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

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

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

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

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

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

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

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.

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

China has built and operated the world’s largest high-speed railway and urban rail networks. With the continuous expansion of network scale and the growing complexity of operating environments, existing automated train operation control systems face diverse challenges in improving efficiency and adaptability, and are increasingly unable to meet the requirements of safe and efficient operations under high-density traffic and dynamic conditions. Autonomous operation control for railway transportation, which integrates perception, low-latency and high-reliability communication, autonomous train control, and intelligent scheduling, is expected to enable safe and efficient train operations in complex environments, thereby facilitating the intelligent development of rail transit. This paper provides a comprehensive review of autonomous operation control technologies for railway transportation. It first summarizes domestic and international research progress and practical applications in this field, clarifies the connotation of autonomy, compares the concepts of autonomy in maritime, road, and railway transportation, and systematically outlines the key technology framework composed of integrated perception, low-latency and high-reliability communication, autonomous train control in complex environments, and intelligent train scheduling. The paper then analyzes the application prospects of these key technologies in the rail transit domain and discusses practical implementations through representative case studies. Finally, it identifies the bottlenecks and challenges in real-world deployment and explores future development directions. This paper aims to provide systematic reference and support for both theoretical research and engineering practice in autonomous operation control of railway transportation.

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

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

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

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

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

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

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

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

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

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

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

To address the challenge of accurately calculating the braking force of eddy current brakes (ECBs) in rail transit, this study proposes a three-dimensional electromagnetic model and analyzes the effects of various parameters on braking performance. First, the expression for static air-gap magnetic flux density is derived using the equivalent magnetic circuit method and Maxwell’s equations. The influence of the transverse edge effect on braking force is examined, and a correction coefficient for the conductivity of the conductor plate is derived. An equivalent eddy current density model is then established, leading to analytical expressions for both air-gap flux density and eddy current density. Subsequently, the impact of the longitudinal end effect on braking force is analyzed, and an expression for the additional air-gap flux density is derived. Based on the relationship between eddy current density and air-gap flux density, an analytical ex-pression for braking force is obtained. Finally, a three-dimensional finite element model is constructed using the design parameters of the Shanghai TR-08 high-speed maglev train’s ECB to verify the accuracy of the proposed model. The three-dimensional electromagnetic model is also used to analyze the influence of different parameters on braking performance. The results indicate that the maximum error between the three-dimensional electromagnetic model and the three-dimensional finite element model is 5.18%, with an average error of 2.35%, validating the accuracy of the three-dimensional electromagnetic model. The braking force increases initially and then decreases with rising speed, exhibiting four distinct characteristics in its variation curve: linearity, criticality, attenuation, and stabilization. The additional braking force increases with speed. Properly widening the conductor plate can effectively mitigates the influence of the transverse edge effect. The braking force increases with higher ampere-turns of the excitation winding and a wider primary core, while it first increases and then decreases with increasing conductivity and permeability of the conductor plate and with greater primary pole distance. The braking force decreases as the air-gap thickness increases, and the speed corresponding to peak braking force also increases. A thinner conductor plate slows the growth of braking force. Both increasing the air-gap thickness and reducing the conductor plate thickness contribute to a more stable braking performance.

This study addresses the coordination imbalance between daily operation and major maintenance of Electric Multiple Units (EMU). First, from the perspective of automated scheduling, the coupling process of EMUs in variable marshaling mode is clarified, and the inherent relationship between EMU operation plans and major maintenance cycles is explored. Building upon this analysis, an integrated optimization model is developed by incorporating balanced maintenance scheduling and comprehensive operational costs into the optimization objectives, while considering constraints such as maintenance capacity, timetable requirements, and operational coupling. Then, heuristic rules are integrated into the feasible solution generation algorithm, which, combined with the optimization strategy of the simulated annealing algorithm, results in an algorithm designed to solve the EMU operation plan and optimize the solution process. Finally, case validation is conducted using timetable data from the Wuhan-Guangzhou high-speed railway. Results demonstrate that the variable marshaling mode reduces the required number of EMUs by 16.9% and decreases routine maintenance frequency by 18.7%, compared to the fixed marshaling mode, effectively lowering overall operational costs. After considering balanced maintenance constraints, the balance of major maintenance in the EMU operation schemes increases by 65%, alleviating issues such as concentrated maintenance demands and insufficient depot capacity in practical operations, thereby enhancing the organizational efficiency of EMU deployment.

