No abstract

No abstract

No abstract

Urban public transportation network is a typically complex network and the local fault of network often leads to serious systemic impact, causing cascading failure. Research on cascading failure of the bus network, is advantageous to understand of the potential key individuals of the network, so as to guide the rational planning of transit network. At first, this paper proposed the path navigation strategy based on the transfer bus to describe the flow propagation law of transit network, and after which an improved failure model of the bus network based on capacity-load model was put forward. Finally, the experimental analysis was conducted based on real traffic data of Beijing, and under the improved path navigation strategy, the correlation between node load and real data reached 99.61%, accord with the real law more than the traditional path navigation strategy. Cascading failure simulation illustrated that even small-scale attacks could lead to systemic paralysis, causing serious impacts on the structure and function of network, meanwhile the damage to the functional integrity is more severe than structural integrity. Conclusion is conducted that results can guide the bus lines and citizens' travel.

Recent years have witnessed the emerging success of Graph Neural Networks (GNNs) for modeling graphical data. A GNN can model the spatial dependencies of nodes in a graph based on message passing through node aggregation. However, in many application scenarios, these spatial dependencies can change over time, and a basic GNN model cannot capture these changes. In this article, we propose a G raph S eq uence neural network with an A tt ention mechanism (GSeqAtt) for processing graph sequences. More specifically, two attention mechanisms are combined: a horizontal mechanism and a vertical mechanism. GTransformer, which is a horizontal attention mechanism for handling time series, is used to capture the correlations between graphs in the input time sequence. The vertical attention mechanism, a Graph Network (GN) block structure with an attention mechanism (GNAtt), acts within the graph structure in each frame of the time series. Experiments show that our proposed model is able to handle information propagation for graph sequences accurately and efficiently. Moreover, results on real-world data from three road intersections show that our GSeqAtt outperforms state-of-the-art baselines on the traffic speed prediction task.