To address the low comparability in carbon emission estimates between High-Speed Railway (HSR) and Civil Aviation (CA), this study first establishes a carbon emission factor calculation framework and mathematical model for these two passenger transport modes, considering both power generation and oil refining processes. Then, using actual parameters of HSR and CA in China, the carbon emission factors of both modes are calculated and compared with those of several developed Western countries. The analysis of influencing factors on carbon emission factors for both modes is then conducted, and key directions for reducing carbon emissions in HSR and CA are proposed. The results show that when accounting for power generation and oil refining processes, the carbon emission factor of CA is approximately 3.6 to 3.9 times that of HSR. China’s HSR carbon emission factor is higher than those of France, Japan, and Germany, due to its power generation structure. The maintenance and updating of infrastructure such as tracks, bridges, subgrades, tunnels, electrification systems, stations, and trains contribute to a roughly 16% increase in the carbon emission factor of HSR.The CA carbon emission factor in China is lower than that of the United States, the United Kingdom, and Japan, due to higher load factors and longer average travel distances. Oil refining and airport-related emissions account for approximately 9.4% and 4.6%, respectively, of the total carbon emissions of CA. In the future, reducing carbon emissions for HSR in China will focus on improving the power generation structure, while for CA, the focus will be on advancements in aviation fuel technology.

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 address the challenge of ineffectively integrating target image features in current multimodal 3D object detection methods, this study proposes a multimodal 3D object detection method, NNC-EPNet, by introducing a Nearest Neighbor Correction (NNC) method to mitigate the impact of sparse target point cloud and non-targeted point clouds. First, the NNC module is designed by using the enhanced features of neighboring point clouds to refine the sampled point clouds. This process reduces noise in point cloud data and strengthens the features of target point clouds, facilitating better integration of target image features. Second, a Multi-Modal Fusion Transformer (MFT) encoder is developed, which uses cross-attention mechanisms to fuse image and point cloud features and introduces a point cloud attention mechanism to aggregate global contextual information, thereby enhancing feature representation capabilities. Finally, comparative experiments are conducted on the standard autonomous driving datasets, namely KITTI and Waymo. Experimental results show that NNC-EPNet achieves an average detection accuracy of 84.47% on the KITTI dataset, with improvements of 2.00%, 3.25%, and 5.68% in the easy, moderate, and hard scenarios, compared to the baseline algorithm. On the Waymo dataset, it achieves a weighted average accuracy of 74.48%, with improvements of 2.49% compared to the baseline algorithm. These results prove that the two designed modules, NNC and MFT, can effectively improve the 3D object detection performance.

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.

To align with the current trend of integrating suburban railways and urban rail transit lines, this study proposes a mathematical model to optimize the train operation plan based on passenger flow distribution. Specifically, suburban railways operate express/slow trains, while urban rail transit lines run all-stop trains, with a connection at terminal station. The model is formulated as a bi-objective nonlinear integer programming problem, aiming to minimize passenger travel costs and enterprise operational costs, under constraints such as passenger demand and line capacity. A genetic algorithm is designed to find near-optimal solutions. The model’s effectiveness is validated using data from Chongqing Metro Line 5 and the Jiangtiao Line, comparing different operation modes-interchange, integrated operation, and express/slow trains-based on travel time for mainline and cross-line passengers, and train stopping and operational costs. The findings indicate that integrated operation with express/slow trains reduces passenger travel time by 2.92% and enterprise operational costs by 4.88%, compared to the interchange mode. This approach is particularly suitable for routes with high cross-line travel demand, facilitating rapid transit from suburban areas to city centers. The proposed model offers theoretical support for the efficient integration of suburban railways and urban rail transit, enhancing transportation efficiency and passenger convenience.

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.

Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) networks can provide high-quality computational services to ground User Equipment (UE), but real-time trajectory design for multiple UAVs remains a significant challenge. To address this issue, a trajectory design algorithm based on multi-agent deep reinforcement learning is proposed, utilizing the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework to collaboratively design UAV trajectories. Considering the limited battery capacity of UAVs, a critical constraint on UAV network performance, the optimization problem is formulated to improve the sum of UAV energy efficiencies. This involves jointly optimizing the trajectories of UAV clusters and the offloading decisions of UEs. Each agent interacts with the edge computing network environment, observes its local state, and determines trajectory coordinates via an Actor network. The Critic network is trained by incorporating the action and observation of other agents, thereby refining the trajectory policy generated by the Actor network. Simulation results demonstrate that the MADDPG-based trajectory design algorithm exhibits excellent convergence and robustness, significantly enhancing UAV energy efficiency. Specifically, the proposed algorithm outperforms the random flight algorithm by 120% at most, the circular flight algorithm by 20% at most, and the Deep Deterministic Policy Gradient (DDPG) algorithm by 5% to 10%.

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.

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.

Online monitoring technology for railway foreign object intrusion plays a critical role in ensuring the safety of railway operations and the security of passengers’ lives and property. To address the issue of incomplete and inaccurate detection of occluded targets and small targets in existing foreign ob-ject detection algorithms, this paper introduces a railway foreign object detection algorithm, Vanilla-YOLOv8, based on YOLOv8. First, leveraging VanillaNet’s approach of reducing network depth, shortcut branches, and enhancing nonlinear capabilities through deep training strategy modifications and dynamic adjustment of activation function states, the proposed method mitigates problems like model degradation, time inefficiency, and the disappearance of low-level small target features caused by excessive network layers and shortcut branches. This enhances the model’s feature extraction capability and detection speed. Then, improved partial convolution is employed to reduce redundant features, ensuring optimal utilization of extracted features. Finally, a Squeeze-and-Excitation (SE) attention mechanism is integrated into the network backbone to increase the weight of key features, enhancing feature representation and detection capabilities for occluded and small targets. Experimental results show that the Vanilla-YOLOv8 algorithm achieves the mean average precision of 98.7%, reduces parameters by 61.39%, and reaches the recognition speed of 125 Frames Per Second (FPS). These improvements mark a substantial advancement over traditional image processing techniques in terms of speed and detection accuracy, offering a valuable reference for real-time online monitoring.

Addressing the issues of decreased traffic flow stability, increased traffic safety risks, and reduced operational efficiency caused by mixed passenger and freight traffic, this paper constructs a multi-lane mixed passenger and freight traffic flow cell transmission model to study the characteristics of highway mixed traffic under different truck penetration rates from a macroscopic perspective. Initially, utilizing microscopic car-following models, the fundamental diagram characteristics of homogeneous passenger car and truck traffic flows are analyzed, and a fundamental diagram model of mixed traffic flow under different truck penetration rates is proposed. Subsequently, specific parameters are employed to characterize the effects of lane-changing and limited acceleration on the reduction of road traffic capacity, depicting the impact of trucks on straight and lane-changing traffic flow in mixed traffic. The Logit model is used to calculate lane-changing traffic volume under the influence of multiple factors, thus constructing a cell transmission model of multi-lane mixed passenger and freight traffic flow. Finally, the model’s effectiveness is verified through multi-scenario highway simulation experiments. The research results show that the proposed model can accurately simulate the evolution of mixed traffic flow on highways; truck penetration rate is a critical factor affecting mixed traffic operations, and when traffic incidents reduce the number of lanes, a truck penetration rate exceeding 0.6 leads to persistent traffic congestion. The study provides analytical tools for researching mixed passenger and freight traffic flow characteristics and offers theoretical methods for traffic management and guidance strategy formulation under mixed traffic conditions.

To address the need for forecasting future trends in airline ticket prices in highly competitive markets, this paper proposes a CNN-LSTM hybrid model that integrates Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). In the data construction and input phase, a channel data structure is developed to represent ticket prices, emphasizing the competitive relationships among airlines. Furthermore, various factors influencing ticket price fluctuations are considered, leading to the creation of independent channel data structures to represent airline attributes, flight attributes, and date attributes. These channel data are then integrated into a multi-channel data input format suitable for CNNs. In the modeling phase, a one-dimensional Convolutional Neural Network (1D-CNN) is utilized to extract features from the multi-channel input data, while the LSTM captures temporal dependencies within the data to predict future ticket prices for different flights on a given route. The proposed CNN-LSTM model is compared against several baseline models, and ablation experiments are conducted to validate the importance of the selected influencing factors. Experimental results demonstrate that the CNN-LSTM model achieves significant improvements in prediction performance. Compared with Random Forest, Support Vector Machine, standalone CNN, standalone LSTM, and Vector Autoregression model, the Mean Absolute Error is reduced by 18.74% to 57.02%, and the Mean Absolute Percentage Error is reduced by 9.31% to 22.16%. Furthermore, the ablation experiments confirm that incorporating these influencing factors enhances the model’s overall performance. The findings of this study not only provide decision-making support for airlines in ticket pricing and adjustment strategies but also introduce novel methodologies and perspectives for research in airline ticket price prediction.

