Exploring node importance evolution of weighted complex networks in urban rail transit

Meng, Yangyang; Tian, Xin; Li, Zhongwen; Zhou, Wei; Zhou, Zhijie; Zhong, Maiying

doi:10.1016/j.physa.2020.124925

Cited by 53 publications

(24 citation statements)

References 41 publications

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“…e experimental results indicated that the comprehensive indicators outperformed the use of a single traditional evaluation indicator. In the research of Meng et al [39], TOPSIS together with coefficient of variation was applied for the identification of important nodes, where the best improvement was that different attributes of the nodes were weighted before aggregating. To reasonably weight the network nodes, Hu et al [40] developed a way of combining ISM with traditional NNI evaluation and validated the effectiveness of the combined method by the simulations on the Advance Research Project Agency (ARPA) network.…”

Section: 2mentioning

confidence: 99%

Causation of Metro Operation Accidents in China: Calculation of Network Node Importance Based on DEMATEL and ISM

et al. 2021

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Continuous metro-operation accidents lead to serious economic loss and a negative social impact. The accident causation analysis is of great significance for accident prevention and metro operation safety promotion. Network node importance (NNI) evaluation has been widely used as a tool for ranking the nodes in complex networks; however, traditional indicators such as degree centrality (DC) are insufficient for examining accident networks. This study proposed an improved method by integrating decision making trail and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) into traditional NNI evaluation, where the key nodes are determined by both the nature of the accident network topology and the contribution of the nodes to accident development. Drawing on this method, 32 accident causal factors were identified and prioritized on the ground of 248 accident cases. It was found that 14 important factors related to staff (e.g., “driver noncompliance”), environment (e.g., “extrinsic nature disturbance”), passenger (e.g., “passenger sudden illness”), and machine (e.g., “track failures”) should be given priority in safety management due to their significant tendency of causing metro accidents. Theoretical and managerial implications were discussed to provide useful insights into the understanding of the causation of metro accidents and form a basis for metro managers to develop targeted safety countermeasures related to metro operation. The proposed hybrid method is proven effective in investigating accident networks involving sequential and casual relationships and revealing factors with high possibility to increase accidents.

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Section: 2mentioning

confidence: 99%

Causation of Metro Operation Accidents in China: Calculation of Network Node Importance Based on DEMATEL and ISM

et al. 2021

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“…Centrality indices provide a common and effective approach to analyze the spatial configurations of transport networks [42]. For a flow network, weighted complex indices have been developed and applied to public transport [43][44][45]. We select three critical indices in the MCA model to measure the characteristics of centrality: namely, weighted degree, weighted betweenness, and weighted closeness.…”

Section: Multiple Weighted Centrality Assessment Indicesmentioning

confidence: 99%

Weighted Centrality and Retail Store Locations in Beijing, China: A Temporal Perspective from Dynamic Public Transport Flow Networks

et al. 2021

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The spatial relationship between transport networks and retail store locations is an important topic in studies related to commercial activities. Much effort has been made to study physical street networks, but they are seldom empirically discussed with considerations of transport flow networks from a temporal perspective. By using Beijing’s bus and subway smart card data (SCD) and point of interest (POI) data, this study examined the location patterns of various retail stores and their daily dynamic relationships with three weighted centrality indices in the networks of public transport flows: degree, betweenness, and closeness. The results indicate that most types of retail stores are highly correlated with weighted centrality indices. For the network constructed by total public transport flows in the week, supermarkets, convenience stores, electronics stores, and specialty stores had the highest weighted degree value. By contrast, building material stores and shopping malls had the weighted closeness and weighted betweenness values, respectively. From a temporal perspective, most retail types’ largest correlations on weekdays occurred during the after-work period of 19:00 to 21:00. On weekends, shopping malls and electronics stores changed their favorite periods to the daytime, while specialty stores favored the daytime on both weekdays and weekends. In general, the higher store type level of the shopping malls correlates more to weighted closeness or betweenness, and the lower-level store type of convenience stores correlates more to weighted degree. This study provides a temporal analysis that surpasses previous studies on street centrality and can help with urban commercial planning.

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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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Exploring node importance evolution of weighted complex networks in urban rail transit

Cited by 53 publications

References 41 publications

Causation of Metro Operation Accidents in China: Calculation of Network Node Importance Based on DEMATEL and ISM

Causation of Metro Operation Accidents in China: Calculation of Network Node Importance Based on DEMATEL and ISM

Weighted Centrality and Retail Store Locations in Beijing, China: A Temporal Perspective from Dynamic Public Transport Flow Networks

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

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