城市轨道交通网络韧性评估及恢复策略

doi:10.13229/j.cnki.jdxbgxb20220453

Abstract

Abstract:

In view of the deficiency of existing research on analyzing the resilience of urban rail transit network based on topological network efficiency， a resilience assessment method based on network performance response function is proposed， which is the weighted sum of OD passenger flow loss ratio and network service efficiency loss ratio. The Delphi-entropy weigh method is used to determine the comprehensive weight of two indicators， and a recovery optimization model with the maximum network resilience index is established， and the adaptive genetic algorithm is adopted to solve the developed model. Taking Xi'an rail transit network as an example， four hypothetical perturbance scenarios are proposed considering random attack and intentional attack. The differences of network resilience repair effects of target recovery strategy， random recovery strategy and preference recovery strategy under four hypothetical perturbance scenarios are compared and analyzed. The results show that the target recovery strategy has the best repair effect on rail transit network， followed by preference recovery strategy. Compared with random attack strategy， different recovery strategies have different network resilience under intentional attack strategy. When selecting the sequence of repairing damaged stations， we should not only consider the importance of damaged stations in network topology， but also consider the impact of passenger flow on network performance. Increasing the input of repair resources can shorten the recovery time and improve the repair efficiency， but the increase of repair resources is not proportional to the improvement of network resilience. The research conclusion can provide decision-making basis for the resilience assessment and emergency repair recovery of urban rail transit network.

Key words: engineering of communications and transportation system, rail transit network, resilience assessment, recovery strategy, adaptive genetic algorithm

CLC Number:

U491

Min MA,Da-wei HU,Lan SHU,Zhuang-lin MA. Resilience assessment and recovery strategy on urban rail transit network[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 396-404.

Figures/Tables 6

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假设情景	攻击策略	失效车站生成方案	失效车站编号
1	随机攻击策略	随机生成10个车站	4、17、33、55、67、79、91、119、125、141
2	蓄意攻击策略1	节点度最大的10个车站	1、4、10、13、15、23、26、43、48、77
3	蓄意攻击策略2	节点加权介数最大的10个车站	10、13、23、24、25、26、77、119、120、121
4	蓄意攻击策略3	客流强度最大的10个车站	6、8、10、17、18、38、39、42、44、54

扰动情景	恢复策略		车站修复顺序	网络韧性
情景1	随机恢复		55→17→91→119→67→4→33→141→79→125	0.8676
	偏好恢复	基于车站度	4→119→33→17→67→79→125→55→141→91	0.9000
	偏好恢复	基于车站重要度	17→79→33→119→4→55→125→67→91→141	0.8975
	目标恢复		4→17→79→119→33→55→125→91→141→67	0.9280
情景2	随机恢复		23→10→43→15→1→26→4→77→13→48	0.5972
	偏好恢复	基于车站度	1→4→10→13→15→23→26→43→48→77	0.6185
	偏好恢复	基于车站重要度	43→15→10→23→48→26→4→13→1→77	0.6787
	目标恢复		43→15→48→23→26→10→4→13→1→77	0.6924
情景3	随机恢复		119→10→77→25→13→24→120→26→23→121	0.8015
	偏好恢复	基于车站度	13→26→23→77→10→119→24→25→121→120	0.8715
	偏好恢复	基于车站重要度	10→23→119→26→13→24→25→77→121→120	0.9232
	目标恢复		23→10→119→13→26→77→24→121→25→120	0.9421
情景4	随机恢复		8→42→18→17→10→6→54→39→38→44	0.8134
	偏好恢复	基于车站度	10→17→42→8→44→38→6→18→54→39	0.8788
	偏好恢复	基于车站重要度	10→17→42→38→8→6→44→18→54→39	0.8802
	目标恢复		10→42→17→38→8→6→44→18→54→39	0.8811

Resilience assessment and recovery strategy on urban rail transit network

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