To address the limitations of traditional manual inspection of rail transit infrastructure, such as low efficiency, safety risks, and the dependence of existing rail-mounted detection equipment on maintenance time gaps, which leads to blind spots and limited coverage, this study develops an integrated “End-Edge-Cloud-Surveillance” framework for autonomous unmanned aerial vehicle (UAV)-based intelligent inspection in rail transit. At the “End” layer, multi-source perception combining visible light, infrared, and LiDAR, together with visual-inertial state estimation, enables autonomous perception and task-level navigation. At the “Edge” layer, beyond-visual-line-of-sight (BVLOS) communication and secure, efficient data transmission mechanisms are established, alongside lightweight onboard inference for real-time defect and risk detection. At the “Cloud” and “Surveillance” layers, cross-scenario and multi-target inspection applications are conducted with global data analytics, while a low-altitude surveillance system integrating cooperative and non-cooperative surveillance is established to ensure regulatory compliance and operational safety throughout the entire process. The results demonstrate that this work systematically identifies the unique challenges and characteristics of the rail transit domain and, for the first time, unifies UAV-based rail transit inspection within a full-chain “End-Edge-Cloud-Surveillance” framework. This provides a generalizable reference framework for the future deployment of autonomous UAVs in rail infrastructure inspection.

For the problem of energy-efficient operation in urban rail transit, existing studies can be grouped into two main strands: Classical methods and technology-driven approaches. The classical strand reviews optimization of train speed profiles, train timetables, and their joint optimization, showing that these methods have developed into mature modeling and solution frameworks capable of reducing traction energy consumption and peak power while enhancing the utilization of regenerative braking energy under safety and service constraints. The second strand summarizes technology-driven progress and discusses the enabling effects and challenges of emerging technologies across three domains: Power supply, control, and operations. On the power side, efforts focus on advancing “source-grid-storage-train” integration, coordinating reversible converters, energy storage, and renewable sources to achieve load shifting and peak suppression. On the control side, autonomous operation and virtual coupling shift the problem toward a dynamic perspective under multi-train coordination. On the operations side, cross-line through services, multi-route patterns, and flexible train formations upgrade capacity supply and reduce inefficient traction. The findings suggest that classical methods already provide well-established frameworks for safe and effective energy saving, while new technology-driven approaches move beyond train-level offline optimization and progressively drive research toward system-level coordination that spans power supply, control, and operations. Future research should place greater emphasis on developing unified benchmark and evaluation systems, while also advancing multi-level coordination and integrated optimization across diverse domains.

This study addresses lane-change obstacle avoidance for autonomous vehicles under sudden road hazards and proposes SafeLC-DelayDDPG, a vehicle control algorithm based on Deep Reinforcement Learning (DRL). The task is formulated as a Markov Decision Process (MDP), and a structured hybrid state space is constructed by integrating local observations, lane-level semantic information, and the ego vehicle’s global states to enhance environmental perception and risk sensitivity. The action space consists of continuous front-wheel steering angle and longitudinal acceleration. The reward function is centered on a two-dimensional time-to-collision (2D-TTC) metric, balancing safety, efficiency, comfort, and traffic-rule compliance, and employs a TTC-conditioned dynamic weighting mechanism that prioritizes safety under high risk and efficiency under low risk. Furthermore, delayed policy updates and target policy smoothing are introduced, and the Critic network loss is refined to mitigate the training instability and Q-value overestimation issues inherent in Deep Deterministic Policy Gradient (DDPG). The proposed method is validated through traffic simulations across diverse scenarios. Experimental results show that, compared with multiple baseline algorithms, SafeLC-DelayDDPG achieves superior safety and efficiency: during training, the first-attempt and consecutive obstacle-avoidance success rates improve by up to 17.9% and 60.5%, respectively; the safety metric by up to 7.6%; and the average speed by up to 2.1%. In cross-scenario tests, the first-attempt and consecutive success rates improve by up to 13.3% and 44.1%, the safety metric by up to 9.8%, and the average speed by up to 0.6%.

Due to constantly interacting targets, rapidly shifting environments, and the inherent heterogeneity of multi-sensor data, dynamic traffic scenarios impose stringent demands on the perceptual robustness and decision reliability of intelligent systems. Multi-modal learning emerges as a critical solution to overcome bottlenecks in dynamic scenario understanding by fusing heterogeneous modalities. This paper offers a systematic review on multimodal robust learning for dynamic traffic scenarios. First, we clarify the definition of multimodal dynamic traffic scenarios, and analyzes the types and dynamic characteristics of multi-source modalities (e.g., optical, radio frequency, and acoustic). Next, we lay out the fundamental principles of multi-modal learning, paying particular attention to key techniques that enhance robustness across data-level processing, model architectures, and training strategies. Furthermore, we delve into core challenges currently confronting the field and future research directions. The review highlights current challenging of data imperfections, model limitations, and the absence of evaluation benchmarks, and chart future directions toward three aspects: technological innovation, technology integration, and collaborative industry efforts. Our aim is to provide a systematic reference for both theoretical research and practical deployment of multimodal robust learning in dynamic traffic scenarios, facilitate the evolution of intelligent transportation systems from being “available in limited scenarios” to achieving “reliability across all conditions,” and provide critical technical support for the widespread adoption of intelligent transportation solutions.

Visual intelligence, as a core branch of AI, seeks to endow machines with human-like capabilities for visual understanding and interaction. Since the breakthrough of deep learning in computer vision in 2012, the field has undergone four progressive stages of evolution. The first stage, represented by AlexNet, VGGNet and ResNet, leveraged large annotated datasets such as ImageNet to achieve remarkable success in closed-domain tasks (e.g., image classification and object detection), but its dependence on labeled data highlighted inherent limitations. The second stage witnessed the rise of self-supervised learning, with models such as MoCo, DION and MAE learning powerful visual representations from massive unlabeled data through contrastive, distillation, and masked reconstruction methods. The third stage marked a shift toward multimodal intelligence, where models like CLIP and GPT-4V integrated vision and language, enabling open-vocabulary understanding and advancing toward fine-grained, intent-driven reasoning. The current frontier is world models, exemplified by Sora, which aim not only to perceive and describe but also to simulate and predict the physical world, paving the way for embodied intelligence capable of interacting with reality. The current frontier is world models, exemplified by Sora, which aim not only to perceive and describe but also to simulate and predict the physical world, paving the way for embodied intelligence capable of interacting with reality. This fundamental transformation from discriminative understanding to generative simulation of the world marks a new consensus: generative modeling is the new deep learning. This survey follows this developmental trajectory, analyzing the core ideas, representative models, and methodological paradigms at each stage, while highlighting ongoing challenges in robustness, reasoning, and generalization. This survey follows this developmental trajectory, analyzing the core ideas, representative models, and methodological paradigms at each stage, while highlighting ongoing challenges in robustness, reasoning, and generalization.

