An urban metro network is susceptible to becoming vulnerable and difficult to recover quickly in the face of an unexpected attack on account of the system’s complexity and the threat of various emergencies. Therefore, it is necessary to assess the resilience of urban metro networks. However, the research on resilience assessment of urban metro networks is still in the development stage, and it is better to conduct said research using a technique which combines many attributes, multiple methods, and several cases. Therefore, based on the complex network modeling and topological characteristics analysis of metro systems, a metro network’s robustness and vulnerability measurement method under node interruption and edge failure is proposed for the first time in this study. Then, considering the three cases of general station interruption, interchange station interruption, and traffic tunnel failure, a quantitative resilience assessment model of metro networks is put forward, and the corresponding recovery strategies are discussed. Finally, a case study of the Zhengzhou Metro Network (ZZMN) under an extreme rainstorm is conducted to demonstrate the viability of the proposed model. The results show that ZZMN possesses scale-free and small-world network properties, and it is robust to random interruptions but vulnerable to deliberate attacks. ZZMN still needs to improve its effectiveness in information transmission. The centrality distribution for each node in the ZZMN network differs significantly, and each node’s failure has a unique impact on the network. The larger the DC, BC, and PR of a node is, the lower the network’s robustness after its removal is, and the stronger the vulnerability is. Compared with the three cases of general station interruption, interchange station interruption, and traffic tunnel failure, the network loss caused by tunnel failure was the lowest, followed by general station interruption, and the interruption at interchange stations was the most costly. Given the failures under various cases, the metro management department should prioritize selecting the optimal recovery strategy to improve the resilience of the metro network system. This study’s findings can assist in making urban metro systems less vulnerable to emergencies and more resilient for a quick recovery, which can provide scientific theoretical guidance and decision support for the safety and resilient, sustainable development of urban metro systems.

At present, the evaluation of normal teaching order and teaching quality has been seriously interfered by the impact of COVID-19. In order to ensure the quality of art classroom teaching, this article uses BP neural network technology to build a model for art teaching quality evaluation during the epidemic. Based on the introduction of the BP neural network model and the problems of art teaching quality evaluation, the article focuses on the art teaching quality evaluation indicators and the BP neural network algorithm and process. In addition, the article also uses an empirical method to verify the effect of the BP network model training method, and obtains the expected effect. Finally, it discusses the problem of information processing in art teaching evaluation.

With the flourishing development of the urban metro system, the topology of important nodes changes as the metro network structure evolves further. The identical important node has distinct impacts on various metro networks’ resilience. At present, the dynamic influences of important station evolution on the resilience of metro networks remain to be studied further. Taking Shenzhen Metro Network (SZMN) as an example, the dynamic influences of the structure evolution of important nodes on the resilience of the metro network were investigated in this study. Firstly, the dynamic evolution characteristics of complex network topology and node centralities in metro systems were mined. Then, combined with the node interruption simulation and the resilience loss triangle theory, the resilience levels of distinct metro networks facing the failure of the same critical node were statistically assessed. Additionally, suggestions for optimal network recovery strategies for diverse cases were made. Finally, based on the evaluation results of node importance and network resilience, the dynamic influences of the topological evolution of important nodes on the resilience of metro networks were thoroughly discussed. The study’s findings help us comprehend the metro network’s development features better and can assist the metro management department in making knowledgeable decisions and taking appropriate action in an emergency. This study has theoretical and practical significance for the resilient operation and sustainable planning of urban metro network systems.

Many kinds of spatial–temporal data collected by transportation systems, such as user order systems or automated fare-collection (AFC) systems, can be discretized and converted into time-series data. With the technique of time-series data mining, certain travel-demand patterns of different areas in the city can be detected. This study proposes a data-mining model for understanding the patterns and regularities of human activities in urban areas from spatiotemporal datasets. This model uses a grid-based method to convert spatiotemporal point datasets into discretized temporal sequences. Time-series analysis technique dynamic time warping (DTW) is then used to describe the similarity between travel-demand sequences, while the clustering algorithm density-based spatial clustering of applications with noise (DBSCAN), based on modified DTW, is used to detect clusters among the travel-demand samples. Four typical patterns are found, including balanced and unbalanced cases. These findings can help to understand the land-use structure and commuting activities of a city. The results indicate that the grid-based model and time-series analysis model developed in this study can effectively uncover the spatiotemporal characteristics of travel demand from usage data in public transportation systems.

Although log-structured merge trees (LSM-trees) are commonly adopted in many NoSQLs as they can significantly improve the write performance in updating a database, most of the proposed LSM-trees are concentrated on storing a single version of data. On the other hand, in many Internet of Things (IoT) applications, it is important to maintain the old versions of data in addition to the latest version. In this article, we introduce our design and implementation of an enhancement of LevelDB to multiversion LevelDB (called MVLevelDB) with the purpose to efficiently support temporal queries on multiversion data in IoT applications. Based on the temporal consistency, we formulated the log-structured multiversion tree (LSMV-tree) to be implemented into MVLevelDB. In LSMV-tree, each data version is associated with two time-stamps to define its validity interval, and both the data versions and the components are time-sorted to improve the efficiency in searching data in processing temporal queries. To handle the problem of multicomponents data versions, we designed the data version duplication (DvD) method in which a data version will be duplicated in the next component if it is valid while its component is being flushed from the main memory to disk storage. Extensive experiments using a benchmark program have been performed to investigate the performance of MVLevelDB as compared with LevelDB both in writing and reading data.