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Causal analysis of highway accidents considering filling in missing values based on RF-Apriori algorithm
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Le XUE1, Lu YU1, **, Longzhe JIN2, Bo LI1, Wenjin SHEN1
China Safety Science Journal | 2025, 35(4) : 211 - 218
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China Safety Science Journal | 2025, 35(4): 211-218
Public safety
Causal analysis of highway accidents considering filling in missing values based on RF-Apriori algorithm
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Le XUE1, Lu YU1, **, Longzhe JIN2, Bo LI1, Wenjin SHEN1
Affiliations
  • 1 School of Transportation Engineering,Dalian Jiaotong University,Dalian Liaoning 116028,China
  • 2 Research Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing 100083, China
Published: 2025-04-28 doi: 10.16265/j.cnki.issn1003-3033.2025.04.0774
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In order to improve the safety condition of highways,26 320 highway traffic accident records in France from 2018 to 2022 were selected as the research object. Three representative algorithms were selected to impute missing values in the data,including the RF algorithm,the expectation-maximization (EM) algorithm,and the K-nearest neighbors (KNN) algorithm. The impact of different imputation algorithms on data stability was compared based on the changes in variable variance before and after imputation. The Apriori association rule algorithm was then applied to analyze the causes of highway accidents with different severity levels using the completed dataset. The results indicate that after missing value imputation,the RF algorithm demonstrates superior stability. Compared to the model trained on the original data,the accuracy is improved by 5.66%,the recall rate is increased by 9.22%,and the F1 score is enhanced by 9.91%. It is found that passenger vehicles are more likely to cause property damage accidents; motorcycles are prone to cause injury accidents on roads with lower speed limits and fatal accidents on roads with higher speed limits. The use of safety equipment is significantly related to the severity level of accidents.

random forest(RF)  /  Apriori algorithm  /  missing value  /  highway  /  accident cause  /  data filling  /  association rules
Le XUE, Lu YU, Longzhe JIN, Bo LI, Wenjin SHEN. Causal analysis of highway accidents considering filling in missing values based on RF-Apriori algorithm[J]. China Safety Science Journal, 2025 , 35 (4) : 211 -218 . DOI: 10.16265/j.cnki.issn1003-3033.2025.04.0774
Year 2025 volume 35 Issue 4
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Article Info
doi: 10.16265/j.cnki.issn1003-3033.2025.04.0774
  • Receive Date:2024-11-14
  • Online Date:2025-07-05
  • Published:2025-04-28
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  • Received:2024-11-14
  • Revised:2025-01-08
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    1 School of Transportation Engineering,Dalian Jiaotong University,Dalian Liaoning 116028,China
    2 Research Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing 100083, China
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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