In large-scale high-speed rail networks (HSRNs), the occurrence of occasional malfunctions or accidents is unavoidable. The key issue considered in this study is the optimal allocation of the maintenance costs, based on the stochastic risk assessment for HSRNs. Inspired by the theoretical risk evaluation methods in the complex network, three major factors, including the local effects, global effects, and component self-effects are considered in the process of assessing the impact on the network components (nodes or lines). By introducing the component failure occurrence probability, which is considered to be an exponential function changing with the component maintenance costs, a feasible stochastic risk assessment model of the HSRNs together with the component impact assessment is proposed that can better unify the impact assessment of both the high-speed rail stations and railways. An optimal allocation algorithm based on a Lagrangian relaxation approach is designed. Correspondingly, the optimal cost allocation scheme can be determined using the algorithm to eliminate the various HSRN risks under the given costs. Furthermore, a real-world case study of the HSRNs in eastern China is illustrated. Compared with the genetic algorithm, the simulation shows that the approach can solve the optimal cost allocation problem to more effectively reduce the risks of large-scale HSRNs in practice.

Obtaining the standing positions of passengers on the subway platform can provide data support for more accurate passenger flow guidance measures. This paper utilizes binocular vision technology to acquire a depth matrix of the platform scene and employs the YOLOX object detection algorithm to identify the positions of passengers’ heads within the depth matrix. The depth values of the center points of passengers’ heads are extracted, and then, through coordinate mapping, these depth values are converted into a platform coordinate system, allowing us to determine the positions of passengers on the platform and subsequently obtain the distribution data of passengers at the station. The distribution data of passengers can provide more accurate information for passenger flow guidance.Due to the lack of binocular image data for underground platform layers in existing public datasets, this study constructs an underground island platform model using Blender software and generates a dataset containing 200 test scenarios. Validation of the proposed model using this dataset shows that 91.9% of the predicted standing positions of passengers exhibit an error of less than 41.8 centimeters, with an average prediction error of 18.1 centimeters and an average computation time of 860 milliseconds. Furthermore, the model’s performance in real subway scenarios has been verified, demonstrating its effective performance in real-world environments.

The capability of railway emergency rescue can be enhanced by maintaining the railway emergency rescue network and upgrading its technology. Nowadays, influenced by the factors, such as resource type, personnel distribution, line level, etc., space-time differences may be unavoidable. In the meantime, the general description method of the transportation network may lack the consideration of the rescue transportation particularity, so the strategies of resource allocation, maintenance, and upgrading could be illogical. Hence, in this paper, the gravity model is utilized to improve the classical travel time budget model and to construct the space-time accessibility model, firstly. Then, further exploring the space-time accessibility of nodes and edges of railway emergency rescue network and considering the randomness of travel time, a space-time accessibility measurement method for an emergency network is proposed. Moreover, a global optimization model with accessibility characteristics is then constructed for the maintenance allocation of the emergency rescue transportation network. The results show that the proposed method can solve the maintenance allocation problem of the large-scale rescue network effectively, reduce the risk of maintenance allocation strategy failure caused by unreasonable node index parameters, and provide an effective basis and theoretical support for the rational formulation of railway rescue transportation network maintenance allocation strategy.

Accurate access to real-time passenger flows on subway platforms helps to refine management in the era of networked operations. The narrow subway platforms suffer from significant crowd scale discrepancies and complex backgrounds when counting passenger flow. In the proposed passenger flow counting algorithm, the feature-enhanced pyramid structure is used to retain the channel information of deep features and eliminate the aliasing effect caused by fusion to enhance the feature representation of the original image and effectively solve the scale problem. The mixed attention mechanism suppresses background interference by capturing the global context relationship and focusing on the target area. On the ShanghaiTech Part_A dataset, the mean absolute error (MAE) and mean square error (MSE) of the proposed algorithm are 2.3% and 1.4% higher than those of the comparison algorithm, respectively. The MAE and MSE on the self-built platform dataset reach 3.1 and 5.7, respectively. The experimental results show that the accuracy of the proposed algorithm is improved and can meet the counting requirements of the subway platform scene.

Under degraded operations in urban rail transit systems, the coordinated state between passengers and trains undergoes significant changes due to factors such as train frequency reduction. This creates a critical mismatch between passenger demand and transport capacity, leading to rapid passenger accumulation at stations and potential safety hazards from abnormal crowding and systemic congestion. This study develops an integrated simulation model that combines train schedules with passenger arrival patterns to quantify how altered departure sequences generate supply-demand imbalances. A bounded rationality route choice model incorporating four decision-making dimensions – time value, transfer burden, service degradation impact, and route familiarity – captures passenger behavioral adaptations during degraded operations. The experimental results indicate that under abnormal train operation scenarios, the model incorporating bounded rationality assumptions is closer to actual observed values compared with the fully rational benchmark model. In the L2 degradation scenario, the deviation between the bounded rationality model and the actual values is controlled within 0.6%–1.3%, whereas the deviation of the fully rational model is approximately 12%. Concurrently, passenger flow propagation intensity in core sections rises from 0.3 to 0.7, showing a clear evolution from localized accumulation to regional diffusion. The research provides an effective methodology for identifying passenger flow dynamics under degraded operational scenarios and supports decision-making for service optimization and passenger flow management.

Under emergencies or fault disruptions, dynamic changes in the passenger–train coordination relationship are encountered by urban rail transit systems, which can easily lead to abnormal passenger congestion and systemic overcrowding, affecting service reliability and operational efficiency. To study the spatiotemporal evolution patterns of passenger flow under abnormal operational modes, factors such as time delays and transfer burdens are considered by introducing a degraded utility function and path familiarity preference model. A limited rationality Path Re-selection model influenced by multi-source data is constructed to reveal the path re-selection behavior of passengers during abnormal operations caused by sudden events. At the same time, a passenger–train coordinated passenger flow simulation model is constructed, considering operational modes, train intervals, and dynamic passenger travel paths. The proposed model is verified through simulation experiments using the Hangzhou Metro as a case study. The results show that passengers' Path Re-selection behavior under abnormal operational modes exhibits limited rationality characteristics. As the severity of abnormal operations increases, the rate of path re-selection significantly rises, leading to a decrease in passenger flow at core stations and a transfer of flow to adjacent sections. The intensity and range of Passenger Flow Propagation also expand simultaneously. Further analysis reveals that limited rationality in path re-selection accelerates the propagation process of passenger flow, enhances its spread intensity, extends its impact range, and shortens the response delay, thereby significantly altering the spatiotemporal evolution patterns of passenger flow.