Vulnerability modeling and assessment in urban transit systems considering disaster chains: A weighted complex network approach

Chen, Hongyu; Zhang, Limao; Ran, Lianyue

doi:10.1016/j.ijdrr.2020.102033

Cited by 38 publications

(10 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, scholars have devoted to determining the factors being most worthy of safety control and resource allocation by examining the topological structure and characteristics of a certain type of accident network [4,22,23]. Taking rail traffic accident analysis as an example, Chen et al [24] constructed an urban transit disaster network to identify the important factors contributing to avoiding accident. Similarly, by analyzing the topological characteristics of a metro construction accident network, Zhou et al [23] suggested that controlling undesirable events such as explosion can decrease the accident propagation efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Chen et alrevealed how sudden incidents in metro operations can trigger the spread of crowding risks, leading to passenger strandings, line changes, panic, and stampede accidents [22]. Chen et alalso developed a weighted complex network to identify an optimal strategy for resisting congestion contagion risk [23]. Fei et alanalyzed congestion risk propagation characteristics by modeling the propagation speed of congested road sections [24].…”

Section: Introductionmentioning

confidence: 99%

Propagation and control of congestion risk in scale-free networks based on information entropy

Yan,

Li,

Sun

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

To study the propagation pattern of congestion risk in the traffic network and enhance risk control capabilities, a model has been developed. This model takes into account the probabilities of five threats (the risk occurrence probability; the risk of loss; the unpredictability of risk; the uncontrollability of risk; the transferability of risk) in the traffic network to define the risk entropy and determine the risk capacity, analyze the mechanism of congestion risk propagation, and explore the impact of risk resistance, the average degree of risk capacity at intersections, and the degree of correlation on congestion risk propagation. Further, a control method model for risk propagation is proposed. Numerical simulation results demonstrate that the risk resistance parameter θ can inhibit the propagation of congestion risk during traffic congestion. The highest efficiency in controlling risk propagation is achieved when θ reaches a threshold value θ*. Furthermore, the average degree of intersection risk capacity α shows a positive correlation with θ* and a negative correlation with control efficiency. However, the degree of association ω has a negative effect on risk propagation control, decreasing the degree of association between nodes aids in risk propagation control.

show abstract

“…The safety and accessibility of roads can be improved, thereby greatly reducing the likelihood of road traffic accidents. At the same time, the severity of accident injuries will be reduced, and the prediction range of accident injury severity will be narrowed, thus improving the accuracy of accident collision prediction [4][5][6]. Governments, traffic management departments, and researchers have been exploring various solutions to address this pressing issue [7].…”

Section: Introductionmentioning

confidence: 99%

Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks

Wang,

Zhai,

Cao

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

The number of motor vehicles on the road is constantly increasing, leading to a rise in the number of traffic accidents. Accurately identifying the factors contributing to these accidents is a crucial topic in the field of traffic accident research. Most current research focuses on analyzing the causes of traffic accidents rather than investigating the underlying factors. This study creates a complex network for road traffic accident cause analysis using the topology method for complex networks. The network metrics are analyzed using the network parameters to obtain reduced dimensionality feature factors, and four machine learning techniques are applied to accurately classify the accidents’ severity based on the analysis results. The study divides real traffic accident data into three main categories based on the factors that influences them: time, environment, and traffic management. The results show that traffic management factors have the most significant impact on road accidents. The study also finds that Extreme Gradient Boosting (XGBoost) outperforms Logistic Regression (LR), Random Forest (RF) and Decision Tree (DT) in accurately categorizing the severity of traffic accidents.

show abstract

Vulnerability modeling and assessment in urban transit systems considering disaster chains: A weighted complex network approach

Cited by 38 publications

References 48 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

Propagation and control of congestion risk in scale-free networks based on information entropy

Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks

Contact Info

Product

Resources

About