and total time as its objectives. Subsequently, to improve the convergence quality of the algorithm and maintain population diversity, an improved adaptive Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) is designed with local optimization strategies. The algorithm’s performance is compared across four practical multimodal transport scenarios with different node sizes. Finally, a complex virtual example is used to validate the model and analyze the robustness of the solution in response to uncertainty. The results demonstrate that, in comparison with the conventional NSGA-Ⅱ, the improved daptive NSGA-Ⅱ exhibits superior performance in the two objectives of total cost and total carbon emission as node size increases. When the node network reaches 30, the adaptability of the two objectives experiences a decline by 25.22% and 26.39%, respectively. The robust solution obtained from the model can effectively adapt to an uncertain transportation environment and changes in the decision maker’s preferences. Multimodal transportation decision makers need to comprehensively consider the impact of uncertain factors, select appropriate regret values and confidence levels for fuzzy parameters, and achieve transportation solutions that align with their preferences.