Examining Bayesian network modeling in identification of dangerous driving behavior.

Traffic safety problems are still very serious and human factor is the one of most important factors affecting traffic crashes. Taking Next Generation Simulation (NGSIM) data as the research object, this study defines six control indicators and uses principal component analysis and K-means++ cluster...

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Autores principales:	Yichuan Peng, Leyi Cheng, Yuming Jiang, Shengxue Zhu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a3b304ca475c4926bb71446592a08a02
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id	oai:doaj.org-article:a3b304ca475c4926bb71446592a08a02
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spelling	oai:doaj.org-article:a3b304ca475c4926bb71446592a08a022021-12-02T20:18:10ZExamining Bayesian network modeling in identification of dangerous driving behavior.1932-620310.1371/journal.pone.0252484https://doaj.org/article/a3b304ca475c4926bb71446592a08a022021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252484https://doaj.org/toc/1932-6203Traffic safety problems are still very serious and human factor is the one of most important factors affecting traffic crashes. Taking Next Generation Simulation (NGSIM) data as the research object, this study defines six control indicators and uses principal component analysis and K-means++ clustering methods to get the driving style of different drivers. Then use the Bayesian Networks Toolbox (BNT) and MCMC algorithm to realize the structure learning of Bayesian network. and parameter learning was completed through Netica software. Finally, the vehicle-based traffic crash risk model was created to conduct sensitivity analysis, posterior probability inference, and simulation data was used to detect the feasibility of the model. The results show that the Bayesian network modeling can not only express the relationship between the crash risk and various driving behaviors, but also dig out the inherent relationship between different influencing factors and investigate the causes of driving risks. The results will be beneficial to accurately identify and prevent risky driving behavior.Yichuan PengLeyi ChengYuming JiangShengxue ZhuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0252484 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yichuan Peng Leyi Cheng Yuming Jiang Shengxue Zhu Examining Bayesian network modeling in identification of dangerous driving behavior.
description	Traffic safety problems are still very serious and human factor is the one of most important factors affecting traffic crashes. Taking Next Generation Simulation (NGSIM) data as the research object, this study defines six control indicators and uses principal component analysis and K-means++ clustering methods to get the driving style of different drivers. Then use the Bayesian Networks Toolbox (BNT) and MCMC algorithm to realize the structure learning of Bayesian network. and parameter learning was completed through Netica software. Finally, the vehicle-based traffic crash risk model was created to conduct sensitivity analysis, posterior probability inference, and simulation data was used to detect the feasibility of the model. The results show that the Bayesian network modeling can not only express the relationship between the crash risk and various driving behaviors, but also dig out the inherent relationship between different influencing factors and investigate the causes of driving risks. The results will be beneficial to accurately identify and prevent risky driving behavior.
format	article
author	Yichuan Peng Leyi Cheng Yuming Jiang Shengxue Zhu
author_facet	Yichuan Peng Leyi Cheng Yuming Jiang Shengxue Zhu
author_sort	Yichuan Peng
title	Examining Bayesian network modeling in identification of dangerous driving behavior.
title_short	Examining Bayesian network modeling in identification of dangerous driving behavior.
title_full	Examining Bayesian network modeling in identification of dangerous driving behavior.
title_fullStr	Examining Bayesian network modeling in identification of dangerous driving behavior.
title_full_unstemmed	Examining Bayesian network modeling in identification of dangerous driving behavior.
title_sort	examining bayesian network modeling in identification of dangerous driving behavior.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a3b304ca475c4926bb71446592a08a02
work_keys_str_mv	AT yichuanpeng examiningbayesiannetworkmodelinginidentificationofdangerousdrivingbehavior AT leyicheng examiningbayesiannetworkmodelinginidentificationofdangerousdrivingbehavior AT yumingjiang examiningbayesiannetworkmodelinginidentificationofdangerousdrivingbehavior AT shengxuezhu examiningbayesiannetworkmodelinginidentificationofdangerousdrivingbehavior
_version_	1718374301597433856

Examining Bayesian network modeling in identification of dangerous driving behavior.

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