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 address the limitations of single sensors, such as the inability of LiDAR to capture true color information and the low accuracy of image-based 3D reconstruction point clouds, this paper proposes a method for 3D track reconstruction by integrating LiDAR point cloud data and image data. First, real-time laser point cloud mapping of the track is achieved using LiDAR Inertial Odometry via Smoothing and Mapping (LIO-SAM), a tightly coupled radar-inertial odometry method. Next, the Scale Invariant Feature Transform (SIFT) algorithm is employed to extract feature points from multiple images, and the geometric relationships between multi-view images are determined by matching corresponding feature points. Structure From Motion (SFM) and Multi-View Stereo (MVS) algorithms are then applied to locate, cluster, and generate dense image-based point clouds enriched with texture and color information of the track. Finally, plane features of the track slabs and linear features of the rails are used as reference features, and the Iterative Closest Point (ICP) algorithm is employed to merge and register the image point cloud with the LiDAR point cloud. By using the spatial position information of the LiDAR point cloud as a baseline and fusing it with the texture and color information from the image point cloud, an accurate and realistic 3D track model is obtained. Experimental results demonstrate that, compared with traditional registration methods, the improved algorithm achieves an 83.4% and 85.9% enhancement in shape parameter accuracy and nearest-neighbor point distribution, respectively. When performing target recognition on the track point cloud, the fused point cloud improves overall accuracy by 7.7% over the original point cloud and performs better on metrics such as average precision and mean intersection-over-union. Furthermore, the comparison between the calculated track gauge and height difference from the fused point cloud and the measured data reveals an error margin within 3 mm, verifying the effectiveness of the proposed 3D track point cloud reconstruction method.

To address the issue of low fault diagnosis accuracy in existing compensation capacitor fault diagnosis methods for track circuits under high noise interference in complex environments, an intelligent fault diagnosis algorithm based on transfer learning, Continuous Wavelet Transform (CWT), and Time-Frequency Enhanced Residual Network (TFEResNet) is proposed. First, CWT is employed to integrate the time-domain and frequency-domain information of the original induced voltage signal, generating a wavelet time-frequency map. This map effectively enhances the model’s ability to capture fault characteristics by mapping compensation capacitor fault features to local positions at different times and scales. The wavelet time-frequency map is then input into the constructed TFEResNet model for transfer learning training, which is used for feature extraction and fault classification. TFEResNet can extract complex time-frequency features from the map, mitigating the adverse effects of redundant and irrelevant noise in the signal, thereby improving diagnosis accuracy and generalization capability of the model. Experimental results show that, in high-noise environments, the proposed algorithm outperforms other methods in compensation capacitor fault diagnosis, achieving an accuracy of 99.28%. Additionally, it shows superior performance in precision, recall, and F1-score, demonstrating the effectiveness of the method and providing a novel approach for data-driven compensation capacitor fault diagnosis in track circuits.

