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.

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 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 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 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 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 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.

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.

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 spatiotemporal mismatch between energy supply and demand caused by the spatiotemporal heterogeneity of charging loads in dual-sided expressway service areas, this study proposes a coordinated scheduling method for multi-source energy flows based on direct current interconnection technology. Utilizing actual traffic flow, charging load, and equipment configuration data from the Hancunhe Service Area on the Jingkun Expressway, this study first analyzes the spatiotemporal distribution of traffic flow and charging demand, as well as their coupling characteristics. Second, it constructs a bidirectional energy mutual-support channel between the dual-sided service areas and establishes an hourly optimization model on a daily timescale. The model aims to minimize carbon emissions, maximize economic benefits, and minimize power grid fluctuations, and is solved using the Gurobi solver. Finally, three scheduling schemes, independent operation, Photovoltaic-Storage-Charging (PSC) self-consistency, and Photovoltaic-Storage-Direct Current-charging (PSDC) integration, are established for comparative simulation. Furthermore, a sensitivity analysis is conducted concerning temporal characteristics and weather conditions. The results demonstrate a significant synchronous fluctuation between traffic flow and charging demand on a daily timescale, with spatial load discrepancies between the dual-sided areas becoming notably more pronounced during holidays and other high-travel-intensity scenarios. Compared to the independent operation and PSC self-consistency schemes, the PSDC integration scheme achieves the best comprehensive performance. Specifically, daily carbon emissions are reduced by 71.2% and 41.8%, economic benefits are increased by 17.1% and 7.6%, grid power fluctuations are decreased by 94.6% and 66.7%, and comprehensive economic costs are lowered by 23.4% and 9.0%, respectively. Furthermore, this scheme exhibits strong adaptability under various temporal charging load distributions and photovoltaic output conditions. It performs optimally in typical scenarios characterized by significant spatial load discrepancies, such as peak outbound holiday travel days, peak inbound holiday travel days, and Sundays, where the cross-area mutual-support energy reaches 438.5 kWh, 236.9 kWh, and 166.3 kWh, respectively. These findings provide a valuable reference for the operational scheduling and low-carbon optimization of integrated PSC systems in dual-sided expressway service areas.

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 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.

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 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 susceptibility of ground-to-train information transmission in track circuits to interference from traction currents in electrified railway traction power supply systems, this study employs finite element simulation software to develop a ground-to-train information transmission model and investigates the electromagnetic field characteristics of the information transfer process between the track circuit and the Track Circuit Reader (TCR) antenna. First, a simulation model of ground-to-train information transmission is established based on the operational principles and installation configuration of the track circuit system. Second, the model’s validity is confirmed by using track circuit signals as the excitation source and comparing key parameters of the TCR antenna’s induced voltage, including peak value, carrier frequency, and low-frequency modulation. Finally, measured traction currents are applied as excitation sources to analyze the electromagnetic interference effects on ground-to-train transmission under both balanced and unbalanced traction current conditions. Simulation results indicate that with measured data as the excitation source, when traction current in the rails is balanced, the peak induced voltage at the TCR antenna is approximately 1 mV, and traction harmonic currents cause no interference to the track circuit system. However, when the peak value of the unbalanced current in the rails reaches 1.1 A, the maximum peak induced voltage of the TCR antenna reaches 220 mV, generating interference signals within the operating frequency band of the track circuit. These findings provide valuable theoretical guidance and practical reference for electromagnetic compatibility design of electrified railway signaling systems and for enhancing the anti-interference performance of TCR equipment.

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 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.

