Risk propagation analysis of urban rail transit based on network model

Li, Man; Wang, Yanhui; Jia, Limin; Cui, Yiru

doi:10.1016/j.aej.2020.02.030

Cited by 30 publications

(3 citation statements)

References 19 publications

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“…It indicates the probability that two nodes adjacent to node i in the network are also adjacent, reflecting the level of clustering of nodes in the network. The larger the clustering coefficient, the closer the connection between nodes is [36]. The calculation formula is shown as follows:…”

Section: Complex Network Evaluation Indexmentioning

confidence: 99%

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

Wang,

Zhai,

Cao

et al. 2023

Applied Sciences

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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.

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Section: Complex Network Evaluation Indexmentioning

confidence: 99%

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

Wang,

Zhai,

Cao

et al. 2023

Applied Sciences

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“…(2) property/event (characterization) This instance/type relation is less common compared to the previous one. A typical example of this relationship is when one node in the graph is associated with multiple nodes that are considered as "risk characteristics" [20]. For instance, we may have multiple nodes about a delay quantified in minutes (e.g., 15 minutes), associated with a node representing a transportation step -all these ranges of time have a different impact on the final calculation of risk.…”

Section: What Is In a Risk Propagation Graph?mentioning

confidence: 99%

On the Semantics of Risk Propagation

Fumagalli

Engelberg²,

Sales

et al. 2023

Lecture Notes in Business Information Processing

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Risk propagation encompasses a plethora of techniques for analyzing how risk "spreads" in a given system. Albeit commonly used in technical literature, the very notion of risk propagation turns out to be a conceptually imprecise and overloaded one. This might also explain the multitude of modeling solutions that have been proposed in the literature. Having a clear understanding of what exactly risk is, how it be quantified, and in what sense it can be propagated is fundamental for devising high-quality risk assessment and decision-making solutions. In this paper, we exploit a previous well-established work about the nature of risk and related notions with the goal of providing a proper interpretation of the different notions of risk propagation, as well as revealing and harmonizing the alternative semantics for the links used in common risk propagation graphs. Finally, we discuss how these results can be leveraged in practice to model risk propagation scenarios.

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“…The occurrence of such failures poses a significant risk to the safe operation of urban rail trains. To address this problem, the method of accurately identifying faults at an early stage of occurrence to avoid major safety accidents in rail transport has become the focus of current research [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Early faint fault diagnosis of wheelset axlebox bearings in urban rail trains based on ICiSSA-MOMEDA

Yang,

Sun,

Yao

et al. 2023

Meas. Sci. Technol.

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Circulant Singular Spectrum Analysis (CiSSA) has good performance in the decomposition and extraction of periodic components of non-stationary signals. However, its decomposition of signals in complex environments suffers from spectral aliasing and difficulty in extracting feature information. Therefore, based on CiSSA, an improved circulant singular spectrum analysis and multi-point optimal minimum entropy deconvolution adjusted (ICiSSA-MOMEDA) is proposed and applied to the early faint fault diagnosis of axlebox bearings of urban rail train wheelsets. Firstly, the optimal embedding dimension is computed adaptively and accurately by an improved Cao's method. Then, the initial components obtained from the decomposition are reorganised by the K-ARs method. ICiSSA effectively solves the problems of spectrum confusion and fault information dispersion. Finally, ICiSSA is combined with MOMEDA to improve its ability to detect weak fault information. The superiority of ICiSSA-MOMEDA is verified based on the analysis of actual bearing data and comparison with other methods.

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Risk propagation analysis of urban rail transit based on network model

Cited by 30 publications

References 19 publications

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

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

On the Semantics of Risk Propagation

Early faint fault diagnosis of wheelset axlebox bearings in urban rail trains based on ICiSSA-MOMEDA

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