To address the challenges of precise governance arising from the spatiotemporal heterogeneity and diverse patterns of pollutant and carbon emissions from road mobile sources in China, this study develops a machine learning-based analytical framework for emission characteristics, driving factors, and mitigation pathways. First, a transport-carbon-environment dataset is constructed by integrating multi-source data to analyze the spatiotemporal distribution characteristics of emissions. Second, a knowledge-constrained non-negative matrix factorization model is developed to identify distinct emission patterns and their underlying drivers. Finally, a multi-pattern integrated multiple linear regression model is applied to evaluate effective pathways for pollution and carbon reduction. The results indicate that in recent years, particulate matter emissions from road mobile sources have shown a decreasing trend, while carbon dioxide emissions continue to increase, with emissions of NO _x, CO, and VOCs remaining substantial. Three primary emission patterns are identified across China: a “CO₂-PM Reduction Pattern,” distributed across border and coastal regions and driven mainly by road passenger and freight transport activities and vehicle mileage (37.2%); a “Pollutant Reduction Pattern,” found in municipalities like Beijing and Tianjin and provinces such as Guangdong, mainly influenced by the Gross Transport Product (30.1%); and a “CO₂-NO _x Pollution Pattern,” located in the central and western regions, affected by multiple factors including railway development and its electrification, as well as the use of light-duty transport vehicles (23.6%). Pathway analysis indicates that accelerating economic development in the transport sector has the most significant impact on reduction, potentially reducing CO₂ and NO _x by up to 6.7% and 4.5%, respectively. Other significant measures include restructuring the transport energy system, reducing road freight and vehicle mileage, and promoting the shift from road to rail alongside railway electrification. The proposed framework provides a quantitative basis and strategic guidance for coordinated pollution and carbon reduction from road mobile sources in the context of China’s dual-carbon goals of carbon peaking and carbon neutrality.

The transformation and upgrading of the railway transportation production system represent a core issue and critical challenge currently faced by railway transportation enterprises in their transition toward modern logistics enterprises. To support the construction of a modern logistics-oriented railway transportation production system, the study first analyzes key aspects including top-level design, theoretical architecture, critical technologies, and implementation evaluation indicators. Subsequently, the key scientific and technological bottlenecks that need to be overcome in building the new system are discussed, proposing a key technological architecture oriented to modern logistics. Finally, based on this architecture, a simulation prototype system for railway production organization capable of optimization, evaluation, and simulation is designed to validate the feasibility and effectiveness of the proposed theoretical framework and technical pathways. The research demonstrates that this study offers certain theoretical reference value and practical significance for enhancing the operational efficiency and service quality of railway logistics, as well as promoting the transformation and development of transportation production. Additionally, the developed prototype system can provide a reusable development paradigm and decision-support tool for subsequent system optimization and engineering applications.

To meet the stringent requirements for low latency, high reliability, and intelligence posed by emerging applications such as autonomous driving and the Industrial Internet, this study examines the limitations of traditional network architectures in heterogeneous resource integration, service adaptation, and intelligent decision-making. A smart computing integration network architecture based on a “three-layer, three-domain” framework is proposed, and its development prospects are discussed in relation to the deep integration of heterogeneous networks, computing resource scheduling, and network-native intelligence. The research results demonstrate that the proposed smart computing integration network architecture enables a paradigm shift from “passive connection” to “active service” through key technologies such as unified resource representation, computing-network demand analysis, and agile resource scheduling, thereby achieving comprehensive coordination between computing and networking. The research findings provide theoretical references and technical pathways for the construction of intelligent, efficient, and reliable emerging network infrastructures.

Most existing quality evaluation methods for high-speed railway ballastless track construction are established based on traditional construction processes. As ballastless track construction progressively shifts towards intelligent construction, the existing evaluation methods can no longer adequately meet the requirements for quality evaluation of intelligent ballastless track construction.Taking the construction of the CRTS Ⅲ slab ballastless track as an example, this paper analyzes the key quality factors of intelligent construction processes, constructs an evaluation index system, and establishes a comprehensive quality evaluation model for intelligent ballastless track construction based on the Analytic Hierarchy Process (AHP) and the matter-element extension evaluation theory. Field tests are carried out on an intelligent construction test section of a high-speed railway for verification.The results show that the model established in this paper can objectively evaluate the quality of intelligent ballastless track construction. Compared with the traditional process, the guided intelligent construction process achieves a 32% improvement in the excellent rate. For the intelligent construction of the CRTS Ⅲ slab ballastless track, the construction quality of the track slab is the most important, followed by the quality of the self-compacting concrete construction, while the construction quality of the base slab has the least impact.The research results can provide a basis for evaluating the quality of intelligent construction of CRTS Ⅲ slab ballastless tracks for high-speed railways and offer a reference for the quality management of ballastless track construction in China’s high-speed railways.

With the continuous evolution of intelligent railway systems, novel railway applications based on spatiotemporal information from the BeiDou Navigation Satellite System (BDS) have garnered significant attention. However, the lack of dedicated testing systems tailored to specific railway applications makes it difficult to meet the full lifecycle requirements of BDS-based railway systems. To address this problem, this paper focuses on exploring the requirements and development potential for specialized testing and evaluation of BDS-based railway applications. It investigates how to leverage effective mechanisms to build dedicated testing instruments and system environments that address the unique characteristics of the railway industry, thereby meeting the full lifecycle needs of BDS-based railway systems. First, existing relevant work is surveyed and summarized, outlining the development needs and vision for dedicated testing of BDS-based positioning in railway. Second, a comprehensive overall architecture for a dedicated railway BDS positioning testing system is established, centered on the concept of "zero-on-site testing". This architecture aims to adopt a modular approach and inter-connected collaborative mechanisms to forge a new pathway that balances conventional satellite positioning testing paradigms with the application characteristics of the railway industry. Third, this paper uses the train positioning unit of a specific type of train control system as a test subject example, detailing the setup of the dedicated test system and the implementation of testing. Finally, this paper analyzes and summarizes the research challenges that may arise in providing comprehensive and reliable testing and evaluation for the progressively developing applications of BDS in railway, offering corresponding approaches. The research results indicate that constructing a dedicated railway BDS positioning testing system can effectively enhance testing coverage, flexibility, adaptability, and compatibility, thereby providing strong support for the deep application of BDS in the railway industry.