To address the susceptibility of existing railway track foreign object intrusion detection algorithms to complex environments, which leads to low detection accuracy and high computational overhead, this paper proposes a lightweight detection method named Railway Lightweighting-YOLO (RL-YOLO). Based on an improved YOLOv12n architecture, this method is designed to facilitate lightweight deployment in complex environments. First, the High Performance GPU NetV2(HGNetV2) is introduced to replace the original backbone network, which is further optimized by integrating DWConv and GhostConv. This integration achieves dual-dimensional collaborative optimization, specifically, the decoupling of spatial convolution from channel fusion, and the generation of cost-effective “ghost” features alongside core features. This approach significantly reduces the number of model parameters and floating-point operations while enhancing the integrity of feature representation. Second, Lightweight Asymmetric Dual-Head (LADH) is adopted to further streamline the model for lightweight deployment. Third, an Adaptive Fine-Grained Channel Attention (AFGCA) mechanism is incorporated to improve the model’s capacity to interpret complex scenes. Finally, the Wise Focaler ShapeIoU (WF-ShapeIoU) loss function is employed to compel the model to focus more effectively on complex samples, thereby improving its localization capability. Experimental results demonstrate that the proposed method achieves a mean average precision (mAP@0.5) of 89.1% in complex environments, representing a 2.6% improvement over the baseline. The processing speed reaches 159 frames per second, an increase of 21 frames per second. Furthermore, the number of model parameters is reduced by 41.7%,GFLOPs is decreased by 34.5%. These results verify that the method successfully meets the demands for both real-time performance and lightweight deployment.

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 improve the adaptability of high-speed train operation plans to daily passenger flows, this paper proposes an optimization method for high-speed train operation planning that considers the availability of unreserved tickets. Recognizing that not all passengers are willing to accept unreserved tickets, it is first necessary to identify the target demographic for this ticket type. A Logit model is established to analyze passenger acceptance of unreserved tickets when reserved tickets are sold out. Simultaneously, Origin-Destination (OD) passengers are categorized into two groups: those who exclusively accept reserved tickets and those who are willing to accept unreserved tickets. Passenger flow is allocated separately for these two groups during the subsequent solution process. Second, an optimization model is constructed with the dual objectives of minimizing passengers’ generalized travel time and railway operational costs. This is subject to constraints ensuring that the quantities of both ticket types meet diverse travel demands and that the ticket allo-cation in each section complies with train capacity limits. To account for generalized travel time, a time penalty coefficient is applied to the travel time of passengers purchasing unreserved tickets; this penalization aims to minimize unreserved ticket purchases, thereby safeguarding passenger comfort while satisfying the load factor requirement. Given the multitude of variables and the large scale of the decision-making model, a hybrid approach combining a Genetic Algorithm (GA) and the Gurobi solver is developed. The Gurobi solver is utilized to separately allocate passenger flow for reserved and unreserved ticket holders, simultaneously determining the ticket allocation scheme and the objective function values. These results then serve as fitness values for the subsequent GA operations. Finally, a case study based on the Beijing-Shanghai high-speed railway is conducted. The results demonstrate that, compared to operation plans that do not offer unreserved tickets, the proposed plan reduces total passenger travel time by 3.15% and railway operation costs by 8.71%. Furthermore, the ticket allocation scheme aligns closely with passenger demand, verifying the effectiveness of the proposed model and algorithm.

To address the irregularity characteristics of track spectra at different speeds, this study proposes a method for comprehensively evaluating track quality by combining the track irregularity quantile spectrum with the Track Quality Index (TQI). First, based on measured track irregularity data from four high-speed railway lines in East China, the improved Welch method is employed to calculate the power spectral density of track irregularities. The structural characteristics of the track irregularity power spectra at speeds of 200, 250, 300, and 350 km/h are then analyzed. By comparing these with classic domestic and international reference spectra, the structural differences across various speed grades are discussed. Second, the Chinese standard spectrum formula is used to fit the track irregularity data at the four speeds, and the adaptability of the fitted spectral parameters to the current standard spectral parameters is evaluated. Finally, the relationship between different individual standard deviations and the track quantile spectra is analyzed. By calculating the areas of the track quantile spectra at each level, the correlation between the track quantile spectra and the TQI is established. The results indicate that the variation trends of the track spectra at all speed levels are consistent with the current normative standard spectra; however, the spectral parameters exhibit certain discrepancies. Specifically, for vertical irregularities at wavelengths above 10 m and alignment irregularities at wavelengths above 22 m, the spectral lines are significantly elevated compared to the standard spectra, accompanied by substantial changes in the spectral parameters. This suggests that after years of high-speed railway operation, the standard spectral parameters require corresponding revision and refinement. The percentile spectra are divided into four intervals: 0-30%, 30%-70%, 70%-90%, and 90%- 100%, corresponding to excellent, good, fair, and warning track conditions, respectively. Based on actual line conditions, reasonable quantile spectra can be constructed and combined with the TQI to facilitate the comprehensive quality management of high-speed railway tracks.

