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The robustness is a crucial and essential problem of a subway network (SN), which can help us improve the efficiency of a transportation system. Several existing researches have analyzed the SN robustness based on the rail structure or the static distribution of passenger flow. However, the spatiotemporal characteristic of passenger flow also plays an important role in the SN robustness, since it can trigger some unexpected cascading failures in SN. Therefore, how to characterize the effect of this cascading failure on the SN robustness still remains an important and open problem. In this paper, we address the above problem as follows: (1) we propose a temporal subway network (TSN) to consider the dynamics of passenger flow in SN; (2) we adopt the linear threshold (LT) model to simulate the cascading failure process of TSN and propose a new robustness metric R(t) to evaluate the effect of this cascading failure on SN robustness. Based on the Shanghai subway smart card data, we carry out extensive experiments to analyze the effects of the cascading failure on the Shanghai SN robustness. Experiments show that the Shanghai TSN robustness varies over time. More significantly, the large volume of passenger flow can increase the impact of failure modes (i.e., random and malicious failure modes) on the Shanghai TSN robustness. INDEX TERMS Subway network, robustness, dynamic passenger flow, cascading failure.

Community detection in multilayer networks plays a key role in revealing the multiple aspects of information spreading and in comprehending the relationships and interactions within and between each layer. However, most existing algorithms are prone to local optimality, and they are also difficult to extend to high-dimensional networks. To address these challenges, we propose here a multi-objective algorithm for community detection that is based on the genetic algorithm. In particular, the modularity is introduced to optimize each network layer iteratively, and the local search is combined with genetic operations to overcome local optimality. Comparative benchmarks with other algorithms on artificial and real-world networks show that the proposed algorithm performs better, especially on high-dimensional networks.

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