Current research on optimizing and controlling traction energy consumption in urban rail transit has largely focused on localized improvements within single systems, making it difficult to coordinate inter-system coupling and achieve global energy efficiency. To address this problem, this study proposes a multi-system collaborative optimization method that integrates power supply, vehicles, and operations. By analyzing the circulation paths of traction energy consumption and the inter-dependencies among decision variables, a four-layer global optimization framework is developed. The first layer optimizes single-train driving curves according to specified interval running times, identifying energy-minimal trajectories under constraints such as punctuality, comfort, and speed limits. The second layer optimizes inter-station running times by adjusting train travel durations between adjacent stations, thereby minimizing overall line energy consumption while ensuring that the total turnaround time equals the given value. The third layer focuses on optimizing multi-train energy-efficient timetables. By adjusting departure intervals and dwell times, this layer reduces total traction energy consumption while balancing passengers’ average waiting times through multi-train coordination. The fourth layer addresses threshold optimization of energy storage and inverter devices. The results of the first three layers of the train scheduling and control optimization process are used to inform the adoption of a grid-voltage-based control strategy. Simulation studies are undertaken in actual line operations. Simulation results demonstrate that the proposed global optimization reduces system traction energy consumption by 13.24%, while increasing passengers’ average waiting time by only 12 seconds. These results verify the effectiveness of the proposed coupled energy-saving optimization method and provide a theoretical foundation for the comprehensive optimization and control of traction energy consumption in urban rail transit.

In response to the challenge of extracting information from railway signal system station yard diagrams, this study proposes a primitive detection model, YOLO11-AT, based on an improved YOLO11. By constructing a detection model that integrates object detection and keypoint detection, it achieves automatic extraction of primitives and keypoints. First, an Attentional Scale Sequence Fusion (ASF) module is incorporated into the neck network to fuse multi-scale features, thereby enhancing detection performance for small targets. Second, a Task-Aligned Dynamic Detection Head (TADDH) is implemented in the head network, which improves feature interaction between classification and localization tasks through task alignment, reduces feature conflicts, and increases detection accuracy for densely distributed targets. Finally, Slicing Aided Hyper Inference (SAHI) is applied to improve detection accuracy on high-resolution station yard images. Experiments are conducted on a constructed dataset containing multi-style station yard diagrams to validate the proposed method. The results show that, compared with YOLO11s-pose, the proposed YOLO11-AT improves precision, recall, mAP0.5, and mAP0.5-kp by 9%, 2.2%, 4.2%, and 3.2%, respectively, while reducing the number of parameters by 4.3%. Compared with existing mainstream detection models, YOLO11-AT achieves a better balance between detection accuracy and efficiency. The results indicate that the proposed method is adaptable to various styles of station yard diagrams and provide a feasible solution for automated information extraction from station yard drawings.

To investigate the decision-making mechanisms of stranded passengers’ travel mode choice during unexpected metro service disruptions and to clarify the influence of key situational factors, this study constructs a baseline Multinomial Logit model incorporating mode-specific attributes, based on scenario simulations and Stated Preference (SP) surveys. A stepwise utility-correction approach is applied to quantify both the impact intensity and patterns of influence exerted by factors such as expected recovery time, weather conditions, travel time and purpose, remaining trip distance, crowd behavior, and the availability of alternative routes. Model improvements are evaluated using the Likelihood Ratio Test (LRT), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and prediction error rates. The research results indicate that passenger decision-making demonstrates strong scenario dependence and a pronounced risk-averse tendency. The effects of situational factors vary considerably, with expected recovery time having the greatest impact (LR=183.98, prediction error reduced by 1.94%), followed by weather conditions (LR=102.63, error reduced by 1.16%). Trip purpose and remaining journey distance also show notable influence, while crowd behavior and alternative-route availability do not exhibit significant effects. Preferences shifts across public transport modes display similar patterns of change. however, waiting on-site, though frequently chosen as a passive option, maintains a considerable selection rate but declines significantly when systemic and situational risks overlap. Sensitivity to scenario factors differs markedly across modes, with those offering higher certainty and lower risk proving more attractive in disruption scenarios.

To address the issues of low detection accuracy, slow detection speed, and frequent missed or false detections in railway track foreign object intrusion detection, this study proposes a lightweight railway track foreign object intrusion detection algorithm based on shallow feature fusion (YOLO-LSF). First, building on the YOLOv8n feature extraction network, the C2f module is improved based on GhostConv to construct the C2f_Ghost module, thereby reducing both the parameter count and computational cost of the model. Second, the MLCA attention mechanism is introduced at the end of the backbone network to enhance the feature representation of the target area and optimize the feature extraction efficiency of the model. Third, deformable convolution DCNv2 is employed to replace some ordinary convolutions in the C2f module of YOLOv8n, constructing the C2f_DCNv2 module and further strengthening the model’s feature extraction capacity. Finally, shallow feature information from the backbone network is integrated into the neck network, effectivelt mitigating detail loss caused by multiple convolution operations and enhancing the model's ability to detect distant foreign objects (small targets). Experimental results show that on a self- constructed railway track foreign object intrusion detection dataset, compared with the original YOLOv8n algorithm, the YOLO-LSF algorithm achieves an improvement of 5.2% in average precision, 3.37% in FPS, a reduction of 20.1% in the number of parameters, and a decrease of 22.2% in computational complexity. These results verify that the proposed algorithm significantly enhances detection accuracy and speed in complex environments while reducing the likelihood of missed and false detections.

Amid the increasing frequency of extreme climate events worldwide, urban transportation systems face dual pressures from structural vulnerabilities and climate-related risks, highlighting the urgent need to enhance climate adaptability and comprehensive resilience. Firstly, this paper systematically reviews the primary impacts of climate change on urban transport network structures and residents’ travel behaviors, revealing issues such as reduced network accessibility and temporal-spatial mismatches in travel demand caused by meteorological hazards. Then, from the perspective of physical and social resilience, the study analyzes the evolutionary characteristics of infrastructure disturbance resistance and recovery capabilities, traveler risk-response behaviors, and governance system adaptation mechanisms. Finally, the paper summarizes sustainable mobility policies guided by low-carbon objectives and transport resilience policy frameworks centered on disturbance absorption and recovery, explores synergistic governance pathways formed through their integration, reviews recent research progress on urban transport networks in infrastructure optimization, coordinated allocation of transport capacity resources, and transport-energy system coupling, and proposes an integrated optimization approach for constructing a multi-modal synergistic, low-carbon resilience-integrated transport system. The findings indicate that enhancing climate adaptability in future urban transportation systems should focus on multi-modal network optimization and technology empowerment, strengthen social equity and policy coordination, and promote a governance shift from infrastructure resilience to system resilience and from single-objective to multi-objective coordination. It is necessary to deeply integrate the coupled development of transport and energy systems, establish a full-cycle resilience assessment framework covering resistance, absorption, recovery, and adaptation to fill the current evaluation gap that focuses on post-climate-disaster impacts while neglecting long-term evolutionary processes. Additionally, integrated modeling of physical and social resilience, combining network topology, travel behavior, and governance mechanism analysis, should be refined to provide customized infrastructure optimization solutions and policy support for cities with diverse climate characteristics.