The current challenges in establishing motor noise and vibration analysis models include the complex structure of insulating paint and winding conductors, as well as the difficulty in determining material parameters. To address these issues, a high-precision equivalent modeling method combined with genetic algorithm optimization for fast correction of material parameters is proposed. A response surface model based on Central Composite Design (CCD) is used to analyze the influence of anisotropic material parameters on modal frequencies. The vibration distribution characteristics of the motor’s shell and rear end cover under rated working conditions are analyzed through multi-physics field coupled simulations. The accuracy of the multi-physics field analysis model is validated through vibration bench tests, and the mechanism for generating the peak value of the 48th-order equivalent radiated power level near 8 000 r/min is explored. The results show that among the anisotropic materials, the elastic modulus of the insulating material contributes the most to the motor’s modal behavior. The relative error between the corrected finite element modal frequencies for the motor and the modal test results using the hammering method for the first three orders of the motor is within 3.5%. The equivalent modeling and correction method can be applied to construct multi-physics field coupled models for motors. Overall, the multi-physics simulation results show good consistency with the experimental results. The 48th-order overall vibration level is the highest, with the peak primarily caused by the resonance between the 0th-order 12x frequency excitation of the radial electromagnetic force and the 0th-order modal frequency (6 239 Hz) of the stator assembly at a rotational speed of 8 000 r/min. This study can provide a reference for investigating the motor’s noise and vibration distribution characteristics and generation mechanisms.

This study investigates the factors influencing passenger mode choice behavior in urban agglomerations, addressing the unclear mechanisms of such behavior. A Revealed Preference (RP) survey is conducted on travel behavior within the Guanzhong urban agglomeration using a questionnaire, and the data’s validity and reliability are tested. A travel mode selection prediction model considering travel preferences is constructed using the Gradient Boosting Decision Tree (GBDT) model, based on the survey data. The interpretable machine learning framework, SHapley Additive exPlanations (SHAP), is employed to analyze the prediction results. The constructed model considers actual passenger decision-making logic, deconstructing multiple travel mode choices into multiple binary choices to enhance model interpretability. The results indicate that the GBDT prediction model considering travel preferences effectively predicts travel mode choice behavior in urban agglomerations. Passenger preferences for convenience, safety, speed and comfort, as well as car ownership home location and travel distance, significantly influence the binary choice behavior between private cars and public transportation. The choices between high-speed buses and railway systems, as well as between ordinary railways and high-speed trains, are significantly influenced by travel distance and passenger preferences for comfort, safety, speed, and punctuality. Car ownership and household location significantly affect the choice between high-speed buses and railway systems, while income status significantly influences the choice between ordinary railways and high-speed trains. The findings can provide reference for decision-makers to make practical and effective decisions from the perspective of passenger travel preferences.

Railway infrastructure is continuously impacted by vehicle loads and external environmental factors, causing issues such as the loss, displacement, and damage of track fasteners along railway lines. These problems pose significant threats to the safe operation of railways. To address the low detection efficiency, high omission rates, and lack of real-time detection capabilities on edge devices associated with traditional manual visual inspections and subjective sampling methods, this paper proposes a lightweight detection model for track fastener status, FTEL-YOLO, based on YOLOv8s. The model is designed to enhance detection accuracy and real-time performance. First, the C2f-Faster module, inspired by the FasterNet-Block concept, is introduced to reduce the model’s parameters. Second, to mitigate the decline in detection accuracy caused by network lightweighting, a Triplet Attention Mechanism is incorporated after the Spatial Pyramid Pooling Fast (SPPF) layer, and EIoU is utilized as the bounding box regression loss function, enhancing the model’s feature extraction capability for track fastener conditions in complex backgrounds. Finally, Layer-Adaptive Magnitude-based Pruning (LAMP) is applied to the improved model to further compress it, reducing redundancy and enhancing its deployment capability on edge devices. Experimental results demonstrate that the improved FTEL-YOLO model achieves a minimal detection accuracy loss of 0.3%, while the computation, parameters, and model size are reduced by 63.1%, 65.6%, and 66.2%, respectively, achieving lightweight design without compromising accuracy.

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.