To address the common issues in existing unsupervised anomaly detection methods, such as insufficient feature extraction and the inability to effectively focus on anomalous regions, which lead to degraded detection performance, we propose an unsupervised anomaly detection method based on a general vision model and attention enhancement. First, the proposed method utilizes a pre-trained general vision model, the Vision Transformer (ViT), to extract features from input images. Second, to further enhance the model’s focus on abnormal regions, we incorporate the Convolutional Block Attention Module (CBAM), which adaptively adjusts feature weights during the feature extraction stage to more precisely capture local anomalous information. Additionally, extensive experiments are conducted on the MVTec industrial dataset and a self-made cable anomaly dataset to comprehensively evaluate the detection performance of the proposed method. The experimental results demonstrate that the proposed method outperforms multiple state-of-the-art approaches in unsupervised anomaly detection tasks. Specifically, on the cable anomaly dataset, the proposed method achieves an Image-wise AUROC (Image-wise Area Under ROC) and F1-Score of 88.1% and 80.8%, respectively, outperforming the baseline Fastflow algorithm by 11.7% and 7.8%.

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

To address the challenge of accurately predicting container transportation time, this study proposes a hybrid model, CNN-GRU-Attention (CGA), which integrates Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and an Attention Mechanism (Attention). Key factors influencing railway container transportation time, such as transport distance and whether the shipment crosses bureau boundaries, are selected as input features. A sliding window approach is employed to segment the data before feeding it into the model. The CNN-GRU framework is used as the main framework to extract data features and capture long-term dependencies, while the attention module enhances the model’s ability to focus on critical information. The model’s performance is evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R²), and Mean Absolute Percentage Error (MAPE). Representative machine learning and deep learning models are used as benchmarks for comparison. Results indicate that the CGA model achieves an MSE of 77.84, RMSE of 8.82, MAE of 2.72, R ² of 0.958, and MAPE of 4.47%. Compared with other models, the CGA model has demonstrates superior prediction accuracy for railway container transportation time and delivers better overall forecasting performance.

To address the limitations of mixed-vehicle Global Positioning System (GPS) trajectory data in supporting fine-grained transportation demand analysis and modeling, this study develops a vehicle classification model based on the Gaussian Hidden Markov Model (Gaussian HMM). First, travel characteristic indicators are extracted from trajectory data in both spatial and temporal dimensions. A comparative analysis of travel behaviors between trucks and private cars is conducted to identify distinctive classification features. Then, the model is trained and tested using the Baum-Welch (BW) and Viterbi algorithms, and a classification algorithm based on travel characteristics is designed. Finally, an empirical study is conducted using travel trajectory data from trucks and private cars in Beijing. The results indicate significant differences between trucks and private cars across seven indicators: travel start time, travel end time, total travel duration, average dwell time, average trip time, trip frequency, and travel distance. The proposed Gaussian HMM-based vehicle classification model achieves an accuracy rate of 83% for private cars, a recall rate of 82% for trucks, and an overall model accuracy of 79%, demonstrating its effectiveness in vehicle type identification. The research results offer valuable support for refined carbon emission estimation, differentiated demand management policy development, and fine-grained traffic management.