To systematically summarize the main themes, methods, and findings in current research on the resilience of comprehensive transportation systems, identify existing gaps, and provide insights for future research, this study conducts a detailed review of relevant literature on the resilience of comprehensive transportation systems. More than one hundred articles are retrieved from major Chinese and international academic databases. The review synthesizes existing work from several perspectives, including research objectives and scopes, resilience evaluation indicators, integrated transportation network modeling approaches, disruption and recovery scenario design, and strategies for enhancing system resilience. Current research can be broadly categorized into two spatial scales, intercity and intra-urban, with primary objectives focusing on resilience assessment, identification of critical components, and resilience enhancement. Regarding resilience evaluation indicators, existing studies predominantly adopt two categories: network-analysis-based indicators and traveler-oriented indicators, alongside some integrated indicators and metrics tailored to multimodal transportation. For modeling integrated transportation networks, the literature is reviewed from the perspectives of multilayer network structures, disruption and recovery scenario configurations, and network dynamics. In terms of resilience enhancement, current strategies typically fall into three groups: pre-disaster prevention, post-disaster response, and optimized recovery planning. The results demonstrate that future research should place greater emphasis on demand-side factors and incorporate multimodal transportation characteristics more thoroughly. Disruption scenarios and passenger behavioral responses require more accurate modeling, with fuller consideration of slow modes of travel. Dynamic analyses should pay closer attention to realism and the influence of travel information guidance. Additionally, the complexity of integrated transportation systems and the heterogeneity across different transport modes warrant further investigation.

With the continuous expansion and technological upgrading of China’s high-speed railway systems, the electromagnetic complexity of the operating environment has significantly increased. In particular, the frequent appearance of strong external electromagnetic interference sources has posed unprecedented challenges to the stable operation of the system. The traditional electromagnetic compatibility (EMC) framework, which focuses primarily on improving device immunity, shows evident limitations when dealing with random and strong external electromagnetic interference, such as delayed response and poor adaptability. These shortcomings make it difficult to meet the current high demands for system functional stability, mission continuity, and rapid recovery. To address these challenges, this paper systematically reviews the current research progress in the field of EMC for high-speed railways in China, with a particular focus on key technical aspects such as interference source modeling, coupling path identification, disturbance immunity evaluation of critical equipment, and system-level protection strategies. On this basis, a new research framework for electromagnetic safety is proposed, with train control capability retention as the core objective. This framework aims to shift from "passive immunity" to "intelligent defense and autonomous recovery," and it includes essential components such as intelligent interference identification and perception, multi-physical-field coupled network modeling, electromagnetic interference risk propagation mechanisms, active protection technologies, and experimental validation methods. Research findings indicate that electromagnetic safety, as an extension of the traditional EMC system, represents a key pathway to ensuring the high safety and high reliability of high-speed railway systems under complex electromagnetic environments. The outcomes of this study can provide theoretical foundations and technical references for the future design and engineering implementation of electromagnetic protection in high-speed railway systems.

To address the limitations of existing road defect detection algorithms, such as low accuracy in complex backgrounds, limited generalization capability, and frequent missed detections of small objects, this study proposes an improved YOLOv8-based detection algorithm. First, a Coordinate Attention (CA) mechanism is integrated into the backbone network layer to introduce positional information, enabling the model to better capture spatial dependencies and enhancing its feature discrimination ability under complex background conditions. Second, the Path Aggregation Network (PANet) in the neck network layer is replaced with a weighted Bi-directional Feature Pyramid Network (BiFPN). By incorporating bidirectional connections and learnable weights, the network facilitates bidirectional information flow across different resolution levels, leading to more effective fusion of low-level positional features with high-level semantic features and improving multi-scale feature representation. Finally, small-object feature maps are introduced to more accurately capture the small-object characteristics and reduce missed detections, thereby improving detection precision. Experimental results show that on the RDD2022 road defect dataset, the improved algorithm increases mean Average Precision (mAP) by 3.1% compared to the original version, while reducing model parameters by 2.3%, achieving more accurate and rapid road defect detection.

The stable operation of switch machines is a key guarantee for the safety of High-Speed Railways (HSRs). With the increasing demand for intelligent railway systems, higher requirements are imposed on the precise perception and autonomous diagnosis of switch machine working conditions. To overcome the limitations of traditional methods in terms of diagnostic accuracy, computational real-time performance, and anti-interference capability, this paper constructs an intelligent diagnosis model that integrates cross-modal feature attention mechanism and Transformer-XL recursive memory mechanism. The proposed model enhances fault recognition and environmental adaptability under complex operating conditions. By introducing a cross-modal attention mechanism, it enables dynamic interaction between power curve signals and switch rail vibration signals, mitigating the judgment bias caused by missing single-modal information. The Transformer-XL with recursive memory mechanism dynamically adjusts the model’s perception of historical information, allowing it to extract state information across time windows. Additionally, 1D-CNN is incorporated for short-term dynamic feature extraction, optimizing global sequential modeling while improving noise robustness and reducing computational complexity. Experimental results demonstrate that the proposed model exhibits significant advantages in cross-modal feature representation, long-term dependency modeling, noise robustness, and computational efficiency. This study provides an intelligent diagnostic solution for HSR maintenance, featuring high efficiency, low resource consumption, and strong environmental adaptability. It facilitates the transition from reactive maintenance to predictive maintenance, thereby improving operational safety margins and robustness.

To address the vulnerability of urban rail transit networks during operational disruptions, this study proposes a systematic evaluation method that integrates network structure, passenger flow dynamics, and passenger behavioral responses. First, a CPT-NL model of travel choice behavior is developed based on Cumulative Prospect Theory (CPT) and the Nested Logit (NL) model. This model captures passengers’ bounded rationality and risk aversion under emergency conditions. Second, a vulnerability analysis framework is designed for urban rail networks within multimodal transportation systems, establishing quantitative vulnerability metrics that reflect the combined impacts of operational disruptions and passenger flow dynamics. Finally, using Shenzhen’s rail transit network as a case study, peak-hour passenger flow data are employed to systematically simulate disruptions at sections and transfer stations, quantifying network performance losses and passenger travel impacts. The results indicate that failures at critical sections or transfer stations reduce network connectivity and trigger large-scale passenger congestion and significant travel delays. During section disruptions, the proportion of affected passengers reaches up to 14.78%, with network vulnerability peaking at 12.02%. During transfer station disruptions, the proportion of affected passengers peaks at 11.73%, and network vulnerability reaches 5.53%. The number of delayed passengers significantly exceeds the number who abandon rail travel due to disruptions, indicating that most passengers opt for detours within the rail network or multimodal travel alternatives. Operational management should strengthen maintenance and emergency preparedness for high-vulnerability nodes, while optimizing multimodal transportation scheduling to improve the resilience and operational stability of urban rail transit systems.

