dentification of Causes and Factor Correlation Mining Methods for Truck Traffic Accidents in Mountainous Areas

SHAN Donghui; LIU Xianyong; LIU Jianbei; DU Yuchuan; QU Qinzhou

doi:10.3963/j.jssn.1674-4861.2025.05.005

Volume 43 Issue 5

Oct. 2025

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SHAN Donghui, LIU Xianyong, LIU Jianbei, DU Yuchuan, QU Qinzhou. dentification of Causes and Factor Correlation Mining Methods for Truck Traffic Accidents in Mountainous Areas[J]. Journal of Transport Information and Safety, 2025, 43(5): 44-56. doi: 10.3963/j.jssn.1674-4861.2025.05.005

Citation:

SHAN Donghui, LIU Xianyong, LIU Jianbei, DU Yuchuan, QU Qinzhou. dentification of Causes and Factor Correlation Mining Methods for Truck Traffic Accidents in Mountainous Areas[J]. Journal of Transport Information and Safety, 2025, 43(5): 44-56. doi: 10.3963/j.jssn.1674-4861.2025.05.005

Citation:

SHAN Donghui, LIU Xianyong, LIU Jianbei, DU Yuchuan, QU Qinzhou. dentification of Causes and Factor Correlation Mining Methods for Truck Traffic Accidents in Mountainous Areas[J]. Journal of Transport Information and Safety, 2025, 43(5): 44-56. doi: 10.3963/j.jssn.1674-4861.2025.05.005

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dentification of Causes and Factor Correlation Mining Methods for Truck Traffic Accidents in Mountainous Areas

doi: 10.3963/j.jssn.1674-4861.2025.05.005

SHAN Donghui^{1, 2, 3
,
,},
LIU Xianyong^{2, 3},
LIU Jianbei^{2, 3},
DU Yuchuan¹,
QU Qinzhou^{2, 3}

1.
The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
2.
CCCC First Highway Consultants Co., Ltd., Xi'an 710075, China
3.
Research and Development Center on Emergency Support Technologies for Transport Safety, Xi'an 710075, China

Received Date: 2024-07-05
Available Online: 2026-03-05

Abstract

Abstract

In response to the difficulties in identifying the causes of truck traffic accidents and the unclear influence of factors, a method for identifying the causes of truck traffic accidents and mining the relationships between factors in mountainous and hilly areas is studied. Data from 1, 839 truck accidents on a freight expressway in a mountainous and hilly area of Guangdong Province are collected. Through mathematical statistical methods, the spatiotemporal distribution of truck traffic accidents in mountainous and hilly areas is analyzed. Employing an improved Apriori algorithm, the study mined association rules to uncover factors influencing truck traffic accidents, resulting in 571 rules across comprehensive, self-correlated, specific dimensions (time, road elements), and accident dimensions. The model evaluation results indicate that the accuracy of the improved Apriori algorithm is 86.4% higher than that of the traditional Apriori algorithm. The results of association rule mining reveal: Clear weather conditions and longitudinal slopes less than 2% are significantly associated with minor accidents (lifted confidence>1.0), indicating that minor accidents primarily occur under these road conditions; Improper operation and insufficient safe distance are strongly associated with rollover and rear-end accidents (lifted confidence>1.8), suggesting that these accidents are predominantly caused by these factors; Slopes between -2% to -3% and radii greater than 1 000 m are significantly associated with major accidents (lifted confidence>1.6), indicating that major and severe truck accidents mainly occur on downhill sections with these slope and radius characteristics; Accidents causing injuries are significantly associated with the hours between 01:00 to 03:00 am (lifted confidence>1.3), highlighting a concentration of injury accidents during the early morning hours. The research results have revealed the causes of truck traffic accidents in mountainous and hilly areas and discovered the correlations among the elements of truck traffic accidents.
- traffic safety,
- tuck accidents,
- association mining method,
- Apriori algorithm,
- mountainous Areas

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References(31)

References

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