Analysis of the Severity of Heavy Truck Traffic Accidents Under Different Road Conditions

The rising frequency of heavy truck accidents in China poses a significant public safety risk, endangering lives and property. However, current research based on data from heavy truck accidents in China remains limited, making it challenging to support the formulation of traffic management measures. To mitigate the severity of these accidents, this study analyzed five years of heavy truck accident data from a specific region in China and developed logistic regression models for different road conditions. The aim was to identify the key factors influencing accident severity and understand the underlying mechanisms. The findings revealed that, under urban road conditions, the severity of heavy truck accidents is significantly impacted by factors such as lighting conditions, road safety attributes, driver age, and vehicle driving status. On highways, accident severity is largely influenced by visibility, roadside protection measures, intersection and section types, vehicle driving status, inter-vehicle accident types, and road safety features. On expressways, critical factors include inter-vehicle accident types, driver violations, visibility, and road alignment. In conclusion, the factors contributing to the severity of heavy truck accidents vary according to road conditions, which necessitates tailored traffic management strategies. The study’s findings offer theoretical support for more targeted approaches to preventing and controlling heavy truck traffic accident severity under different road conditions in China.

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Publication Details

Journal
Applied Sciences
Published
2024-11-20
DOI
https://doi.org/10.3390/app142210751
Citations
6
Primary Topic
Traffic and Road Safety
Type
article
Field-Weighted Citation Impact
2.19
Controls
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article

Analysis of the Severity of Heavy Truck Traffic Accidents Under Different Road Conditions

Mengzhu Guo, Zhen Tian, Facheng Chen, Sheqiang Ma
6 citations
Applied Sciences
Traffic and Road Safety
2.19
article

Analysis of the Severity of Heavy Truck Traffic Accidents Under Different Road Conditions

Mengzhu Guo, Zhen Tian, Facheng Chen, Sheqiang Ma
article en
6 citations

Abstract

The rising frequency of heavy truck accidents in China poses a significant public safety risk, endangering lives and property. However, current research based on data from heavy truck accidents in China remains limited, making it challenging to support the formulation of traffic management measures. To mitigate the severity of these accidents, this study analyzed five years of heavy truck accident data from a specific region in China and developed logistic regression models for different road conditions. The aim was to identify the key factors influencing accident severity and understand the underlying mechanisms. The findings revealed that, under urban road conditions, the severity of heavy truck accidents is significantly impacted by factors such as lighting conditions, road safety attributes, driver age, and vehicle driving status. On highways, accident severity is largely influenced by visibility, roadside protection measures, intersection and section types, vehicle driving status, inter-vehicle accident types, and road safety features. On expressways, critical factors include inter-vehicle accident types, driver violations, visibility, and road alignment. In conclusion, the factors contributing to the severity of heavy truck accidents vary according to road conditions, which necessitates tailored traffic management strategies. The study’s findings offer theoretical support for more targeted approaches to preventing and controlling heavy truck traffic accident severity under different road conditions in China.

Applied SciencesVol. 14(22)
Jilin University (CN), China People's Public Security University (CN)
Good health and well-being
Openalex Percentile: Top 13%
Traffic and Road Safety
2.19
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Analysis of the Severity of Heavy Truck Traffic Accidents Under Different Road Conditions — Mengzhu Guo, Zhen Tian, et al. · Applied Sciences (2024) | TGRS Research Map | TGRS