To address the low prediction accuracy of the Track Quality Index (TQI) in heavy-haul railways, this study proposes an analysis method based on machine learning. First, a raw dataset comprising 303 381 track inspection and maintenance records collected between 2015 and 2023 from 2 906 sections of a heavy-haul railway operated by a major energy enterprise is utilized. Following data cleaning, the dataset was categorized into four track section types based on geometric characteristics: neither gradients nor curves, curves without gradients, gradients without curves, and both gradients and curves. Second, the data for each category were processed into sequential datasets spanning three consecutive months without maintenance interventions. Sequences exhibiting an excessively large range in the TQI were then eliminated using anomaly detection methods. Third, using the TQI and its seven components (left-rail longitudinal level, right-rail longitudinal level, left-rail alignment, right-rail alignment, gauge, cross level, and track twist in triangle form) from the first two months of each sequence as inputs, six machine learning models, such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), and linear regression, are leveraged to perform rolling predictions of the TQI for the third month. Finally, the predictive performance of the different models across various section types is comparatively analyzed. The results indicate that when using the six machine learning models to predict the TQI across the four section types, the coefficient of determination R² between the measured and predicted values exceeds 0.94 for all models, suggesting highly accurate and reliable prediction performance. Among the six models, the RNN demonstrates particularly outstanding performance, achieving an average R² of 0.967 5 with a standard deviation of ±0.000 8 across various test sets. Specifically, for the four representative track sections, the prediction R² values are 0.990 1, 0.974 3, 0.988 3, and 0.988 9, respectively. Compared to the other five models, the RNN yields more stable prediction results and exhibits greater adaptability to diverse scenarios. This study demonstrates that machine learning methods effectively meet the demands of TQI prediction in heavy-haul railways, providing a valuable reference for establishing scientific tamping maintenance schedules and advancing intelligent track maintenance practices.

To address the difficulty of optimizing capacity in the collaborative scheduling of express and local trains for urban rail transit under the constraint of fixed express timetables, this paper proposes a coordinated optimization method for train timetabling. Based on the characteristics of publicly available express timetables and the spatiotemporal dynamics of passenger demand in actual operations, a mixed-integer programming model is developed. The model aims to minimize total train operating mileage, subject to the constraints of satisfying passenger travel demand. First, passenger travel characteristics are portrayed using sectional passenger demand, and operational constraints, such as train arrival and departure headways, station dwell times, and turnaround operations, are es-tablished. Second, the correlation between local train timetables and rolling stock circulation plans is formulated to achieve the integrated modeling of the timetable and rolling stock circulation. Third, given the large number of variables and the strong coupling of constraints in the model, an engineering application algorithm named Fast Solution Algorithm for Engineering-Stop Skip (FSAE-SS) is designed. By leveraging the structural characteristics of the model, the constraints are decomposed, and an iterative process is employed to achieve a joint solution for both the timetable and the rolling stock circulation plan. Finally, an empirical analysis is conducted using Line 10 of an urban rail transit system in Southwest China to verify the practical effectiveness of the model and algorithm. The results demonstrate that, compared to a traditional genetic algorithm, the proposed method reduces total operating mileage by 5.6% and maintains stable solution performance under large-scale constraints. Furthermore, compared to the actual operational timetable, the total operating mileage is reduced by 8.1%, achieving a better match between transport supply and passenger demand. The constructed collaborative optimization model successfully coordinates the local train operation plan with rolling stock circulation under fixed express timetable constraints. This provides an implementation pathway for capacity optimization that ensures timetable stability, as well as methodological support for cross-line collaborative scheduling under complex rail net-work conditions.

