scholarly journals Critical Hazards Identification and Prevention of Cascading Escalator Accidents at Metro Rail Transit Stations

2020 ◽  
Vol 17 (10) ◽  
pp. 3400
Author(s):  
Zhiru Wang ◽  
Ran S. Bhamra ◽  
Min Wang ◽  
Han Xie ◽  
Lili Yang
Keyword(s):  
Network Analysis ◽  
Complex Network ◽  
Network Theory ◽  
Preventive Measures ◽  
Management Plan ◽  
Rail Transit ◽  
Analysis Method ◽  
Cascading Effects ◽  
Complex Network Theory ◽  
Passenger Behavior

Escalator accidents not only happen frequently but also have cascading effects. The purpose of this study is to block the formation of cascading accident networks by identifying and preventing critical hazards. A modified five-step task-driven method (FTDM) is proposed to break down passenger-related cascading escalator accidents. Three complex network parameters in complex network theory are utilized to identify critical and non-critical Risk Passenger Behavior (RPB) hazards and Other Hazards related with Risk Passenger Behavior (OH-RPB) in accident chains. A total of 327 accidents that occurred in the Beijing metro rail transit (MRT) stations were used for case studies. The results are consistent in critical and non-critical RPB and OH-RPB and prove that through combination of FTDM accident investigation model and complex network analysis method, critical and non-critical RPB and OH-RPB in a complicated cascading hazards network can be identified. Prevention of critical RPB can block the formation of cascading accident networks. The method not only can be used by safety manager to make the corresponding preventive measures according to the results in daily management but also the findings can guide the allocation of limited preventive resources to critical hazards rather than non-critical hazards. Moreover, the defects of management plan and product design can be re-examined according to the research results.

scholarly journals The South African Banking Director Network: An Investigation Into Interlocking Directorships Using Social Network Analysis (SNA)

2014 ◽  
Vol 13 (5) ◽  
pp. 963
Author(s):  
Burgert A. Senekal ◽  
Karlien Stemmet
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Complex Systems ◽  
South African ◽  
Complex Network ◽  
Network Theory ◽  
Complex Network Theory ◽  
International Trends ◽  
Director Network

The theory of complex systems has gained significant ground in recent years, and with it, complex network theory has become an essential approach to complex systems. This study follows international trends in examining the interlocking South African bank director network using social network analysis (SNA), which is shown to be a highly connected social network that has ties to many South African industries, including healthcare, mining, and education. The most highly connected directors and companies are identified, along with those that are most central to the network, and those that serve important bridging functions in facilitating network coherence. As this study is exploratory, numerous suggestions are also made for further research.

scholarly journals A New Accident Analysis Method Based on Complex Network and Cascading Failure

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Ziyan Luo ◽  
Keping Li ◽  
Xin Ma ◽  
Jin Zhou
Keyword(s):  
Complex Network ◽  
Network Theory ◽  
Cascading Failure ◽  
Accident Analysis ◽  
Analysis Method ◽  
Complex Network Theory ◽  
Accident Prediction ◽  
Prediction And Prevention ◽  
Accident Causation ◽  
The Government

A new accident causation model is proposed for accident analysis based on the complex network theory. By employing the cascading failure scheme, a new accident investigation method is performed on the associated new model, by which we can reveal key causation factors and key causation factor chains that lead to the final accident. The efficiency of a network is introduced for evaluating the severity of the damage of the whole network and hence the severity of the accident if it happens. All these can provide the government or associations with recommendations for accident prediction and prevention.

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