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

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

To explore the nonlinear effects of the built environment on urban rail transit station ridership, this study takes Beijing’s rail transit stations as its focus. Leveraging multi-source data, including Point of Interest (POI) data, mobile phone signaling data, and road network data, the built environment is finely characterized from four perspectives: socio-economic and demographic attributes, land use, multimodal connectivity, and station characteristics. A Gradient Boosting Decision Tree (GBDT) model is employed to reveal the relative importance, nonlinear influences, and threshold effects of these factors on station ridership across different time periods and travel directions. Results indicate that the GBDT model outperforms the Ordinary Least Squares (OLS) model, Adaptive Boosting (AdaBoost), and Random Forest (RF) models in terms of fitting performance. Socio-economic and demographic attributes exert the greatest influence on peak-period ridership, contributing over 40% to both morning inbound and evening outbound peak-period ridership. Land use attributes have the strongest impact during off-peak periods, accounting for 33.06% and 49.10% of inbound and outbound ridership, respectively. The most influential variables exhibit pronounced nonlinear and threshold effects on station ridership. Notably, stations located 15 to 22 km from the city center show significantly higher morning inbound and evening outbound ridership. Additionally, the proportion of passengers accessing rail transit via private cars is considerably higher during peak hours than during off-peak periods. These findings provide valuable theoretical support for the planning of urban rail transit networks and the spatial layout of urban land use.

This study proposes a Metro Freight Network Layout Mathematical Optimization Model (MFNLMOM) to address the issue of urban freight transportation by utilizing the idle capacity of metro and suburban railway lines. It integrates metro, suburban railways, and road networks. First, a multi-objective mathematical optimization model for metro freight network layout is developed, comprehensively considering factors such as transportation costs, node reconstruction and expansion costs, investment expenses, transfer frequencies, and carbon emission costs. The model aims to minimize overall freight costs and transportation time. Next, passenger flow, freight flow, and metro operation logic modules are designed and simulated using Anylogic software to obtain transfer arc segment data within the freight network. Finally, the Gurobi mathematical optimization solver is employed to allocate freight network traffic flow. This is followed by a sensitivity analysis of carbon emission costs, transfer costs, and trans-portation costs. The research findings demonstrate that, compared to traditional road transportation schemes, the metro freight network demonstrates significant advantages, reducing total costs by 55.08% and transportation time by 44.24%. Additionally, compared to the full-station transfer model, the MFNLMOM partial-station transfer model achieves higher computational efficiency.

To address the problem of thermal buckling at joints in longitudinally connected slab ballastless track structures caused by excessive thermal stress accumulation under high-temperature conditions, this study proposes an optimization method for the inter-slab joints in longitudinally connected slab ballastless track. A thermal damage calculation model for longitudinally connected slab ballastless track structures on bridges is established. The matching relationship between the width and elastic modulus of the elastic material used for joint replacement is analyzed. The effects of the equivalent elastic modulus of the joints on thermal stress, thermal deformation, compressive damage at the joints, and interfacial damage are systematically investigated, and a recommended value for the equivalent elastic modulus is proposed. The results indicate that, for a given width of elastic material, a lower elastic modulus leads to greater release of longitudinal thermal stress, while resulting in greater longitudinal deformation of the track structure, increased vertical deformation reduction at the joint, and higher compressive and interfacial damage. It is recommended that the equivalent elastic modulus of the joints be set at 28 400 MPa, corresponding to a total elastic material width of 50 mm and an elastic modulus of 17 324 MPa. Compared to the original track structure, the replacement of both sides of the wide joint with elastic material reduces the longitudinal thermal stress by 14.2%, the compressive damage at the joints by 7.4%, the interfacial damage by 3.2%, and the vertical deformation of the joint by 2.8%.