Railway track obstructions pose potential threats to train operation safety, with severe incidents possibly leading to derailments, overturns, and casualties. To address the challenge of achieving real-time detection on edge devices, where existing railway intrusion detection models often fail, this paper proposes an improved railway obstruction detection algorithm based on FasterNet and YOLOv8s. First, FasterNet, a network with fewer parameters, replaces the CSPDarkNet53 backbone of YOLOv8s for feature extraction, reducing both the parameters and computational complexity. Second, inspired by partial convolution in FasterNet, a FasterBlock module is introduced to replace the C2f module in YOLOv8s’ neck, enabling multi-scale feature fusion and further decreasing model parameters. Finally, to mitigate accuracy loss caused by model lightweighting, a redesigned BiFPN-A feature fusion structure is proposed. In this structure, Fusion operations replace Concat for tensor concatenation, achieving feature map fusion via FasterBlock and Fusion. Additionally, a parameter-free attention mechanism SimAM is integrated before each FasterBlock, ensuring that the lightweight model maintains robust detection accuracy. The results demonstrate that the improved model achieves a 60.89% reduction in size, a 61.8% decrease in parameters, and a 45.1% reduction in computational complexity, with only a 0.2% loss in detection accuracy.

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 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.

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.

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.

In the industrial field, bearing fault signals are often subject to significant interference from strong background noise due to the harsh operating environment and complex working conditions of mechanical equipment, making it challenging to effectively extract fault characteristics. To address this, this paper proposes a fault diagnosis method for rolling bearing based on Adaptive γ-order Cyclostationary Blind Deconvolution (ACYCBD _γ ). First, a novel metric, the Local peak ratio (Lpr), is introduced to determine the optimal filter length. Then, the estimated fractional order based on a Gaussian smooth model is calculated to construct the fractional-order cyclostationary blind deconvolution. Finally, the proposed model’s performance is validated using both public and real-world datasets. The results demonstrate that ACYCBD _γ achieves suppression ratios that are 20.61%, 17.85%, and 44.95% higher than those of Minimum Entropy Deconvolution, Maximum Correlated Kurtosis Deconvolution, and Maximum Second-order Cyclostationarity Blind Deconvolution (CYCBD), respectively, on the public dataset. On the real dataset, the suppression ratios are improved by 53.63%, 60.27%, and 55.16%, respectively. Under signal-to-noise ratios of -10 to -20 dB, ACYCBD _γ enhances the Lpr by 87.51% compared to CYCBD. Therefore, ACYCBD _γ effectively reduces the impact of noise and interference, enabling the accurate extraction of bearing fault features in strong noise environments.

To address the challenges of insufficient overall brightness, edge indistinctness, and color distortion in low-light images, this study proposes a low-light image enhancement method based on a multi-scale self-guided Sharpening-Smoothing Image Filter (SSIF) within the HSV color space. First, leveraging the color-luminance separation property of the HSV space, the multi-scale self-guided SSIF is applied to the V component to accurately estimate the illumination component and subsequently extract a precise reflection component. Second, to mitigate the issue of uneven illumination distribution, a two-dimensional adaptive gamma transform algorithm is proposed. Optimal parameters are determined through extensive comparisons, allowing the algorithm to enhance the brightness of darker regions while suppressing the brightness of lighter regions. This results in more uniform image illumination and brightness that aligns with human visual perception. Third, to address edge blurring and noise in the reflection component, a multi-scale passivation masking algorithm is developed, effectively enhancing image details while suppressing noise and improving the overall dynamic range of the image.Finally, an adaptive saturation enhancement algorithm is applied to the S component. The enhanced S and V components are then merged with the unchanged H component and transformed into the RGB image. This output is further refined by incorporating the color restoration factor from the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm, producing the final enhanced image. Experimental results show that the proposed algorithm achieves improvements of 14.62% and 32.10% in noval blind image quality assessment based on fine natural scene statistics and average gradient, respectively, compared to other algorithms. The proposed method not only effectively addresses uneven brightness distribution but also enhances image contour details and contrast, outperforming existing approaches.

