When the subway network system has insufficient resilience and strong vulnerability, the network operation function will be greatly attenuated or even lost due to the failure of key nodes or lines. Evaluating the vulnerability of the metro network is one of the keys to prevent and control risks. This paper proposes an entropy weight multiple criteria decision‐making evaluation method that combines the vulnerability indexes of agglomeration, network efficiency, and network traffic with the multiple criteria decision‐making method, classifies the vulnerability of subway stations with a clustering idea, and analyzes the most vulnerable area of the Beijing Metro network with the Newman fast algorithm. The results show that this method can evaluate the node vulnerability of metro networks more accurately and effectively by considering the topological vulnerability, functional vulnerability, and human flow function vulnerability of the networks. According to the node vulnerability value, the stations can be divided into five grades: The station at the intersection of the loop and radial lines and its adjacent stations have a higher vulnerability level. The vulnerability level of the downtown core area and suburban line terminal stations is relatively low, and the areas with the greatest vulnerability of the Beijing Metro network are mainly located on the east and west sides of the core city.

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Vortex shedding at the tail of a high-speed train changes the aerodynamic characteristics of the train, which affects the safety and stability of train operation. This paper takes CR400AF as the research object, and uses dynamic monitoring points to realize the whole process monitoring of the flow field at the tail of the train running in open air and in tunnel for the first time. The wake of the train in different infrastructure scenarios is analyzed by the proper orthogonal decomposition method. The study found that the wake vortex structure is quite different when the train runs in different scenarios, and the turbulent kinetic energy intensity of the wake in tunnel is higher than that of the open air running. Modal decomposition method can identify flow structures that have a large impact on train aerodynamics. Through frequency analysis, it is found that the modal frequency obtained from the decomposition is higher when running in open air than when running in tunnel. With the increase of train speed, the modal strouhal number increases when the train is running in open air, and decreases when the train runs in tunnel. After the train enters the tunnel, the reverse movement of the air around the train body suppresses the development and separation of the boundary layer, which is the main reason for the low frequency of wake vortex shedding in tunnel. The stability of the train running in tunnel is worse than that when running in open air, which is closely related to the more complex flow structure around the car body and the drastic change of aerodynamic force when running in the tunnel.

After increasing the train speed, the wheel-rail coupling effect is intensified, as is the coupling effect between the train and the surrounding air, which causes the severe vibration of the train. Considering this problem and taking the high-speed train as the object of this study, a method for the coupling of aerodynamics and vehicle dynamics is proposed. Its validity is then demonstrated by the results of field tests and moving model experiments. On this basis, the fluid–structure coupling characteristics of a high-speed train passing through a tunnel are studied, as well as the effects of different coupling methods and track irregularities. The obtained results demonstrate that the interaction between the tail car and the surrounding air is significant. In the tunnel and at its exit, the lift and the car body acceleration significantly change, especially when the coupling of aerodynamics and vehicle dynamics, as well as the vertical and lateral track irregularities, are simultaneously imposed. When the head car travels out of the tunnel, the lift of the middle and tail cars both dramatically will change. It is deduced that the fluid–structure interaction has a significant effect on the swing phenomenon of the tail car. This phenomenon is the result of the combined effects of aerodynamics and track irregularities. The different frequencies of the lateral displacement for each car body, except the frequencies of the car bodies themselves, are mainly determined by vertical track irregularities.

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