-
Cascading failure process in interdependent networks has always been an important field of network cascading analysis. Unlike the previous studies, we take people’s demand for minimizing travel costs into consideration in this article and propose a network dynamics model based on the cost constraint. On this basis, we pay attention to the characteristics of different layers in the interdependent network, and taking the real-world traffic network for example, we define different load propagation modes for different layers. Then, we carry out the simulation experiment on cascade failure in the artificial network. By changing the structure of the network and the parameters in the model, such as the capability value of the network side and the connectivity of the network, we are able to focus on the effects of traditional protection strategies during the simulation and obtain some interesting conclusions. It is generally believed that increasing the quantity of connections in the network or improving the quality of edges will enhance the network robustness effectively. However, our experimental results show that these methods may actually reduce network robustness in some cases. On the one hand, we find that the resurrection of some special edges in the network is the main reason for the capacity paradox, as these edges will destroy the stable structure of the original network. On the other hand, neither improving the internal connectivity of a single-layer network nor enhancing the coupling strength between interdependent networks will effectively improve network robustness. This is because as the number of edges increases, some critical edges may appear in the network, attracting a large amount of the network load and leading the network robustness to decrease. These conclusions remind us that blindly investing resources in network construction cannot achieve the best protection effect. Only by scientifically designing the network structures and allocating network resources reasonably can the network robustness be effectively improved.
[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] -
组件或过程 定义方式 负载流动过程 $ {F}_{i\to j}={F}_{i\to }\cdot \dfrac{{\omega }_{j}/{t}_{ij}^{\gamma }}{\displaystyle\sum\limits_{n\in N\cap n\ne i}^{N} \dfrac{{\omega }_{n}}{{t}_{in}^{\gamma }}} $ 边初始负载 $ {L}_{m}\left(0\right)=\displaystyle \sum\limits_{i, j\in N}{F}_{i\to j}\cdot {R}_{m}^{i, j} $ 边能力 $ {C}_{m}=\left(1+\beta \right){L}_{m}\left(0\right), m\in E $ 级联失效过程 若$ {L}_{m}\left(T\right) > {C}_{m} $, 则删除边$ m $ 鲁棒性统计指标 失效边数$ S $ 
DownLoad:
CSV
$ \alpha $ = 1 $ \alpha $ = 2 $ \alpha $ = 3 $ \alpha $ = 4 $ {\mathrm{B}}{\mathrm{A}} $ 55.25% 47.61% 40.67% 31.03% $ {\mathrm{W}}{\mathrm{S}} $ 39.33% 35.54% 40.37% 38.92% DownLoad:
CSV
$ \alpha $ = 1 $ \alpha $ = 2 $ \alpha $ = 3 $ \alpha $ = 4 $ {\mathrm{B}}{\mathrm{A}} $ 60.74% 52.41% 46.83% 44.11% $ {\mathrm{W}}{\mathrm{S}} $ 45.54% 46.70% 43.69% 41.15% DownLoad:
CSV
$ \alpha $ = 1 $ \alpha $ = 2 $ \alpha $ = 3 $ \alpha $ = 4 $ {\mathrm{B}}{\mathrm{A}} $ 1.0454 1.0762 1.0968 1.1375 $ {\mathrm{W}}{\mathrm{S}} $ 1.1124 1.1287 1.0962 1.0931 DownLoad:
CSV
$ \gamma $ = 1 $ \gamma $ = 2 $ \gamma $ = 3 $ \gamma $ = 4 $ {\mathrm{B}}{\mathrm{A}} $ 50.85% 50.59% 51.25% 52.20% $ {\mathrm{W}}{\mathrm{S}} $ 56.30% 55.64% 50.71% 42.02% DownLoad:
CSV
$ \gamma $ = 1 $ \gamma $ = 2 $ \gamma $ = 3 $ \gamma $ = 4 $ {\mathrm{B}}{\mathrm{A}} $ 50.63% 52.21% 55.84% 57.16% $ {\mathrm{W}}{\mathrm{S}} $ 48.15% 49.32% 55.47% 58.94% DownLoad:
CSV
-
[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51]
DownLoad:
Catalog
Metrics
- Abstract views:569
- PDF Downloads:32
- Cited By:0