Analysis of factors affecting serious multi-fatality crashes in China based on Bayesian network structure
This study aims to identify and analyze risk factors affecting serious multi-fatality crashes using Bayesian networks. First, a Bayesian network structure was constructed based on expert experience and the Dempster–Shafer evidence theory. Second, the structure was amended to satisfy the conditional-...
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Series: | Advances in Mechanical Engineering |
Online Access: | https://doi.org/10.1177/1687814017704145 |
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doaj-02bad6ffa48346b78566857b92ba60492020-11-25T01:27:33ZengSAGE PublishingAdvances in Mechanical Engineering1687-81402017-06-01910.1177/1687814017704145Analysis of factors affecting serious multi-fatality crashes in China based on Bayesian network structureShengxue Zhu0Xiaonan Cai1Jian Lu2Yichuan Peng3Faculty of Transportation Engineering, Huaiyin Institute of Technology, Huaian, ChinaCCCC Third Harbor Engineering Co. Ltd., Shanghai, ChinaSchool of Transportation Engineering, Tongji University, Shanghai, ChinaSchool of Transportation Engineering, Tongji University, Shanghai, ChinaThis study aims to identify and analyze risk factors affecting serious multi-fatality crashes using Bayesian networks. First, a Bayesian network structure was constructed based on expert experience and the Dempster–Shafer evidence theory. Second, the structure was amended to satisfy the conditional-independence test. Finally, 484 serious multi-fatality crashes for the period 2000–2012 in China were inputted into the Bayesian network to calculate the posterior probability of each factor. Results showed that the most influential factor was driver behavior, followed by vehicle condition, road condition, and external environment. And compared to the other behaviors, speeding and mistaken adjustment had greater influence on serious crashes. The findings in this study provide useful and valuable information for engineers to take corrective and preventative measures to reduce the probability for serious multi-fatality crashes.https://doi.org/10.1177/1687814017704145 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shengxue Zhu Xiaonan Cai Jian Lu Yichuan Peng |
spellingShingle |
Shengxue Zhu Xiaonan Cai Jian Lu Yichuan Peng Analysis of factors affecting serious multi-fatality crashes in China based on Bayesian network structure Advances in Mechanical Engineering |
author_facet |
Shengxue Zhu Xiaonan Cai Jian Lu Yichuan Peng |
author_sort |
Shengxue Zhu |
title |
Analysis of factors affecting serious multi-fatality crashes in China based on Bayesian network structure |
title_short |
Analysis of factors affecting serious multi-fatality crashes in China based on Bayesian network structure |
title_full |
Analysis of factors affecting serious multi-fatality crashes in China based on Bayesian network structure |
title_fullStr |
Analysis of factors affecting serious multi-fatality crashes in China based on Bayesian network structure |
title_full_unstemmed |
Analysis of factors affecting serious multi-fatality crashes in China based on Bayesian network structure |
title_sort |
analysis of factors affecting serious multi-fatality crashes in china based on bayesian network structure |
publisher |
SAGE Publishing |
series |
Advances in Mechanical Engineering |
issn |
1687-8140 |
publishDate |
2017-06-01 |
description |
This study aims to identify and analyze risk factors affecting serious multi-fatality crashes using Bayesian networks. First, a Bayesian network structure was constructed based on expert experience and the Dempster–Shafer evidence theory. Second, the structure was amended to satisfy the conditional-independence test. Finally, 484 serious multi-fatality crashes for the period 2000–2012 in China were inputted into the Bayesian network to calculate the posterior probability of each factor. Results showed that the most influential factor was driver behavior, followed by vehicle condition, road condition, and external environment. And compared to the other behaviors, speeding and mistaken adjustment had greater influence on serious crashes. The findings in this study provide useful and valuable information for engineers to take corrective and preventative measures to reduce the probability for serious multi-fatality crashes. |
url |
https://doi.org/10.1177/1687814017704145 |
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