Assessing transfer property and reliability of urban bus network based on complex network theory

Abstract

Transfer reliability has an important impact on the urban bus network. The proportion of zero and one transfer time is a key indicator to measure the connectivity of bus networks. However, it is hard to calculate the transfer time between nodes because of the complicated network structure. In this paper, the topological structures of urban bus network in Jinan are constructed by space L and space P. A method to calculate transfer times between stations has been proposed by reachable matrix under space P. The result shows that it is efficient to calculate the transfer time between nodes in large networks. In order to test the transfer reliability, a node failure process has been built according to degree, clustering coefficient and betweenness centrality under space L and space P. The results show that the deliberate attack by betweenness centrality under space P is more effective compared with other five attack modes. This research could provide a power tool to find hub stations in bus networks and give a help for traffic manager to guarantee the normal operation of urban bus systems.

Reliability analysis of complex electromechanical systems: State of the art, challenges, and prospects

“…Subsequently, a new measure was constructed on the basis of failure data and the network model to reflect the reliability of the nodes in combination with their functional and topological properties. Additionally, CNT-based reliability modeling has been successfully applied to F I G U R E 7 General framework for complex network-based reliability modelling urban bus networks, 135 typical road networks, 136 and other fields. 137 The general framework for complex network-based reliability modeling is summarized in Figure 7.…”

Reliability evaluation of wireless multimedia sensor networks based on instantaneous availability

“…The red line (Case 1) indicates different degrees of sensor nodes, and the green line (Case 2) shows the same degree where the degree of each node is replaced by the average in Case 1. Owing to the same values of a and b, the failure rate as well as repair rate of each node in Case 1 is different on the basis of equation (13). As the image shows, the IA has obvious fluctuation at the initial stage of the system operation in Case 1.…”

“…12 In recent years, we are delighted to see system reliability research based on complex network theory which has broken through the traditional reliability research methods and application fields. [13][14][15] In other words, cascading failure, percolation, and other complex network theories are also applicable to the reliability research of WMSN from the perspective of the system, which is no longer limited to the framework of classical physics and statistical physics. However, some network indicators such as degree, betweenness, and cluster coefficient merely reflect the nature of the complex network itself, but users' requirements and failure factors are neglected, which do not accord with the objective of the failure-centered reliability research.…”