To address the challenges of obtaining demand for air-rail passenger flows within urban agglomerations and understanding passenger flow patterns, a comprehensive analysis framework integrating air-railway passenger flow identification and prediction modules is proposed. Firstly, considering the spatial constraints of transportation hubs and the spatiotemporal characteristics of travel, a method for identifying intermodal passenger flows using signaling data is introduced, accompanied by an analysis of distribution patterns. Subsequently, leveraging the Bidirectional Gated Recurrent Unit (BiGRU), time period coding is integrated to construct a Temporal-Bidirectional Gated Recurrent Unit (T-BiGRU) for passenger flow prediction. Finally, the framework is validated using the Beijing-Tianjin-Hebei urban agglomeration as a case study. Results indicate that the intermodal passenger flow in the Beijing-Tianjin-Hebei urban agglomeration exhibits a clustered distribution, with the scenarios of Beijing South Railway Station-Tianjin Railway Station-Tianjin Binhai Airport and Beijing West Railway Station-Zhengding Airport Station-Shijiazhuang Zhengding Airport having the highest proportion, exceeding 65% of the total intermodal passenger flow. The T-BiGRU model accurately predicts the demand for air-rail intermodal passenger flows. The bidirectional passenger flow prediction accuracy for the two main scenarios exceeds 89%, surpassing multiple baseline models. These findings offer support for the coordinated development of air-rail transportation and the optimization of air-rail intermodal services in urban agglomerations.

Low-frequency voltage oscillations in high-speed railway EMU-traction network coupling systems frequently occur when multiple EMUs start simultaneously under light-load conditions. These oscillations can easily lead to traction blockades, significantly jeopardizing the safe operation of high-speed railways. Addressing this low-frequency oscillation issue, this study focuses on the CRH3 EMU to investigate the mechanism and suppression methods of low-frequency oscillations. First, an impedance model of the EMU-traction network coupling system is developed, and the generation mechanism of low-frequency oscillation is analyzed using impedance ratio Bode diagram. Second, a shunting model PI controller is designed, and its performance is compared through simulations with that of traditional PI controllers, Active Disturbance Rejection Control (ADRC) and Self-Adaptive Auto Disturbance Rejection PI (SAADR-PI) controller. Then, a transient direct current control strategy based on the shunting model PI control is proposed, and simulations are conducted to evaluate its suppression effectiveness on low-frequency oscillations. Finally, a comparative experiment between the shunting model PI control strategy and the traditional PI control strategy is carried out on a low-power experimental platform. The results show that the DC-side voltage overshoot of the four-quadrant converter in the EMU equipped with the shunting model PI controller is 17.13%, with a regulation time of 0.104 s and a voltage fluctuation of ±54 V, indicating enhanced dynamic performance of the four-quadrant converter. When six EMUs using the shunting model PI control strategy start simultaneously under light-load conditions, the system stabilizes in approximately 0.4 s, with a DC-side voltage regulation time of 0.27 s, an overshoot of 7.12%, and a stabilized voltage fluctuation of ±32 V. These findings confirm that the shunting model PI control strategy effectively suppresses low-frequency oscillations, providing valuable insights for further research on the suppression of such oscillations in high-speed railway EMU-traction network coupling systems.

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.

To address the challenges of time-consuming processes, low efficiency, and inaccurate results in Electric Vehicle (EV) charging load prediction, this study proposes a prediction method combining variable bandwidth kernel estimation with Convolutional Neural Network (CNN)-based time series prediction. First, the charging and driving data of large-scale EVs are collected by analyzing their charging behaviors and driving habits. Using extensive real-time data, the study conducts an in-depth analysis of multiple factors influencing large-scale EV charging load and constructs a unit mileage energy consumption model based on these influencing factors and actual road conditions. Next, to improve data fitting accuracy, three traditional probabilistic models are introduced, and their advantages, disadvantages as well as fitting accuracy are analyzed and compared. Finally, based on the fitting results, the variable bandwidth kernel estimation model with the highest fitting accuracy is used to fit the EV charging load. The fitted results are then combined with a CNN to predict EV charging load. The results demonstrate that the proposed method reduces the average prediction error of EV charging load to 3.11% and the maximum error to 6.42%, which significantly improves the prediction accuracy,providing reference and guidance for the maintenance of power grid systems.