To promote the coordinated development of metropolitan multi-airport systems and enhance passenger travel experiences, this study investigates the problem of flight coordinative planning based on passenger travel demand. First, considering factors including the geographical distribution of passengers, their preferred travel time, and airport capacity at different periods, an optimization model is developed to determine flight routes and frequencies across the airport system. The objective is to minimize passengers’ ground transportation costs, the deviation from their preferred departure time, and airfare differences. Second, a mathematical programming model is developed, and a variable neighborhood search algorithm incorporating nine problem-specific neighborhood structures is designed. Finally, the proposed method is validated using flight planning cases involving Beijing’s dual-airport system (PEK and PKX) and routes connecting Chongqing Jiangbei, Yuncheng Yanhu, and Foshan Shadi airports. Results indicate that when average ticket prices are identical at both Beijing airports, PKX is allocated significantly fewer flights than PEK. However, when PKX’s average ticket price is reduced by 6% compared to PEK, the arrival and departure flight volumes between the two airports become balanced. Under the fare consistency strategy, 71% of departing passengers and 62% of arriving passengers can choose their preferred airport, while 58.77% of passengers experience travel time deviations within two hours. Although the 6% fare reduction strategy slightly lowers the satisfaction rate of passenger preferences, it attracts more travelers to non-preferred time slots and airports, further balancing passenger flow and flight density. This approach aligns with the dual-hub positioning of Beijing’s two major international airports and contributes to the efficient operation of the overall aviation network.

To address the issues of poor generalization and susceptibility to local optima when using a single Back Propagation (BP) neural network for predicting excavation-induced deformations, this study employs Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for optimization and integrates an Attention mechanism to construct hybrid GA-Attention-BP and PSO-Attention-BP neural network models. The Nanjing Twin Towers excavation project is used as a case study, with PLAXIS 2D simulating the deformation characteristics of the retaining structure and ground surface under 680 different conditions. Additionally, 20 sets of field monitoring data from foundation pits in the Nanjing area are included in the dataset. The prediction results of different neural networks are compared with actual monitoring data under evaluation metrics including Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results demonstrate that the GA-Attention-BP and PSO-Attention-BP models achieve MSE values of 3.47 and 3.22, MAE values of 1.59 and 1.47, and R² values of 0.93 and 0.96, respectively, indicating significant performance improvements over the standard BP and Attention-BP neural networks. Furthermore, the attention-based weight allocation results indicate that excavation depth and diaphragm wall width have the most substantial influence on retaining structure deformation, with weight coefficients reaching 1.33 and 1.17, respectively.

To address the issue of train delays caused by uncontrollable factors during operation, this study investigates the impact of various adjustment strategies under a variable train formation operation mode on both passengers and subsequent train services. First, a train delay adjustment model is developed based on the variable formation operation framework, incorporating constraints such as train formation, arrival and departure times, rolling stock circulation, headway intervals, and passenger flow calculations. Then, the model optimizes four key decision variables, train formation status, dwell time, section running time, and headway, to achieve two main objectives: minimizing the number of stranded passengers and minimizing the deviation between the actual and scheduled timetables. Finally, using Shanghai Metro Line 16 as a case study, three distinct delay scenarios are simulated and solved using the CPLEX solver. The research results indicate that in different scenarios, for shorter delays, there is no need to implement coupling or decoupling strategies. However, for longer delays, these strategies are necessary to mitigate operational impacts. Within the same scenario, allowing coupling and decoupling operations significantly reduces both the impact on subsequent trains and the total number of stranded passengers compared to scenarios where such adjustments are not permitted.

This study addresses the integrated optimization problem of order allocation, processing sequence, and shelf access sequence in a multi-picking-station scenario of an Autonomous Mobile Robot (AMR)-based parts-to-picker picking system. The Order Allocation and Sequencing Problem (OASP) in a multi-picking-station scenario is proposed, which jointly optimizes how orders are assigned to picking stations, the processing sequence of orders at each station, and the shelf access sequence. A mixed-integer programming model is formulated with the objective of minimizing total order picking time. A Variable Neighborhood Search Algorithm (VNSA) is developed, which batches orders based on order similarity to generate a greedy initial solution. The algorithm incorporates four types of local search neighborhoods, including shelf replacement and order reallocation jitter operators, order exchange/insertion, and shelf sequence adjustments, combined with a dynamic switching mechanism to iteratively improve the solution. The performance of VNSA is compared with that of the CPLEX solver. Results demonstrate that VNSA outperforms CPLEX in solution speed and accuracy on small-scale instances, and shows significant improvements in initial solution quality on large-scale instances, verifying the effectiveness of joint optimization of order allocation and sequencing. Moreover, order picking time exhibits a negative correlation with the number and capacity of picking stations, and a positive correlation with the load balancing coefficient.

