Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city-China

How to identify critical nodes in metro networks is still an open and vital topic in complex networks, which has been a key issue in analyzing the structural organization of a network, especially in public transportation. Many effective measures have been developed to solve this problem in undirected or unweighted network. However, statistical indicators of static networks cannot reflect the spatial-temporal characteristics of passenger flow in the metro network. Furthermore, a single measure in critical nodes identification has its own shortcomings causing inaccurate estimation results. In this paper, a novel method for node significance on metro network based on Improved Topological Potential model considering Entropy (ITPE) is proposed. ITPE is utilized to aggregate the multi-measure by considering several different centrality measures to conduct the evaluation of node importance. In order to fully reflect the influence of nodes, topological entropy is adopted and applied to identify the weights of different centrality measures. In addition, invulnerability measurement is used to demonstrate the effectiveness of the proposed node identification method. Finally, the metro transit system in Shenzhen City, China was used as a case study to demonstrate the feasibility of the proposed method. It is found that ITPE method could effectively identify nodes or stations which are crucial both on network structure and passenger flow mobility while traditional undirected and unweighted network cannot completely identify. Accordingly, all the nodes estimated from ITPE method are ranked by the significance, and invulnerability is further used to test the rationality of the identification for critical nodes. (C) 2019 Elsevier B.V. All rights reserved.