In the laboratory testing of Automatic Train Protection(ATP) onboard equipment, the large volume and high complexity of test cases, combined with numerous specialized terms in the train control domain, pose significant challenges for existing methods and models. These approaches often lack domain-specific knowledge, making it difficult to accurately interpret contextual information and automatically generate detailed structured representations. To address these challenges, this paper proposes an event extraction method for test cases based on the Rail Bidirectional Encoder Representations from Transformers (RailBERT) model. First, a corpus of specialized terms in the train control domain is expanded and constructed using a neologism mining algorithm. A RailBERT model tailored to the train control system domain is then pre-trained with a Railway Whole Word Masking (RWWM) task to improve its understanding of domain-specific contexts. Then, an event extraction approach is developed to automatically extract the expected outcomes of onboard ATP test cases. The predefined event types and event theory elements are used to achieve comprehensive parsing and characterization of the expected results. Finally, the RailBERT is integrated with Bidirectional Long Short-Term Memory(BiLSTM) and Conditional Random Field (CRF) to enhance its ability to capture dependencies between sequence information and labels, thereby enabling more effective event extraction from test cases. The experimental results show that the proposed model achieves an F1 score of 90.3% on the test case event extraction dataset. This model accurately extracts predefined events from test cases and generates structured representations of the expected outcomes, providing a foundation for the implementation of automated testing.

To enhance the operational reliability of power converters and accurately assess the thermal dissipation state of air-cooling systems, this study proposes a novel thermal modeling method for air-cooled heat sinks. First, a three-dimensional lumped-parameter thermal model for air-cooled heat sinks is developed based on fluid dynamics and heat transfer theories, taking into account the effects of air temperature variations and inlet blockage. Second, the power module losses are calculated through simulation, and Computational Fluid Dynamics (CFD) software is employed to simulate the heat sink temperatures under various blockage levels, thereby validating the three-dimensional thermal network model. Finally, an experimental platform is established to compare the measured heat sink temperatures with those obtained from the two-dimensional and three-dimensional thermal network models. The results indicate that the proposed three-dimensional thermal network model achieves an average calculation error of 1.8%, which is 5.1% lower than that of the traditional two-dimensional model. This approach provides a robust reference for evaluating the thermal state of cooling systems.

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.

In Vehicle to Everything-Device to Device (V2X-D2D) communication scenarios, the rapid time-variation of channels often prevent the Base Station (BS) from acquiring perfect Channel State Information (CSI). To address the limitations that the existing spectrum allocation schemes are not applicable to V2X-D2D scenarios, considering constraints such as Vehicle-to-Vehicle (V2V) link reliability, maximum transmission power, and spectrum reuse, a scenario model and communication model for V2X scenarios are established. This study specifies the optimization objective of maximizing the ergodic capacity of Vehicle-to-Infrastructure (V2I) links while ensuring V2V link reliability. Closed-form expressions are derived for the outage probability of V2V links and the ergodic capacity of V2I links, accounting for the effects of channel time-variations. For spectrum allocation in one-to-one mode, a fast spectrum allocation scheme based on the improved Hungarian algorithm is proposed. For one-to-many mode, a spectrum allocation scheme based on graph coloring and preference lists is developed. Simulation results show that the improved Hungarian algorithm-based scheme achieves higher access rates and lower complexity compared to existing algorithms, while the graph coloring-preference list scheme offers superior access rates and spectrum utilization.

Addressing the traffic oscillation issues triggered by the integration of Connected and Automated Vehicles (CAV) into traffic flows, this study focuses on heterogeneous traffic flows with varying CAV market penetration rates. It introduces CAV platoon size and intensity to classify heterogeneous fleet compositions and simulate traffic oscillation phenomena. Space-time trajectory maps and visualizations of acceleration/deceleration wave propagation velocity are employed to illustrate the evolution of traffic oscillation. Additionally, the study measures traffic oscillation periods using acceleration and deceleration durations, and quantifies oscillation amplitudes using the standard deviation of speeds. Through interactive experiments considering CAV platoon size, intensity, penetration rate, and lead vehicle speed change modes, the study aims to explore the impact of CAV platoon characteristics and lead vehicle speed change modes on traffic oscillations. The results indicate that increasing CAV platoon size, intensity, and penetration rate can positively influence the reduction of oscillation cycles. Oscillation amplitudes tend to increase with platoon size and decrease with platoon intensity. Furthermore, oscillation amplitudes initially increase and then decrease with CAV penetration rate, peaking at a penetration rate of 0.5~0.6. The period and amplitude of traffic oscillations are found to be smallest in the headway fast deceleration-fast acceleration mode and largest in the headway slow deceleration-slow acceleration mode.

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.