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.

The proportion and scale of connected commercial vehicles significantly influence traffic flow speed, which in turn affects overall vehicular emissions across the road network. To address the lack of quantitative evaluation methods for the impact of connected commercial vehicle platooning on overall network emission reductions, this study develops a large-scale traffic simulation model based on SUMO to quantify emission reductions under future platooning scenarios. First, a large-scale highway simulation environment is established in SUMO by calibrating against real-world highway data. Next, within a joint SUMO-Python simulation framework, car-following and fuel consumption models for various vehicle types are configured, and emissions are calculated using the MOVES software. Using formation size and penetration rate as variables, platoons consisting of 2 to 8 commercial vehicles are simulated under free-flow conditions to analyze fuel consumption and emissions at different penetration levels. Finally, the study proposes optimal platoon sizes for the target road network at varying penetration rates. The research results indicate that average instantaneous fuel consumption of the platoon decreases as platoon size increases, reaching minimum values at platoon sizes of 4 and 8 vehicles, but rises with increasing penetration rates. Compared to a baseline scenario, higher platoon penetration rates and larger platoon sizes yield maximum reductions of 22.25% in fuel consumption, 21.09% in CO₂, 16.91% in HC, 19.36% in NOₓ, 15.23% in PM2.5, 17.39% in PM10, and 26.06% in CO emissions. Both emissions and fuel consumption decline as penetration rates rise; under fixed penetration, they further decrease with larger platoon sizes. Overall, increasing both penetration rate and platoon size leads to significant reductions in total fuel consumption and pollutant emissions from road traffic flow.

This study investigates the dynamic request matching problem in the ridesharing operations of electric Vertical Take-Off and Landing (eVTOL) aircraft, with a focus on matching and route planning. First, a dynamic route planning model based on ridesharing fairness is first developed, aiming to maximize the benefits of both passengers and eVTOL operators. The model incorporates key constraints such as vertiport capacity, eVTOL payload, and battery energy consumption. Second, two solution approaches, the basic insertion algorithm and the linear insertion algorithm, and two request handling strategies, namely first-come-first-served and request priority, are compared for their effectiveness in matching new requests to available eVTOLs. Finally, a case study is conducted using real geographic data from five train stations and one airport in City T, designated as vertiports. The results show that the linear insertion algorithm reduces computation time by over 60% compared to the basic insertion algorithm, demonstrating its computational efficiency. Furthermore, compared to the first-come-first-served strategy, the request priority approach decreases the average passenger payment by 0.87% and increases operator ridesharing revenue by 5.86%, achieving a more optimal matching between new requests and eVTOLs while balancing the interests of passengers and operators. The proposed dynamic route planning model provides valuable insights for the development of shared eVTOL operation systems.

To investigate the competitive impact of the rapid expansion of high-speed rail on civil aviation and to further enhance the structural stability of the air-rail intermodal transportation network, this study proposes a vulnerability assessment and restoration strategy that incorporates competitive effects. First, based on the dynamic equilibrium relationship between passenger travel demand and transportation supply, an air-rail competition effect index is introduced to improve the network vulnerability assessment model. Second, in response to various attack strategies and failure scenarios, a restoration model is developed with the dual objectives of minimizing both restoration cost and network vulnerability, and is solved using a particle swarm optimization algorithm. Finally, a multi-scenario comparative analysis is conducted using China Eastern Airline-China Railway intermodal network as a case study to determine optimal restoration sequences under different scenarios. The results indicate that incorporating the air-rail competition effect index increases the average node vulnerability index by approximately twofold, significantly improving the identification and accuracy of critical nodes. By classifying city nodes according to their vulnerability levels, 15 severely vulnerable cities, including Shanghai, Nanjing, Guangzhou, Shenzhen, and Xiamen, are identified. Under deliberate attack scenarios, node and regional failure restoration strategies yield the best results, with more balanced traffic distribution, a 23% increase in restoration cost, and more than a twofold improvement in overall network performance.