Evaluating the dynamic resilience of the multi-mode public transit network for sustainable transport

Liu, Zhizhen; Chen, Hong; Liu, Enze; Zhang, Qi

doi:10.1016/j.jclepro.2022.131350

Cited by 37 publications

(12 citation statements)

References 26 publications

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“…Zhang et al 15 proposed a framework to quantitatively assess resilience of rail transit network while obtaining the best recovery strategy by recovery sequence and duration. Liu et al 16 built a nonlinear load-capacity model and a cascading failure model to reflect the dynamic feature of resilience. They explored the influence of the capacity planning of the multi-mode public transit network by adjusting the capacity control parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraints ( 14) and ( 15) ensure boarding and alighting behaviors of commuters are reasonable. Constraint (16) defines the range of decision variables.…”

Section: Commuter Flow Model Under Disruptionsmentioning

confidence: 99%

“…proposed a framework to quantitatively assess resilience of rail transit network while obtaining the best recovery strategy by recovery sequence and duration. Liu et al 16 . built a nonlinear load‐capacity model and a cascading failure model to reflect the dynamic feature of resilience.…”

Section: Literature Reviewmentioning

confidence: 99%

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Enhancing resilience assessment and bus bridging service design for large‐scale rail transit systems under disruptions

Gong,

Song,

et al. 2023

Quality & Reliability Eng

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This paper proposes a more computationally efficient approach for resilience assessment of rail transit system under disruptions. An improved linear programming model is developed to depict commuter flows and estimate system statuses. To address the computational challenge caused by the complexity of system, a four‐step approach is proposed based on the proposed commuter flow model. In the first step, Origin‐Destination (OD) pairs are divided into smaller groups and their flows under normal conditions are estimated by the proposed model separately, with the assumption that the railway capacity is sufficient relative to demand. Next, overall system statuses under normal conditions, including commuters on each train and spare capacities of each train are calculated by integrating results obtained in the first step. In the third step, system statuses under disruptions are estimated. In this step, we assume that unaffected commuters will not change their routes and flows of all affected commuters are estimated by a modified commuter model with given spare space of trains. Based on these outputs, several critical measures are introduced and calculated to quantify the resilience, resistance, and recovery ability of rail network systematically. We also demonstrate how our approach could be used to facilitate design and evaluation of bus bridging service. The proposed approach is demonstrated on the core part of Hangzhou rail transit network.

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Section: Literature Reviewmentioning

confidence: 99%

“…Constraints ( 14) and ( 15) ensure boarding and alighting behaviors of commuters are reasonable. Constraint (16) defines the range of decision variables.…”

Section: Commuter Flow Model Under Disruptionsmentioning

confidence: 99%

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Enhancing resilience assessment and bus bridging service design for large‐scale rail transit systems under disruptions

Gong,

Song,

et al. 2023

Quality & Reliability Eng

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“…Many studies on this topic mainly focus on the following aspects: (1) the topological characteristics of the entire network [18], such as the small-world [19,20] and scale-free [21,22] effects, which are analyzed on the basis of the classical statistical indicators [23] of complex networks, such as the average shortest path length or diameter, network efficiency, density, assortativity [24,25], etc. ; (2) the topological features or node importance [26,27] of metro stations, which are evaluated on the basis of the centrality parameters of network nodes [17], such as the degree centrality (DC) [28], betweenness centrality (BC) [29], closeness centrality (CC) [29], eigenvector centrality (EC) [8,28] and PageRank (PR) [30]; (3) the vulnerability [31][32][33][34], robustness [35][36][37] and resilience [38][39][40][41] of the metro-complex network, which are evaluated on the basis of (1) and ( 2); (4) the dynamic evolution law of a network or node, which is studied, and the rationality of the network development, which is assessed [42][43][44][45]; (5) the characteristics of the metro complex network with weighted passenger flow and traffic flow, which are analyzed [4,8,46].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Evolution Analysis of Complex Topology and Node Importance in Shenzhen Metro Network from 2004 to 2021

Meng

Liu

et al. 2022

Sustainability

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With the prosperous development of the urban metro network, the characteristics of the topological structure and node importance are changing dynamically. Most studies focus on static comparisons, and dynamic evolution research is rarely conducted. It is necessary to track the dynamic evolution mechanism of the metro network from the perspective of development. In this paper, the Shenzhen Metro Network (SZMN) topology from 2004 to 2021 was first modeled in Space L. Five kinds of node centralities in eight periods were measured. Then, the dynamic evolution characteristics of the SZMN network topology and node centralities were compared. Finally, an improved multi-attribute decision-making method (MADM) was used to evaluate the node importance, and the spatiotemporal-evolution mechanism of the node importance was discussed qualitatively and quantitatively. The results show that, with the spatiotemporal evolution of the SZMN, the nodes became more and more intensive, and the network tended to be assortative. The different kinds of node centralities changed variously over time. Moreover, the node importance of the SZMN gradually dispersed from the core area of Chegongmiao–Futian to the direction of the Airport and Shenzhen North. The node importance evolves dynamically over time, and it is closely related to the changes in the node type, surrounding nodes and whole network environment. This study reveals the dynamic evolution mechanism of the complex topology and node importance in the SZMN, which can provide scientific suggestions and decision support for the planning, construction, operation management and resilient sustainable development of the urban metro.

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“…To meet the ever-increasing and diversified mobility demand, multi-modal transit systems have been developed in many cities around the world (Cervero, 1998;Ceder, 2016;Zhou, 2016;Nuzzolo and Lam, 2017;Xu et al, 2021;Liu et al, 2022). Among them, the trunk-feeder bimodal transit system is one of the most commonly adopted.…”

Section: Introductionmentioning

confidence: 99%

Joint optimization of bimodal transit networks in a heterogeneous environment considering vehicle emissions

Yang

Jiang

Yan

et al. 2022

Journal of Cleaner Production

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Evaluating the dynamic resilience of the multi-mode public transit network for sustainable transport

Cited by 37 publications

References 26 publications

Enhancing resilience assessment and bus bridging service design for large‐scale rail transit systems under disruptions

Enhancing resilience assessment and bus bridging service design for large‐scale rail transit systems under disruptions

Dynamic Evolution Analysis of Complex Topology and Node Importance in Shenzhen Metro Network from 2004 to 2021

Joint optimization of bimodal transit networks in a heterogeneous environment considering vehicle emissions

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