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To fulfill the missing part in cascading failures based reliability study of bus transit network (BTN), this paper is devoted to establishing a novel cascading failures model under route failure perspective, as well as considering the link prediction effect on failure load dynamic redistribution (FLDR). First, for advancing the route failure perspective, the weighted bus transit route network (weighted BTRN) is established through modifying the Space R method. Thereby, the represented passenger flow system of BTN can adapt to the demand of investigating the cascading failures under route failure perspective, because the node actually denotes the route under weighted BTRN perspective. Second, through analyzing the general evolution process of FLDR under time step scale perspective and providing the overall evolution steps of the proposed cascading failures model, the cascading failures analytical framework under time step scale perspective is further proposed. Furthermore, through embedding the effect of structural similarity based link prediction indices on evaluating the existence possibility relative size of all optional evacuation links, FLDR pattern considering link prediction effect is proposed. Finally, the case study of Jinan’s BTRN is conducted to verify the adaptability of the proposed cascading failures model. This interdisciplinary study between transportation engineering and system science provides a strong link between two important study areas of complex network theory- cascading failures and link prediction, and helps to improve the engineering applicability of cascading failures based reliability model in actual BTN operation.

In the methodology of urban rail transit network (URTN), the study on the hour-scale based time-varying characteristic of topology complexity (HTCTC) dynamics is a missing part. This paper proves the existence of HTCTC and analyzes its dynamics through establishing the testing framework in Nanjing’s weighted URTN. Furthermore, a novel network structure entropy based on station strength that characterizes the uniformity of energy distribution is proposed for capturing and understanding the HTCTC dynamics. Its availability is proved by conducting Pearson correlation analysis between it and a series of traditional weighted topology complexity indicators. This programmed analytical framework can contribute to improving the processible time and space scales of complex system, and can be widely applied to other cities.

In this paper, an evolution traffic network model considering the influence of the next-nearest neighbor is constructed. Using the model, we focus on studying the topology of the evolution traffic network in order to understand the traffic dynamics better. First, the influence of the next-nearest neighbor on the cumulative degree distribution is investigated by altering the values of the sensitivity coefficients; then the influence of some important parameters (density, the maximum velocity, etc.) is also discussed; subsequently, we study the trend of the degree of the hub when the values of the parameters change; some hubs of the evolution network are given at last. In this model, the hub is denoted as the node whose ranking of the degree is at the top a fixed percentage. By studying the statistical properties of the traffic network, we can grasp the traffic dynamics so as to take the appropriate methods for the better traffic situations.

In this work, we propose and study a model for the diffusion of congestion in complex networks. According to the proposed model, the level of congestion on each node will be self-organized into the same value. The diffusion of congestion throughout various networks with different topologies is investigated analytically and by numerical tests. The flow fluctuations in complex networks are studied. We recover a power-law scaling relation between the standard deviation and mean flow, which is consistent with the previous studies. Finally, we extend our model by adding two constraints, which may be effective strategies for diffusing the local and the global congestion in complex networks, respectively.