To investigate the variation patterns of the temperature field and thermal stress in seamless CRTS Ⅲ slab tracks on simply supported beam bridges under high-temperature conditions, an indirect heat-stress coupling analysis method is employed. A finite element model of the coupled structural system of the heat-ballastless track is established to analyze the distribution and evolution of the temperature field, as well as the longitudinal force, stress, and displacement distribution between track structure layers under high temperatures. The results indicate that the temperature of each structural layer of the ballastless track exhibits a wave-like variation with ambient temperature. Over the course of a day, the maximum and minimum temperatures, 53.1 ℃ and 26.4 ℃, respectively, occur at the top of the track slab. The amplitude of the temperature time-history curves decreases with increasing vertical depth of the track structure, and the peak temperature shows a time lag. In summer high-temperature conditions, the temperature gradient of the track slab approaches zero around 11:00 and 21:00; the positive gradient peaks at 85.5 ℃/m at 15:00, while the negative gradient peaks at -43.8 ℃/m at 03:00. As the depth of the track structure increases, the temperature gradient gradually decreases. Under single-day high-temperature conditions, the rail longitudinal force and track structure displacement reach their maximum around 18:00 every day, representing the most unfavorable state, while the longitudinal stress in the track slab and self-compacting concrete layer peaks between 14:00 and 16:00. These findings provide a t theoretical reference for monitoring and maintenance of track structures in high temperature regions during summer.

To address the insufficient adaptability of fixed signal timing in dynamic traffic flow environments, this study proposes an improved Gradient-Based Meta-Learning Deep Q-Network (GBML-DQN) algorithm integrating a meta-learning mechanism for adaptive signal control at isolated intersections. First, the state space is constructed based on lane density, the signal phase action space is defined, and a multi-objective weighted reward function is designed. Second, using DQN as the base architecture, the discount factor γ is dynamically adjusted via a meta-gradient strategy. Third, a Dueling structure is used to decouple state values from action advantages, and NoisyNet is employed to replace the traditional ε-greedy strategy. Consequently, two improved algorithms are constructed: Improved GBML-DQN-Dueling (I-GD-D) and Improved GBML-DQN-Noisy (I-GD-N). Finally, experimental validation is conducted on the SUMO simulation platform across high, medium, and low traffic volume scenarios. The experimental results indicate that I-GD-N exhibits superior robustness and adaptability across different traffic scenarios. Specifically, under medium traffic conditions using the Stochastic Gradient Descent (SGD) optimizer, the average delay is reduced to 20.55 s, representing an improvement of approximately 20% compared to DQN. While DQN exhibits higher stability than GBML-DQN in medium and low traffic scenarios due to its simpler structure, it suffers from significant policy degradation in high traffic scenarios or when using the Root Mean Square Propagation (RMSprop) optimizer, performing worse than even fixed signal timing. The dynamic γ adjustment mechanism adaptively optimizes based on traffic intensity; in high traffic scenarios, it significantly outperforms fixed γ strategies by reducing γ to rapidly respond to congestion. The research results provide a valuable reference for urban intersection signal control.

To address the complex and diverse characteristics of track fastener defects, as well as the low efficiency and high missed-detection rates of traditional detection methods, this study proposes a lightweight detection model, FPSI-YOLOv8s, based on the YOLOv8s framework. First, to reduce model complexity, FasterNet, featuring higher processing speed and fewer parameters, is adopted to replace the CSPDarkNet53 backbone in YOLOv8s for defect feature extraction. Second, the C2f module in the YOLOv8s neck is redesigned using Position-aware Recurrent Convolution(ParConv) to form a new FasterBlock module, enabling multi-scale feature fusion and further model lightweighting. Third, a Spatial Group-wise Enhance (SGE) attention mechanism is integrated after the SPPF layer to enhance the model’s sensitivity to defect features and mitigate accuracy degradation. Finally, the Inner-IoU loss function replaces CIoU to improve detection performance for objects of varying scales and shapes, while refined quality evaluation and gradient-gain strategies further enhance model robustness. Experimental results show that the improved model reduces model size by 29.78%, and decreases computational cost and parameter count by 29.93% and 30.46%, respectively, with only a 0.7% decrease in detection accuracy. These results demonstrate that the proposed model achieves significant lightweighting and improved operational efficiency while maintaining high accuracy, indicating strong application potential for rapid inspection of track fasteners.

To address the limitations of existing research in adequately characterizing road-rail topological coupling and the mechanisms of cross-system load transfer and transmission time lags during node failures, this study constructs a topology model for coal multimodal transport networks that incorporates road-rail synergistic structures. A cascading failure evolution mechanism is proposed, explicitly accounting for capacity constraints, cross-system transshipment, and transmission time lags. Addressing functional maintenance and recovery needs throughout the disturbance lifecycle, a three-dimensional resilience assessment index system, comprising resistance, absorption, and recovery capabilities, is established to quantify functional attenuation and restoration under varying disturbance intensities and scopes. Furthermore, two load redistribution strategies based on network structure and capacity distribution are designed to compare resilience under scenarios of key node failure, cascading congestion, and cross-mode substitution. To characterize the impact of differentiated disturbances such as intentional attacks and natural disasters, a dynamic disturbance strategy based on grey information is introduced to simulate attack selection and resilience evolution under varying levels of network knowledge. Finally, a typical Chinese coal transport corridor is used as a case study to simulate the scenario. Simulation results demonstrate that the proposed model effectively captures key node failure characteristics and cascading evolution dynamics. The findings reveal that load redistribution strategies and disturbance modes significantly influence system resilience, providing a theoretical basis for enhancing network resilience, optimizing road-rail capacity allocation, and improving emergency response.

To address the problem of approach flight delays and reduced operational efficiency caused by severe weather, a quantitative assessment framework for operational resilience in arrival operations is developed. A Greedy Algorithm (GA) embedded within a Receding Horizon Control (RHC) framework is proposed to optimize the sequencing of weather-affected approach flights, enabling rapid improvement of key performance indicators during the resilience recovery phase and enhancing overall operational resilience. First, based on the dynamic evolution pattern of arrival flight performance, comprising four stages: stable, disrupted, recovery, and new stable, delay duration, on-time performance, and landing throughput under severe weather conditions are selected as fundamental indicators. Incorporating actual operational characteristics, a comprehensive resilience metric is constructed that integrates system redundancy and average recovery performance compensation. Second, an airborne holding cost function is formulated based on aircraft type and delay duration, balancing economic efficiency and fairness principles. Third, the RHC+GA approach decomposes the dynamic optimization problem through a rolling time-domain mechanism. Aiming to minimize flight airborne holding costs, the greedy algorithm yields locally optimal solutions and thereby generates an improved flight sequence under adverse weather conditions. Finally, using a thunderstorm scenario at Tianjin Binhai International Airport for verification. Results show that under the actual scheduling sequence, the operational resilience of arriving flights is 1.529. After reordering with the RHC+GA method, resilience increases to 2.791, an 82.5% improvement, while the average delay per aircraft is reduced by 6 minutes, significantly accelerating operational performance recovery.