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%.

Traditional single-source data-based fault diagnosis methods for railway switch machines are limited by insufficient fault feature representation from a single data modality. When dealing with complex fault types, these methods often suffer from low diagnostic accuracy and an inability to precisely and finely locate faults, making them inadequate for practical applications. To improve the diagnostic performance of the ZYJ7 electro-hydraulic switch machine, this paper proposes a deep learning-based fault diagnosis method based on feature-level multi-source data fusion using real-world oil pressure, current, and power data. First, multi-modal data from oil pressure, current, and power signals are normalized, denoised, and subjected to spatiotemporal feature extraction. Then, bilinear interpolation is employed to unify the dimensions of feature matrices across different modalities, and an attention mechanism is introduced to achieve dynamic weighted fusion of these feature matrices. Finally, the fused features are fed into a hybrid neural network composed of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for classification. The proposed method is compared with single-source data models, simple voting methods, simple concatenation methods, and fixed-weight fusion models. Experimental results show that the proposed feature-level multi-source data fusion model improves diagnostic accuracy by 7.19%, 4.38%, 3.13%, and 1.88%, respectively, demonstrating its effectiveness and providing a valuable reference for fault diagnosis of railway switch machines.

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.

Existing vehicle trajectory optimization methods based on deep reinforcement learning struggle to achieve the dynamic merging and splitting of platoons in connected, mixed-traffic environments. Simultaneously, conventional signal timing optimization, which operates at the cycle or phase level, is hindered by response lag. The decoupled control between these two elements restricts further improvements in intersection efficiency. To address the necessity for the collaborative optimization of flexible vehicle trajectory control and real-time signal timing, this paper proposes a joint optimization method for platoon trajectories and signal timing based on Proximal Policy Optimization (PPO), termed FT-SIWO (Fleet Trajectory and Signal timing based on Waiting Offsetting). First, the vehicle trajectory optimization problem is formulated as a markov decision process. A state space integrating local vehicle dynamics and global traffic state variables, an action space encompassing car-following and multiple acceleration modes, and a multi-dimensional reward function considering factors such as driving comfort and safety are designed. This formulation enables the agent to comprehensively perceive the environment and learn efficient policies. Subsequently, the PPO-clip algorithm is employed for policy updates to enhance both training stability and sample efficiency. Finally, a real-time signal timing optimization mechanism based on Waiting Offsetting (WO) is introduced. By dynamically calculating and balancing the “time loss” of vehicles in the green phase and the “time surplus” of vehicles in the red phase, this mechanism enables the dynamic adjustment of the green light duration at a second-level granularity, thereby achieving deep coordination with the vehicle trajectory optimization. Simulation results on a joint SUMO and Matlab platform demonstrate that, compared to a non-optimized baseline scheme, FT-SIWO reduces the average vehicle delay at intersections by 28%-32% and the average waiting time by 30%-35%. Additionally, it decreases the total emissions of various pollutants (CO₂, CO, HC, NO _x, and PM _x ) by 34%-38% and reduces fuel consumption by 19%. Furthermore, when compared to state-of-the-art distributed control or vehicle-infrastructure cooperation schemes, FT-SIWO achieves an additional 6%-10% improvement in control effectiveness. By enabling intelligent platoon formation and a second-level linkage with signal timing, this method effectively enhances green time utilization and the overall operational efficiency of traffic flow. It provides a viable collaborative optimization solution for smart urban traffic management systems to handle mixed traffic flows, alleviate congestion, and reduce emissions.

