@article{yin_quantitative_2022,
	title = {Quantitative analysis for resilience-based urban rail systems: {A} hybrid knowledge-based and data-driven approach},
	volume = {219},
	issn = {0951-8320},
	shorttitle = {Quantitative analysis for resilience-based urban rail systems},
	url = {https://www.sciencedirect.com/science/article/pii/S0951832021006670},
	doi = {10.1016/j.ress.2021.108183},
	abstract = {The rapid expansions of urban rail networks are faced with the growing number of disruptions caused by the complex rail signaling systems, incorrect driving behaviors, and extreme weather. Since urban rail systems are inherently complex and many of these disruptions are usually uncertain and inevitable, the rail managers have gradually paid more attention to the ability to withstand and quickly recover. Nevertheless, only a small number of recent developments have tried to address the ability of an urban rail system to recover from disruptions while considering the inherent structures. In this work, we propose a hybrid knowledge-based and data-driven approach for quantitative analysis of resilience. The aim is to model the causal relationships to quantify the importance of different perturbations to the overall resilience criteria. A set of key features related to the risk assessment and system resilience are summarized according to the historical data in Beijing Metro. Then, we develop a training procedure based on the structure of BN and historical data. Finally, we embed this hybrid approach into software that is applied to Beijing Metro. The results demonstrate the quantitative relationships between system resilience and different types of events.},
	language = {en},
	urldate = {2021-12-28},
	journal = {Reliability Engineering \& System Safety},
	author = {Yin, Jiateng and Ren, Xianliang and Liu, Ronghui and Tang, Tao and Su, Shuai},
	month = mar,
	year = {2022},
	keywords = {Bayesian network, Quantitative, Resilience, Transportation, Urban rail systems},
	pages = {108183},
}

{"_id":"AKvMyvTE7fXRtzqwJ","bibbaseid":"yin-ren-liu-tang-su-quantitativeanalysisforresiliencebasedurbanrailsystemsahybridknowledgebasedanddatadrivenapproach-2022","author_short":["Yin, J.","Ren, X.","Liu, R.","Tang, T.","Su, S."],"bibdata":{"bibtype":"article","type":"article","title":"Quantitative analysis for resilience-based urban rail systems: A hybrid knowledge-based and data-driven approach","volume":"219","issn":"0951-8320","shorttitle":"Quantitative analysis for resilience-based urban rail systems","url":"https://www.sciencedirect.com/science/article/pii/S0951832021006670","doi":"10.1016/j.ress.2021.108183","abstract":"The rapid expansions of urban rail networks are faced with the growing number of disruptions caused by the complex rail signaling systems, incorrect driving behaviors, and extreme weather. Since urban rail systems are inherently complex and many of these disruptions are usually uncertain and inevitable, the rail managers have gradually paid more attention to the ability to withstand and quickly recover. Nevertheless, only a small number of recent developments have tried to address the ability of an urban rail system to recover from disruptions while considering the inherent structures. In this work, we propose a hybrid knowledge-based and data-driven approach for quantitative analysis of resilience. The aim is to model the causal relationships to quantify the importance of different perturbations to the overall resilience criteria. A set of key features related to the risk assessment and system resilience are summarized according to the historical data in Beijing Metro. Then, we develop a training procedure based on the structure of BN and historical data. Finally, we embed this hybrid approach into software that is applied to Beijing Metro. The results demonstrate the quantitative relationships between system resilience and different types of events.","language":"en","urldate":"2021-12-28","journal":"Reliability Engineering & System Safety","author":[{"propositions":[],"lastnames":["Yin"],"firstnames":["Jiateng"],"suffixes":[]},{"propositions":[],"lastnames":["Ren"],"firstnames":["Xianliang"],"suffixes":[]},{"propositions":[],"lastnames":["Liu"],"firstnames":["Ronghui"],"suffixes":[]},{"propositions":[],"lastnames":["Tang"],"firstnames":["Tao"],"suffixes":[]},{"propositions":[],"lastnames":["Su"],"firstnames":["Shuai"],"suffixes":[]}],"month":"March","year":"2022","keywords":"Bayesian network, Quantitative, Resilience, Transportation, Urban rail systems","pages":"108183","bibtex":"@article{yin_quantitative_2022,\n\ttitle = {Quantitative analysis for resilience-based urban rail systems: {A} hybrid knowledge-based and data-driven approach},\n\tvolume = {219},\n\tissn = {0951-8320},\n\tshorttitle = {Quantitative analysis for resilience-based urban rail systems},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0951832021006670},\n\tdoi = {10.1016/j.ress.2021.108183},\n\tabstract = {The rapid expansions of urban rail networks are faced with the growing number of disruptions caused by the complex rail signaling systems, incorrect driving behaviors, and extreme weather. Since urban rail systems are inherently complex and many of these disruptions are usually uncertain and inevitable, the rail managers have gradually paid more attention to the ability to withstand and quickly recover. Nevertheless, only a small number of recent developments have tried to address the ability of an urban rail system to recover from disruptions while considering the inherent structures. In this work, we propose a hybrid knowledge-based and data-driven approach for quantitative analysis of resilience. The aim is to model the causal relationships to quantify the importance of different perturbations to the overall resilience criteria. A set of key features related to the risk assessment and system resilience are summarized according to the historical data in Beijing Metro. Then, we develop a training procedure based on the structure of BN and historical data. Finally, we embed this hybrid approach into software that is applied to Beijing Metro. The results demonstrate the quantitative relationships between system resilience and different types of events.},\n\tlanguage = {en},\n\turldate = {2021-12-28},\n\tjournal = {Reliability Engineering \\& System Safety},\n\tauthor = {Yin, Jiateng and Ren, Xianliang and Liu, Ronghui and Tang, Tao and Su, Shuai},\n\tmonth = mar,\n\tyear = {2022},\n\tkeywords = {Bayesian network, Quantitative, Resilience, Transportation, Urban rail systems},\n\tpages = {108183},\n}\n\n","author_short":["Yin, J.","Ren, X.","Liu, R.","Tang, T.","Su, S."],"key":"yin_quantitative_2022","id":"yin_quantitative_2022","bibbaseid":"yin-ren-liu-tang-su-quantitativeanalysisforresiliencebasedurbanrailsystemsahybridknowledgebasedanddatadrivenapproach-2022","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/S0951832021006670"},"keyword":["Bayesian network","Quantitative","Resilience","Transportation","Urban rail systems"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["bayesian network","quantitative","resilience","transportation","urban rail systems"],"search_terms":["quantitative","analysis","resilience","based","urban","rail","systems","hybrid","knowledge","based","data","driven","approach","yin","ren","liu","tang","su"],"title":"Quantitative analysis for resilience-based urban rail systems: A hybrid knowledge-based and data-driven approach","year":2022}