To address the challenges of resilience assessment and post-failure recovery in railway passenger networks following large-scale station failures, this study proposes a railway passenger network resilience assessment and optimal recovery method based on a service efficiency indicator. First, from the perspectives of network service capacity and actual operational characteristics, a railway passenger service network model is constructed. A service efficiency indicator considering the number of operating trains between stations is proposed, and a network service resilience assessment model is established using the resilience curve and the service efficiency indicator. Second, an optimal resilience recovery model is formulated with the objective of maximizing recovery resilience, and an improved Particle Swarm Optimization (PSO) algorithm is developed to determine the optimal recovery strategy under large-scale station failures. Finally, the Chengdu-Chongqing railway passenger network is used as a case study to evaluate network resilience and compare the resilience recovery performance of different strategies under three disturbance scenarios. The results indicate that the service efficiency indicator effectively assesses the impact of station failures on network performance and accurately identifies critical stations. The optimal recovery strategy derived from the resilience recovery model consistently outperforms both random and degree recovery strategies across all disturbance scenarios. Under deliberate attack scenarios, the recovery resilience of the network achieved by the optimal strategy improves by 69.90% and 4.81% compared with the random and degree recovery strategies, respectively.

To address the prevalent false positives and missed detections of railway fasteners in complex scenarios such as switch machines and turnouts for railway fastener inspection tasks, this paper proposes a railway fastener condition detection method based on an improved YOLOv9. First, to overcome the challenge of extracting features from fastener regions in complex scenes, the Large Selective Kernel (LSK) attention mechanism is integrated with the RepNCSPELAN4 module. This optimization enhances the performance of the feature extraction module, enabling more effective capture of critical feature information in fastener regions and improving the model’s adaptability to diverse scenes and targets. Second, to better distinguish subtle details of confusing fastener damage states, a feature fusion network based on Space to Depth (SPD) convolution is developed, thereby increasing accuracy in low-resolution and small-object detection and ensuring precise identification of fastener damage states even against complex backgrounds. Third, Shape IoU is adopted as the new loss function to more accurately measure the overlap between predicted and ground-truth bounding boxes, endowing the model with greater robustness and superior target localization precision. Finally, to validate the effectiveness of the proposed method, a real-world railway fastener dataset encompassing complex operating conditions is collected and constructed, and comprehensive comparative experiments are conducted. Experimental results demonstrate that the proposed method effectively detects railway fastener conditions, achieving a 1.3% improvement in detection accuracy over the baseline model, while reducing the false detection rate by 0.7% and the missed detection rate by 1.4%. This enhances the reliability and stability of railway fastener condition detection in complex scenarios.

This study investigates the time-varying vulnerability of high-speed rail (HSR) networks under cascading failures initiated by unexpected train halts that disrupt network links. Based on the actual passenger timetable of December 15, 2023, the paper simulates the complete propagation process of cascading train failures under diverse attack strategies (random and intentional) and intensities. First, a cascading failure model is developed that captures the operational characteristics of HSR networks and analytically dissects the failure propagation mechanism. Subsequently, network performance metrics are designed from both structural and functional perspectives, incorporating temporal dynamics inherent in cascading failures, to establish integrated static and dynamic vulnerability assessment frameworks. Finally, using real-world Chinese HSR timetable data, a topological network is constructed and the interdependencies among trains, lines, and stations are formalized. Through cascading failure simulations, the static characteristics of stations and lines alongside the diurnal operational patterns of trains are analyzed, thereby revealing the temporal evolution of network vulnerability indicators. The results demonstrate that under low-intensity attacks within identical time windows, intentional targeting disrupts significantly more trains than random failures. However, as attack intensity escalates, the differential impact between the two strategies diminishes. Under intentional attacks, trains traversing critical links during morning peak hours exhibit the greatest vulnerability and impose the most severe degradation on daily network performance. Therefore, prioritizing protective measures for morning peak corridors and implementing rapid recovery interventions constitute essential strategies for safeguarding network performance and enhancing resilience.

To address operational disruption risks in urban rail transit networks arising from station failures, this study proposes a systematic resilience optimization methodology based on a weighted coupled map lattice (CML) model integrated with an improved simulated annealing algorithm. First, by comprehensively incorporating station degree, inter-station passenger flows, and boarding/alighting volumes, a weighted CML model is constructed to accurately simulate cascading failure processes following station disruptions. Structural and service performance metrics derived from this simulation are then employed to quantify system resilience. Subsequently, two optimization models are established: A critical station identification model aimed at minimizing cumulative resilience loss, and a recovery sequence optimization model for failed stations designed to maximize restoration efficiency. To enhance algorithmic robustness and search efficiency, an improved simulated annealing algorithm featuring dynamic cooling rates and hybrid perturbation strategies is developed. Finally, the Wuhan Metro network serves as a case study for empirical validation. Results demonstrate that, compared to traditional single-indicator-based methods (such as those relying solely on station degree, inter-station flow, or boarding/alighting flow), the critical stations identified by the proposed model (primarily high-flow non-transfer stations) induce 7% to 13% greater network performance degradation upon failure. Moreover, the recovery sequence optimization model and algorithm yield a 3% to 7% improvement in restoration efficiency, validating the effectiveness of the proposed approach in enhancing network robustness and recovery capacity.

To address the difficulty of jointly optimizing safety and timeliness in route planning for large-scale emergency supply transportation under extreme events, this study investigates railway–highway intermodal route planning. First, a multi-objective mathematical programming model is developed that aims to simultaneously maximize safety probability and minimize transportation time, while comprehensively incorporating flow balance constraints, road hierarchy constraints, and disaster-induced route disruption constraints. Second, a hybrid LCA-NSGA-Ⅱ algorithm is proposed, in which the Label Correcting Algorithm (LCA) is embedded into the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) framework to generate high-quality initial populations. In addition, a Dual-Node Partially Mapped Crossover (DNPMX) operator and a Segment Deletion and Repair (SDR) operator are designed, and an adaptive crossover and mutation mechanism is introduced to enhance search efficiency. Finally, the effectiveness of the proposed model is validated through multiple experiments on a railway-highway multimodal network comprising 104 nodes and 496 arcs, and the performance of the LCA-NSGA-Ⅱ algorithm is compared with that of the Gurobi solver and the conventional NSGA-Ⅱ algorithm. The results indicate that the LCA-NSGA-Ⅱ algorithm achieves an 87.98% improvement in solution efficiency compared to the Gurobi solver. Compared to the traditional NSGA-Ⅱ algorithm, it achieves 76.1% and 1.15% improvements in inverted generational distance (IGD) and hypervolume (HV), respectively. The model effectively balances time and safety objectives and generates distinct routes for different types of supplies between the same origin-destination pair. Sensitivity analysis of disaster intensity levels shows that as the disaster impact weakens, transportation time is reduced by up to 3.33%, while safety probability increases by 4.70%, demonstrating the model’s adaptability to dynamic disaster scenarios. The proposed model and algorithm provide effective decision support for emergency supply transportation scheduling under extreme events, thereby enhancing the reliability and responsiveness of emergency logistics systems.

