Analysis of Risk Bus Driver Characteristics and Research on Risk Level Evaluation Methods for Bus Drivers
Currently, there is a lack of a comprehensive and integrated method for assessing risk levels of bus drivers. This study utilizes XGBOOST and Logistic regression models to analyze the impact of various indicator features of bus drivers on crash risks. A grey whitening weight function model is then c...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10753582/ |
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| author | Tongqiang Ding Huijuan Yin Zhiqiang Li Xinyu He Lili Zheng Jianfeng Xi |
| author_facet | Tongqiang Ding Huijuan Yin Zhiqiang Li Xinyu He Lili Zheng Jianfeng Xi |
| author_sort | Tongqiang Ding |
| collection | DOAJ |
| description | Currently, there is a lack of a comprehensive and integrated method for assessing risk levels of bus drivers. This study utilizes XGBOOST and Logistic regression models to analyze the impact of various indicator features of bus drivers on crash risks. A grey whitening weight function model is then constructed to evaluate the risk levels of bus drivers, achieving a quantified assessment of their risk levels. Based on the research findings, the following observations were made: 1) The number of non-fault crashes is the most important risk feature influencing the occurrence of at-fault crashes; 2) Features related to crashes, violations, and alarms, as well as age, bus driving experience, driving experience, route length, and the number of stops, have a negative impact on the occurrence of at-fault crashes; 3) The study quantifies bus drivers into five risk levels, with higher levels indicating higher risk. It was found that 94.94% of bus drivers are in the second and third risk levels, 4.93% in the first and fourth risk levels, and only 0.12% of bus drivers are in the highest fifth risk level. The conclusions drawn in this study, along with the proposed method for evaluating risk levels of bus drivers, will contribute to the evaluation and management of bus drivers by bus companies and transportation authorities, thereby reducing crashes in public transportation. |
| format | Article |
| id | doaj-art-81398b851b944112988f832c0a0e4581 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-81398b851b944112988f832c0a0e45812025-01-16T00:02:12ZengIEEEIEEE Access2169-35362024-01-011217134817136710.1109/ACCESS.2024.349893610753582Analysis of Risk Bus Driver Characteristics and Research on Risk Level Evaluation Methods for Bus DriversTongqiang Ding0https://orcid.org/0000-0002-2212-961XHuijuan Yin1https://orcid.org/0009-0007-1086-6560Zhiqiang Li2Xinyu He3Lili Zheng4https://orcid.org/0000-0002-7142-5407Jianfeng Xi5https://orcid.org/0000-0002-4488-0850Transportation College, Jilin University, Changchun, ChinaTransportation College, Jilin University, Changchun, ChinaChina Academy of Transportation Sciences, Beijing, ChinaBureau of Commerce of Hulunbuir City, Hulunbuir, ChinaTransportation College, Jilin University, Changchun, ChinaTransportation College, Jilin University, Changchun, ChinaCurrently, there is a lack of a comprehensive and integrated method for assessing risk levels of bus drivers. This study utilizes XGBOOST and Logistic regression models to analyze the impact of various indicator features of bus drivers on crash risks. A grey whitening weight function model is then constructed to evaluate the risk levels of bus drivers, achieving a quantified assessment of their risk levels. Based on the research findings, the following observations were made: 1) The number of non-fault crashes is the most important risk feature influencing the occurrence of at-fault crashes; 2) Features related to crashes, violations, and alarms, as well as age, bus driving experience, driving experience, route length, and the number of stops, have a negative impact on the occurrence of at-fault crashes; 3) The study quantifies bus drivers into five risk levels, with higher levels indicating higher risk. It was found that 94.94% of bus drivers are in the second and third risk levels, 4.93% in the first and fourth risk levels, and only 0.12% of bus drivers are in the highest fifth risk level. The conclusions drawn in this study, along with the proposed method for evaluating risk levels of bus drivers, will contribute to the evaluation and management of bus drivers by bus companies and transportation authorities, thereby reducing crashes in public transportation.https://ieeexplore.ieee.org/document/10753582/Bus driverdata miningmachine learningdata analyticslevel evaluation |
| spellingShingle | Tongqiang Ding Huijuan Yin Zhiqiang Li Xinyu He Lili Zheng Jianfeng Xi Analysis of Risk Bus Driver Characteristics and Research on Risk Level Evaluation Methods for Bus Drivers IEEE Access Bus driver data mining machine learning data analytics level evaluation |
| title | Analysis of Risk Bus Driver Characteristics and Research on Risk Level Evaluation Methods for Bus Drivers |
| title_full | Analysis of Risk Bus Driver Characteristics and Research on Risk Level Evaluation Methods for Bus Drivers |
| title_fullStr | Analysis of Risk Bus Driver Characteristics and Research on Risk Level Evaluation Methods for Bus Drivers |
| title_full_unstemmed | Analysis of Risk Bus Driver Characteristics and Research on Risk Level Evaluation Methods for Bus Drivers |
| title_short | Analysis of Risk Bus Driver Characteristics and Research on Risk Level Evaluation Methods for Bus Drivers |
| title_sort | analysis of risk bus driver characteristics and research on risk level evaluation methods for bus drivers |
| topic | Bus driver data mining machine learning data analytics level evaluation |
| url | https://ieeexplore.ieee.org/document/10753582/ |
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