To address traffic congestion in the merging area of urban expressway on-ramps, this paper takes the Zhongshan North Road on-ramp of the Xuzhou North Third Ring Expressway as an empirical case study. A modular Deep Reinforcement Learning (DRL) framework integrating a Squeeze-and-Excitation Network (SENet) attention mechanism is proposed to optimize ramp signal control, formulating this dynamic and complex control problem as a Markov Decision Process (MDP). A comprehensive state space is designed, encompassing the grid-based traffic flow distribution characteristics of the merging area, signal phase states, temporal features, and the real-time traffic volume of Connected and Automated Vehicles (CAVs). Furthermore, a multi-objective reward function is constructed to balance real-time microscopic feedback with long-term system objectives. To ensure the simulation environment closely reflects actual traffic scenarios, an orthogonal regression method is adopted to calibrate the traffic flow model during the construction of the road network in the Simulation of Urban MObility (SUMO) microscopic traffic simulation platform. Consequently, the discrepancies between the simulated lane-changing ratios at the upstream, mid-section, and downstream segments of the merging area are controlled within ±10%. compared with field monitoring data. During the training phase, a Double Deep Q-Network (DDQN) is employed alongside prioritized experience replay and a progressive traffic flow learning strategy to enhance learning efficiency and stability. Experimental results indicate that, compared to traditional fixed-time signal control, the proposed method reduces the average delay rate by 40.5% and shortens the queue length by 29.5% during peak hours, outperforming the baseline DDQN model. Furthermore, as the CAV penetration rate increases from 20% to 80%, the average delay rate decreases by an additional 44.7%, demonstrating the model’s excellent congestion mitigation capabilities and strong adaptability in mixed traffic environment.

This study investigates critical node identification in urban rail transit networks to address the limitations of existing methods, which often overlook community structure and differences in macro-level importance, thereby failing to comprehensively characterize the roles of nodes in both overall network organization and local connectivity. First, a critical node identification model integrating community structure and multi-scale node centrality is constructed. An improved Louvain algorithm based on consensus clustering is adopted to partition the rail transit network into communities, which are then abstracted as super-nodes. Subsequently, a weighted PageRank algorithm is used to quantify the macro-level importance of these communities. Second, at the node level, two micro-level centrality indicators, namely node strength and weighted betweenness, are selected. Grey relational analysis is applied to determine the indicator weights, and the macro-level community importance is introduced to revise the initial node importance, thereby establishing a comprehensive node importance evaluation model. Finally, an empirical analysis is conducted using the Beijing rail transit network as a case study. Deliberate attack simulations are performed to verify the model’s identification efficacy and the rationality of the node rankings. The results indicate that the Beijing rail transit network exhibits significant community structure characteristics, with varying macro-level importance across different communities. The identified critical nodes are primarily concentrated at hubs featuring multi-line intersections and cross-regional connections, displaying distinct spatial agglomeration. These nodes play a vital role in maintaining network connectivity, cross-regional links, and overall network stability. Under deliberate attacks simulated according to the model’s identification results, network performance degrades more rapidly. This demonstrates that the proposed model can effectively identify nodes with a critical impact on network stability, providing a novel analytical perspective and methodological reference for critical node identification, resilience enhancement, and the operational management of urban rail transit networks.