This study addresses the issues related to the optimization of transfer flows between high-speed rail hubs and urban rail transit, focusing on Nanjing South Railway Station as the case study. A full-process simulation model is developed to identify transfer bottlenecks, using the Gradient Boosting Decision Tree (GBDT) to determine parameter importance and propose improvement measures. Firstly, the pedestrian and facility flows of the transfer process are decomposed to analyze the distribution characteristics of passenger flows from high-speed rail to urban rail transit. Secondly, AnyLogic is employed to create a full-process simulation model of the transfer at high-speed rail hubs, analyzing the current simulation results to identify spatial bottlenecks. Then various optimization measures are designed under different types, adjusting parameters to formulate combination schemes. The GBDT algorithm is used to ascertain the relative importance of different measures and parameters, establishing the priority of improvement measures. Finally, based on this prioritization, different types of optimization combination schemes are determined, with machine learning interpretability methods applied to analyze their effectiveness, providing recommendations for service enhancements across different scenarios. Results show that transfer bottlenecks are primarily located at stair/escalator passageways and service facilities such as gates and ticket machines. The proportion of urban rail ticket purchases significantly influences both average transfer time and the maximum number of transfers per unit time. For average transfer time, the number of automatic ticket machines, ticket purchase time, urban rail entry service time, and the number of entry gates have a relatively large impact. For maximum transfers per unit time, ticket purchase time and entry gate service time are most influential. To improve transfer efficiency at high-speed railway hubs, it is recommended to promote electronic tickets and various payment methods, and optimize ticketing and checking facilities.

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.

This study investigates methods for analyzing network nodes to address the challenge of identifying key nodes in intercity rail transit networks. A key node identification method, termed \mathrm{K} {\mathrm{s}}^{+}, is developed. This method integrates the K-shell decomposition method with the influence of neighboring nodes and both dynamic and static network indicators. The model considers static physical indicators such as node degree and shortest path, as well as dynamic operational indicators like hub passenger flow and operational intensity, to compute a comprehensive evaluation value for each node. The {k_{\mathrm{s}}}^{+}value of nodes, indicating their influence within the network, is determined by assessing the global core position through the K-shell decomposition algorithm and evaluating local importance with the influence of neighboring nodes. The effectiveness of this algorithm is demonstrated using the Susceptible-Infectious-Recvered （SIR） model and data from the Yangtze River Delta rail network. The results indicate that the identified key nodes closely correspond to city influence, with the top four nodes being direct-administered municipalities and provincial capitals of the Yangtze River Delta region. Furthermore, the algorithm accurately distinguishes core from non-core nodes, ranking nodes with more critical lines and higher passenger flows higher. Key nodes identified by \mathrm{K} {\mathrm{s}}^{+} exhibit a faster propagation rate in the SIR simulation by 1~3 iterations compared to other algorithms, with a peak passenger loss exceeding by 7%.

Cracks are a primary form of rural pavement distress, and their detection is often hindered by interference factors such as road shadows, weeds, and soil, complicating automated detection based on road images. To address this issue, this study proposes the Swin-Transformer Rural Road Crack Detection (S-TRCD) model, which leverages the Swin-Transformer backbone network. To mitigate the reduced recognition accuracy caused by surrounding interference during feature extraction, an adaptive hybrid attention mechanism module, CAS (Channel and Spatial), is designed. This module adjusts the crack weights in both spatial and channel dimensions, enhancing the model’s resistance to interference. To address the challenge of identifying cracks of varying sizes within the same image, a multi-scale object detection head with an attention mechanism, AHead (Attention Head), is developed. This detection head adaptively adjusts the network’s receptive field, enabling effective multi-scale crack detection. A rural pavement distress benchmark dataset, LNTU_RDD_NC, is created to evaluate the performance of the S-TRCD model. The study also trains and compares the S-TRCD model with commonly used detection models in the field, including improved YOLOv5, Faster R-CNN, and YOLOv8. Experimental results demonstrate that the S-TRCD model achieves mean average precision 4.06%, 12.12%, and 2.84% higher than the improved YOLOv5, Faster R-CNN, and YOLOv8 models, respectively, highlighting its superior detection performance for rural pavement crack detection.