Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks
The prevention of railway operational accidents has become one of the leading issues in railway safety. Identifying the impact factors which significantly affect railway operating is critical for decreasing the occurrence of railway accidents. In this study, 8440 samples of accident data are selecte...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2022-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/395142 |
| _version_ | 1797206798639300608 |
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| author | Jin Wujie Jia Le Yan Lixin* Zhang Cheng |
| author_facet | Jin Wujie Jia Le Yan Lixin* Zhang Cheng |
| author_sort | Jin Wujie |
| collection | DOAJ |
| description | The prevention of railway operational accidents has become one of the leading issues in railway safety. Identifying the impact factors which significantly affect railway operating is critical for decreasing the occurrence of railway accidents. In this study, 8440 samples of accident data are selected as the datasets for analyzing. Fishbone diagram is applied to obtain the factors which cause the accident from the perspective of human-equipment-environment-management system theory. Then, the Bayesian network method was selected to establish a railway operation safety accident prediction model, and the sensitivity analysis method was used to obtain the sensitivity of each variable factor to the accident level. The results show that season, location, trouble maker and job function have a significant impact on railway safety, and their sensitivity was 0.4577, 0.4116, 0.3478 and 0.3192, respectively. Research helps the railway sector to understand the fundamental causes of accidents, and provides an effective reference for accident prevention, which is conducive to the long-term development of railway transportation. |
| first_indexed | 2024-04-24T09:12:45Z |
| format | Article |
| id | doaj.art-712bdbbea1ce44009bc14965ec4024b1 |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:12:45Z |
| publishDate | 2022-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-712bdbbea1ce44009bc14965ec4024b12024-04-15T17:34:55ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392022-01-0129254255210.17559/TV-20211102092922Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian NetworksJin Wujie0Jia Le1Yan Lixin*2Zhang Cheng3School of Transportation and Logistics, East China Jiaotong University, No. 808, East Shuanggang Road, Nanchang City, Jiangxi Province, China, State Grid Zhoushan Power Supply Company, Zhoushan, Zhejiang Province, ChinaSchool of Transportation and Logistics, East China Jiaotong University, No. 808, East Shuanggang Road, Nanchang City, Jiangxi Province, ChinaSchool of Transportation and Logistics, East China Jiaotong University, No. 808, East Shuanggang Road, Nanchang City, Jiangxi Province, ChinaSchool of Transportation and Logistics, East China Jiaotong University, No. 808, East Shuanggang Road, Nanchang City, Jiangxi Province, ChinaThe prevention of railway operational accidents has become one of the leading issues in railway safety. Identifying the impact factors which significantly affect railway operating is critical for decreasing the occurrence of railway accidents. In this study, 8440 samples of accident data are selected as the datasets for analyzing. Fishbone diagram is applied to obtain the factors which cause the accident from the perspective of human-equipment-environment-management system theory. Then, the Bayesian network method was selected to establish a railway operation safety accident prediction model, and the sensitivity analysis method was used to obtain the sensitivity of each variable factor to the accident level. The results show that season, location, trouble maker and job function have a significant impact on railway safety, and their sensitivity was 0.4577, 0.4116, 0.3478 and 0.3192, respectively. Research helps the railway sector to understand the fundamental causes of accidents, and provides an effective reference for accident prevention, which is conducive to the long-term development of railway transportation.https://hrcak.srce.hr/file/395142Bayesian networkcauses of accidentfishbone diagramprediction modelrailway safety |
| spellingShingle | Jin Wujie Jia Le Yan Lixin* Zhang Cheng Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks Tehnički Vjesnik Bayesian network causes of accident fishbone diagram prediction model railway safety |
| title | Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks |
| title_full | Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks |
| title_fullStr | Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks |
| title_full_unstemmed | Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks |
| title_short | Analyzing and Predicting Railway Operational Accidents Based on Fishbone Diagram and Bayesian Networks |
| title_sort | analyzing and predicting railway operational accidents based on fishbone diagram and bayesian networks |
| topic | Bayesian network causes of accident fishbone diagram prediction model railway safety |
| url | https://hrcak.srce.hr/file/395142 |
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