To alleviate the mismatch between flight supply and passenger demand, this paper proposes an optimization model for flight supply-demand alignment that incorporates passenger choice behavior. Based on 519 valid choice experiment observations, a Multinomial Logit (MNL) model is employed to characterize the heterogeneous preferences of business and leisure passengers regarding departure time, ticket price, aircraft type, and flight durations. These preferences are translated into differentiated flight demands at the time-slot level to accurately reflect the market’s behavioral responses to flight supply. First, considering multiple constraints such as flight schedule, aircraft assignment, and aircraft maintenance, a bi-objective optimization model is constructed. This model aims to optimize flight schedule allocations and aircraft assignments to minimize both the deviation in market demand fulfillment and operational costs. Second, a hybrid Greedy-Genetic Algorithm (GGA) is adopted to solve the model. By combining the global search capability of a Genetic Algorithm (GA) with the local repair and optimization abilities of a greedy strategy, this approach ensures optimization efficiency and high solution quality. Finally, a case study based on a one-week flight schedule from Airline A is conducted. The results indicate that the optimization scheme significantly improves the match between flight supply and demand, reducing the deviation in market demand fulfillment by 34.3% and lowering operational costs by 12.1%. Furthermore, flight resource utilization is enhanced, and the flight plan’s capacity to accommodate passenger demand is notably improved, particularly during high-demand periods. By comprehensively accounting for the relationships among flight schedules, aircraft types, and passenger demand, the optimization model ensures both the economic viability and operational feasibility of the flight plan. Overall, the proposed optimization model, which integrates passenger choice behavior, holds substantial application value for enhancing the utilization efficiency of flight resources and market responsiveness.

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.

Pedestrian intrusion into railway perimeters poses a serious threat to train safety. To address the high false-negative rate of single-image pedestrian detection algorithms based on deep learning in complex railway environments, this paper proposes a railway perimeter intrusion detection algorithm that combines YOLOv5x single-frame detection results with pedestrian trajectory correlation in image sequences. First, high-confidence pedestrian targets from single-frame images are integrated with three types of features, including spatial relationships of trajectories, trajectory irregularity information, and matching results between detections and trajectory predictions, to achieve reliable pedestrian trajectory updating and filtering. Second, the trajectory-correlated prediction results are used to reset the confidence scores of low-confidence pedestrian targets in single-frame images, thereby improving detection performance in challenging railway environments such as nighttime and adverse lighting conditions. Furthermore, a pedestrian intrusion dataset covering five typical railway scenarios is constructed, including cases prone to missed detections, such as occluded objects, blurred targets, and distant small objects under low-light or glare conditions. Finally, to further validate the effectiveness of the proposed algorithm, Faster R-CNN and YOLOv3 are employed as detectors to assess the performance improvement. Experimental results demonstrate that, compared to the single-frame detection algorithm YOLOv5x, the proposed method improves recall by 4.5% and reduces the log-average miss rate by 4.0% without significant loss in detection precision, proving the efficacy of the trajectory updating, filtering, and confidence reset modules. Additionally, compared to Faster R-CNN and YOLOv3, the proposed algorithm achieves recall improvements of 2.3% and 3.6%, respectively, and reduces log-average miss rates by 1.3% and 3.5%, confirming its adaptability to different detection models.

To investigate the relationship between the built environment and the carbon emission reduction benefits of bike-sharing, a Geographically Weighted (GW) machine learning model is proposed to analyze the nonlinearity and spatially heterogeneous impacts of the built environment. Based on bike-sharing order data from Shanghai and Xi’an, and integrating life-cycle carbon emission accounting, a “5D” built environment indicator system is established. The GW analysis is incorporated into the eXtreme Gradient Boosting (XGBoost) model, utilizing a bi-square kernel function to calculate spatial weights, thereby constructing the GW-XGBoost model. Localized weighted training is then utilized to capture the nonlinearity and spatial heterogeneity of the relationships among variables, facilitating an exploration of the nonlinear mechanisms by which the built environment influences the carbon emission reduction benefits of bike-sharing. The results indicate that the average carbon emission reduction per kilometer of bike-sharing in Shanghai and Xi’an is 51.09 g and 44.85 g, respectively. The relative importance of built environment variables differs between the two cities, and key variables exhibit nonlinear threshold effects. For instance, when population density is below the threshold of 40 000 people/km², it exerts a positive impact in both cities. However, when the density exceeds this threshold, the impact becomes insignificant. Furthermore, spatial heterogeneity analysis reveals significant spatial variations in the impacts of built environment variables. In Shanghai, for example, the marginal carbon emission reduction benefit of population density peak is highest in the central urban area, with SHapley Additive exPlanations (SHAP) values ranging from 10.01 to 47.52. Conversely, the effect turns negative in suburban areas, with SHAP values ranging from -80.25 to -50.01. Specifically, for every increase in population density of 10 000 people/km², carbon emission reduction can increase by up to 47.52 kg in the central urban area, while it can decrease by up to 80.25 kg in suburban areas. These findings provide a theoretical basis for formulating low-carbon development policies for bike-sharing systems.

