Resilience Evolution of Multi-mode Transportation Network in Urban Agglomeration Based on Risk Diffusion

doi:10.12141/j.issn.1000-565X.220486

Abstract

Abstract:

It can help improve the risk resilience of urban agglomerations to carry out in-depth analysis of the dynamic resilience evolution characteristics of multi-modal transport networks in urban agglomerations during the risk diffusion phase. Based on complex network theory and its extension theory, this study constructed a multi-modal and multi-level transportation network model for urban agglomerations based on road, railway and air networks. It matched node degree and node betweenness with accessibility, and analyzed their static topological characteristics under risk diffusion. Based on the theory of cascading failure dynamics, it considered the initial risk level, risk warning threshold and risk resilience of nodes at different stages, and constructed a risk dynamic diffusion model based on the nodes and connected edge measures affecting risk diffusion. Considering the characteristics of structural and functional changes under risk shocks, the paper constructed a network structural and functional resilience measurement model, and the resilience performance of multi-modal transport networks was represented by their coupling values. Using the multi-modal transport network of the Guan-Zhong Plain urban agglomeration as the research object, the Python Networkx and Matlab network analysis tools were used to simulate and analyze the dynamic resilience evolution of the multi-modal transport network of the urban agglomeration during the risk diffusion phase with respect to different risk diffusion methods, network reliability, redundancy, robustness and node risk handling capacity coefficients. The results show that the model results are consistent with reality. The network resilience can be effectively enhanced by improving network reliability, redundancy, robustness and node risk handling. Compared to the risk diffusion approach based on the node measure, the risk diffusion approach based on the connected edge measure has greater impact on resilience performance, suggesting that the distribution of route hierarchy levels has a greater impact on the resilience performance of multimodal transport networks in urban agglomerations compared to the number of routes. The overall resilience of a multi-modal, multi-level transport network performs better than that of a single transport network.

Key words: traffic engineering, urban agglomeration, multi-mode transportation network, risk diffusion, resilience evolution

CLC Number:

U491.1⁺3

MA Shuhong, YANG Lei, CHEN Xifang. Resilience Evolution of Multi-mode Transportation Network in Urban Agglomeration Based on Risk Diffusion[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(6): 42-51.

Figures/Tables 13

Fig.1

Fig.2

Table 1

Hierarchical division of multi-mode transportation network in urban agglomeration"

层次	节点枢纽	公路网络 M^（1）	铁路网络 M^（2）	航空网络 M^（3）	$ω i j (p)$	影响权重 $μ p$
第1层次 P^（1）	市级节点	高速公路	高速铁路	航线	4	0.47
第2层次 P^（2）	区县节点	国道	干线铁路		3	0.28
第3层次 P^（3）	公路节点	省道	支线铁路		2	0.16
第4层次 P^（4）	虚拟节点	虚拟网络			1	0.09
影响权重方正汇总行 $μ m$		1/3	1/3	1/3

Table 1

Fig.3

Table 2

Calculation specifications of complex features in static topology"

测度维度	静态拓扑指标	计算公式	指标含义
风险扩散节点测度	节点度中心性	$D e (v i) = ∑ m = 1 3 ∑ p = 1 3 u m u p N m p - 1 ∑ j ∈ N m p i A i j$	$D e (v i)$ 代表节点度， $μ p$ 、 $N m p$ 指p层次交通网络m的权重和节点数， $N m p i$ 是p层次交通网络m除节点i外所有节点的集合， $A i j$ 为加权矩阵 G 的变量。
	节点介数中心性	$B c (v i) = ∑ v s ≠ v i ≠ v t, s < t σ s t v i σ s t$	$B c (v i)$ 节点介数， $σ s t$ 表示为从节点对s、t的最短路径的总数量， $σ s t (v i)$ 表示这些最短路径中经过节点 $i$ 的路径的数量。
	节点可达性	$R e (v i) = 1 N ∑ p = 1 3 ∑ k = 1 g N k p$	$R e (v i)$ 表示节点可达性，N为网络总节点数， $N k p$ 表示节点i通过第p层次交通网络走k步所能到达的节点数，当p=｛1，2，3｝，对应能走的步数为g=｛3，2，1｝。
风险扩散连边测度	加权路径长度	$D i j = L ∑ j ∈ v i ω i j m i n ∑ u = 1 L ∑ v = 1 d i j u 1 ω v u$	$D i j$ 节点对i、j间的标准加权距离， $d i j u$ 为点i、j间第u条最短路径的长度， $ω n u$ 为点i、j间第u条最短路径上第v条边的权重， $L$ 为最短路径数量。
风险扩散连边测度	路径重要度	$E i j = ∑ p = 1 3 ∑ m = 1 3 l i j, p ω p m$	$E i j$ 表示路径重要度， $l i j, p$ 和 $ω p m$ 表示节点i相连的线路中第p层次交通网络m对应的线路数和影响度。

Table 2

Fig.4

Fig.5

Fig.6

Table 3

Dynamic risk state judgment indicators"

判断指标	计算指标	指标含义
节点风险水平	$R i = H i S i p$	$H i = B c i α$ ，表示风险概率， $S i p = μ p$ ，表示风险危害， $α$ 为可靠性参数
节点风险预警阈值	$T i = (1 + r) R i$	$r$ 为冗余性系数
节点风险抵御能力	$F i = γ p T i$	$γ p$ 为鲁棒性系数
节点风险状态	$J m i = 1, 失效 0, 未失效$	$J m i$ 表示交通网络m中节点i是否处于失效状态，节点失效为1，未失效为0

Table 3

Fig.7

Fig.8

Fig.9

Fig.10

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