To address the problem that isolated analysis of conventional bus and subway networks fails to capture actual passenger transfer behaviors and inter-network coupling effects, this study constructs a weighted composite network model of conventional bus and subway systems using the Space L modeling method. The model integrates actual spatial distances between nodes, establishing a coupling radius of 660 m via Geographic Information Systems (GIS) and the Amap Application Programming Interface (API), while excluding stations where the actual walking path is excessive despite meeting straight-line distance criteria. Line carrying capacity and passenger travel time are assigned as edge weights to balance network supply capacity with passenger travel costs. Network topological properties are analyzed using Matlab and Gephi, focusing on core metrics such as average degree, average path length, clustering coefficient, and betweenness centrality. Furthermore, two novel attack strategies are proposed: the Node Centrality Importance Attack Strategy (CIAS), based on multi-attribute decision theory, closeness centrality, and betweenness centrality; and the Degree Attack Strategy of Coupling Nodes (DASCN), which accounts for the substitution effects of coupling nodes. An attack efficiency evaluation function is introduced to compare these novel strategies against traditional ones, using Harbin’s main urban area as a case study. Performance is evaluated via global efficiency, the relative size of the largest connected subgraph, and the network connectivity rate. The results demonstrate that the composite network shows significant improvements in average degree, clustering coefficient, network efficiency, and connectivity rate, with decreased average path length and betweenness centrality, indicating enhanced resilience. Under identical node removal scales, the CIAS exerts a greater negative impact on network performance than traditional strategies, whereas the DASOCN has a comparatively smaller impact on global efficiency, the largest connected subgraph, and connectivity rates.

Highway traffic speed is jointly influenced by multiple external factors, including adjacent land-use types, weather conditions, and traffic volume, and exhibits nonlinear spatiotemporal variations. To address this, this paper proposes WP-STGCN-GRU, a multi-step short-term traffic speed prediction model that integrates external attributes with spatiotemporal features. First, a spatiotemporal prediction framework is constructed using the Spatial-Temporal Graph Convolutional Network (STGCN) and Gated Recurrent Unit (GRU). Adaptive weighted adjacency matrices are employed to encode spatial relationships among highway segments, capturing the spatiotemporal dependencies of traffic speed. Second, weather conditions and Points of Interest (POI) features are incorporated into a speed attribute enhancement unit, which expands the feature dimensionality and strengthens the representation of external factors in speed variation patterns, enabling more accurate traffic speed predictions. Finally, model performance is evaluated through baseline comparisons and ablation studies. Experimental results indicate that dynamically integrating external attributes such as weather and POIs significantly improves both single-step and multi-step traffic speed predictions. Compared with baseline models, the proposed model reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by over 14.1% and 14.8%, respectively, for single-step prediction, and by over 4% and 14%, respectively, for multi-step prediction. In a 15-minute forecasting task, fusing both external attributes reduces MAE and RMSE by 23.0% and 17.8%, respectively, with weather information contributing more prominently and demonstrating clear complementarity between two factors. These findings provide valuable insights for highway speed prediction and offer guidance for improving traffic safety and intelligent highway management.

Existing methods for identifying critical communities and key stations in transportation networks often lack unified evaluation criteria and a systematic analytical framework. To address this, this study proposes a hierarchical identification framework based on the Leiden algorithm. First, an improved modularity function that integrates both passenger flow and topological features is constructed, systematically incorporating station passenger volumes and network topological characteristics into the Leiden algorithm’s community detection process. Second, based on the community detection results, a multi-dimensional evaluation system is developed that simultaneously considers functional and topological attributes to quantitatively assess the importance of critical communities and key stations. Finally, the applicability of the proposed method is validated using real operational data from Beijing’s integrated bus-metro network, by comparing variations in community structures and critical node distributions across different day types. Results indicate that compared with non-working days and holidays, the number of communities on weekdays decreases by 9.05% and 8.59%, respectively, while the number of large-scale communities (with more than 150 nodes) increases by 16.67% and 40%. Overall, community importance is predominantly driven by functional attributes, but weekday net-works exhibit a more balanced interplay between functional and topological significance. Furthermore, bus stations consistently represent a higher proportion of critical nodes due to their superior spatial coverage and service flexibility. This study provides theoretical insights and methodological support for the structural optimization and differentiated operational planning of multimodal transportation networks.

To address the issues of low extraction accuracy of track areas and lagged risk response during railway maintenance windows, this paper proposes a safety detection algorithm based on hierarchical semantic fusion. First, to overcome challenges such as the slender geometry of distant tracks, background interference, and high computational redundancy in traditional segmentation networks, a hierarchical semantic fusion-based track area extraction algorithm framework is constructed. By employing a hierarchical feature encoder and a lightweight semantic fusion decoder, the algorithm significantly reduces parameter counts and computational complexity. This enables high-precision, real-time segmentation of track areas under limited hardware resources, providing a reliable spatial foundation for subsequent risk assessment. Second, to adapt to diverse operational requirements and risk control needs during maintenance windows, a hierarchical safety zoning method based on lateral distance measurement is designed, categorizing are-as into a core operation zone (layer A), an auxiliary operation zone (layer B), and a non-operation zone (layer C). Combined with a cross-stage feature-coupled real-time identification algorithm for boundary violations, this approach integrates regional prior information with multi-scale target features to perform multi-task analysis, including worker detection, uniform identification, and helmet detection. This facilitates the precise determination of safety equipment compliance and unauthorized boundary incursions. Finally, the effectiveness of the algorithm is validated on the self-constructed RailScapes railway maintenance window dataset. The results indicate that the proposed algorithm effectively addresses the limitations of traditional video surveillance, such as inadequate coverage of blind spots and high manual dependency. By implementing a tiered early-warning mechanism, the algorithm achieves real-time proactive alerts for violations, providing a high-precision, low-power, and highly resilient technical foundation for active safety protection during railway maintenance.

To address the widespread challenges of limited data samples and low diagnostic accuracy in existing switch machine fault diagnosis, this study takes the action power curve, a critical time-series signal of the S700K switch machine, as the research object and proposes a fault diagnosis model based on GAN-BO-BiGRU. First, a small number of power data samples collected by the Centralized Signalling Monitoring system (CSM) are fed into a Generative Adversarial Network (GAN). Through adversarial training between the generator and the discriminator, more sample data are generated to address the issue of data scarcity. Second, a BO-BiGRU fault diagnosis algorithm model is established. The Bayesian Optimization (BO) algorithm is used to determine the optimal values of key hyperparameters for the Bidirectional Gated Recurrent Unit (BiGRU) model, including the number of hidden-layer neurons, the initial learning rate, and the L2 regularization parameter, thereby obtaining the optimal hyperparameter combination. By exploiting BiGRU’s capability to capture information bidirectionally, the proposed model more comprehensively mines patterns from the time-series power data of the switch machine. Finally, simulations are conducted using both the generated data and the original data as samples. The simulation results demonstrate that the data generated by GAN exhibits minimal difference from the original data and can effectively serve as an augmented dataset for fault diagnosis. Moreover, compared to the Long Short-Term Memory (LSTM) model, the BO-BiGRU fault diagnosis model improves the F1 score by 1.77%, indicating its superior ability to extract fault features and its effectiveness in enhancing the accuracy of switch machine fault diagnosis.