To address the challenges in time series anomaly detection tasks, including label scarcity, high false positive and false negative rates, and model performance vulnerability to seasonal and long-term trend variations, this paper proposes a Time-Frequency Cross-Fusion-Guided Dual-Stream Clustering Anomaly Detection Method (TFCDC). First, time-domain and frequency-domain feature vectors are extracted separately, and a cross-fusion mechanism is employed to enable complementary modeling of multi-scale information, yielding a joint time-frequency feature representation. Second, the time-frequency features are encoded using a Long Short-Term Memory (LSTM) network and a linear transformation layer to produce two latent variables. Third, a two-stage clustering strategy is applied in the latent spaces to aggregate normal samples while distinguishing anomalous ones, which effectively reduces false positive and false negative rates. Finally, comparative experiments are conducted against state-of-the-art baseline models on six benchmark anomaly detection datasets. The experimental results demonstrate that the proposed TFCDC model outperforms mainstream baselines on multiple dataset, achieving an average F1-score improvement of 14.5% across the six datasets and a notable 32.8% improvement on the ultra-high-dimensional, ultra-long-sequence WADI dataset compared to the baseline. These findings confirm that TFCDC exhibits superior accuracy and robustness, effectively mitigating the interference from long-term trend variations and anomalous data.

To address the challenges of numerous construction procedures, complex stress conditions, and difficulty in controlling surrounding rock deformation at the intersection of the No. 2 cross-tunnel and the main tunnel of the Qunke Tunnel on the Xi’an-Chengdu Railway, a roof-breaking excavation scheme and associated mechanical analysis are investigated. First, a comprehensive roof-breaking excavation and support scheme consisting of “cross-tunnel climbing + reinforcing ring support + temporary-to-permanent support + symmetrical excavation and support” is proposed. Subsequently, a numerical model is established using Midas finite element software to finely analyze the dynamic mechanical response characteristics and three-dimensional spatial effects of the support structure and surrounding rock during the roof-breaking construction process. Finally, combined with field real-time monitoring data, the analysis focuses on the stress variation characteristics of the support structure and surrounding rock at the tunnel intersection, as well as crown settlement and lateral convergence deformation patterns around the tunnel periphery. The results show that during construction, the stress of the primary support structure remains within the range of 0.15 MPa to 0.76 MPa, and the maximum axial force in the steel arch frame is 343 kN. This support system effectively reduces the stress and settlement deformation of the primary support induced by surrounding rock pressure. Construction of the intersection induces stress redistribution in the surrounding rock near the reinforcing ring, making this area prone to stress concentration. The maximum crown settlement at Sections 1-3 is 1.12 mm, and the maximum lateral displacement at the arch springline is 1.06 mm; both values are relatively small, indicating that the intersection remains in a stable and controllable state overall. During the excavation and support process of the left-side upper bench at the intersection, the left and right arch springlines and the right arch shoulder transition from compression to tension. These locations should be closely monitored during construction, and timely support should be provided to prevent local rockfalls, ensuring that the entire construction process remains safe and stable. The research findings can provide important references for roof-breaking construction, support structure design, and surrounding rock stability control at tunnel cross-tunnel and main tunnel intersections under similar complex conditions.