To address the issue of reduced control accuracy in high-speed train speed tracking caused by system susceptibility to internal and external disturbances, this study proposes an Active Disturbance Rejection Control (ADRC) scheme for high-speed train speed tracking based on fractional-order integral sliding mode. The scheme introduces improvements to both the Linear Extended State Observer (LESO) and the nonlinear error feedback control law in ADRC. First, in the LESO design, a differential state variable of the total disturbance is incorporated to enhance the disturbance estimation capability of the observer. Second, Fractional Order Integral Sliding Mode Control (FOISMC) is employed to improve the nonlinear error feedback control law, thereby mitigating chattering in sliding-mode control while enhancing tracking accuracy. Finally, a composite fractional-order integral sliding mode ADRC scheme is developed and applied to track a desired speed profile in simulations using CRH3 train parameters. The proposed scheme is compared with other traditional control methods to verify the tracking performance of the control scheme. The results demonstrate that, under identical conditions and external disturbances, the proposed control scheme achieves higher tracking accuracy and stronger disturbance rejection capability than other control schemes, with a maximum speed-tracking error of 0.000 05 m/s.

To address the challenge of extracting fault features from switch machine vibration signals under varying noise conditions, this study proposes a denoising method combining Crested Porcupine Optimization (CPO)-optimized Variational Mode Decomposition (VMD) with Stationary Wavelet Transform Total Variation (SWTTV). First, the vibration signal is decomposed into multiple Intrinsic Mode Functions (IMF) using CPO-optimized VMD. Second, a hybrid criterion combining correlation coefficient and kurtosis is employed to select relevant IMFs. The selected IMFs are then denoised and reconstructed using the SWTTV algorithm. Finally, the method’s performance is evaluated using both simulated test signals and field-collected switch machine vibration signals. Experimental results demonstrate that under different Signal Noise Ratios (SNR), compared to the VMD-WT algorithm, the proposed method achieves an improvement in output SNR of approximately 1 to 6 dB, a reduction in Root Mean Square Error (RMSE) by about 0.03, and an increase in the correlation coefficient with the original signal by approximately 0.01. Furthermore, the proposed algorithm effectively preserves signal features under various operating conditions, avoiding signal distortion. The proposed algorithm exhibits strong generalization capability and robustness, providing a theoretical basis for feature extraction and fault diagnosis of switch machine vibration signals.

To address the intensifying competition between road and rail freight transportation and the increasingly severe loss of rail freight volume, this study investigates the coordinated optimization of off-site warehouse location selection and freight flow allocation. First, it identifies that under existing port logistics models, private enterprises, characterized by fragmented, small-scale, and weak operations, lack the incentive and capacity for rail block train shipments. Second, by comparing traditional port transportation routes with those integrated with off-site warehouses, this study highlights how the off-site warehouse model alleviates inventory pressure and reduces costs for small and medium-sized enterprises with limited demand and financial resources. Third, focusing on the coordinated optimization of off-site warehouse location and freight flow allocation, mixed-integer programming model based on service networks is developed. This model comprehensively considers key factors, such as transportation and construction costs, to optimize off-site warehouse layout and enhance road-rail intermodal efficiency. Finally, an empirical study using the port clearance process of non-ferrous ores at Tianjin Port as a case study validates the model’s effectiveness. Research findings indicate that the off-site warehouse model significantly reduces total transportation costs and increases rail turnover compared to traditional models. Specifically, the single-warehouse configuration reduces total operating costs by 16.63% with a 45.18% rail turnover share, while the multi-warehouse configuration reduces costs by 30.81% and increases the rail turnover share to 95.41%.

To examine the resilience characteristics of urban taxi systems under daily disturbances, this study employs a dynamic threshold method to identify disturbance periods and establishes a three-dimensional resilience assessment framework encompassing resistance, recovery, and adaptability. Nine evaluation indicators tailored to industry operational features, such as empty-vehicle response efficiency, idle mileage redundancy, and spatiotemporal matching balance, are incorporated. Indicator weights are determined using the entropy weight method, and a comprehensive resilience index is constructed. Drawing on three weeks of taxi order data from Zhuhai, an empirical analysis is conducted to explore resilience differences across multiple dimensions, including day type (weekdays vs. non-weekdays) and temporal characteristics (morning peak, evening peak, night peak). Results indicate that the overall system resilience is jointly determined by period composition and demand structure. Weekdays exhibit substantially higher comprehensive resilience than non-weekdays, largely due to the presence of the highly organized morning peak period. Specifically, recovery and adaptability indicators on weekdays exceed those on non-weekdays by 54.75% and 110.18%, respectively. The morning peak exhibits the strongest resilience on weekdays, while the night peak is the most vulnerable. Further analysis reveals that under the same period composition (i.e., only evening and night peaks), weekday resilience performance is weaker than that on non-workdays, highlighting the critical influence of demand patterns on resilience levels. Resistance is primarily driven by empty-vehicle response efficiency and spatiotemporal matching balance; recovery is significantly affected by changes in the order completion rate; and adaptability is closely related to service efficiency per unit distance and cross-regional dispatching efficiency. The proposed assessment framework and empirical findings provide valuable theoretical and practical guidance for enhancing resilience and improving daily operational management of urban taxi systems.

Frequent lane-changing and acceleration-deceleration behaviors in expressway merge areas restrict overall traffic efficiency and compromise driving safety. The geometric design of these areas is critical to traffic operations; specifically, the length of the acceleration lane is a vital design element that directly impacts efficiency and safety. To address issues of low efficiency and frequent accidents in merge areas, this study investigates acceleration lane length. Based on field-measured data, the Kolmogorov-Smirnov (K-S) test is employed to analyze headway distributions across different segments of the outermost mainline lane within the merge area, and the maximum likelihood method is applied to estimate distribution parameters. Subsequently, the acceleration lane length is calculated by integrating gap acceptance theory with the non-sequential merging behavior of ramp vehicles. Finally, VISSIM is utilized to simulate and compare traffic operations of the merge area before and after the optimization of the acceleration lane length. The results indicate that headway distributions exhibit heterogeneity across different segments of the outermost lane. The optimized acceleration lane accommodates the waiting time required for most ramp vehicles to find a merging gap, thereby enhancing safety of vehicle merging. Under level of service 3 conditions on the mainline, ramp merging volume increased by 7.15%-15.11%, while traffic conflicts decreased by 8.68%-22.16%. Under level of service 4 conditions on the mainline, merging volume increased by 5.47%-14.22%, and conflicts decreased by 2.08%-14.74%. Consequently, the optimized acceleration lane improves both traffic efficiency and safety of the merge area, providing a novel approach for calculating acceleration lane length.