Analysis of Factors Contributing to the Injury Severity of Overloaded-Truck-Related Crashes on Mountainous Highways in China

Huiying Wen, Yingxin Du, Zheng Chen and Sheng Zhao
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Huiying Wen: School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
Yingxin Du: School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
Zheng Chen: School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
Sheng Zhao: School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China

IJERPH, 2022, vol. 19, issue 7, 1-17

Abstract: Overloaded transport can certainly improve transportation efficiency and reduce operating costs. Nevertheless, several negative consequences are associated with this illegal activity, including road subsidence, bridge collapse, and serious casualties caused by accidents. Given the complexity and variability of mountainous highways, this study examines 1862 overloaded-truck-related crashes that happened in Yunnan Province, China, and attempts to analyze the key factors contributing to the injury severity. This is the first time that the injury severity has been studied from the perspective of crashes involving overloaded trucks, and meanwhile in a scenario of mountainous highways. For in-depth analysis, three models are developed, including a binary logit model, a random parameter logit model, and a classification and regression tree, but the results show that the random parameter logit model outperforms the other two. In the best-performing model, a total of fifteen variables are found to be significant at the 99% confidence level, including random variables such as freeway, broadside hitting, impaired braking performance, spring, and evening. In regards to the fixed variables, it is likely that the single curve, rollover, autumn, and winter variables will increase the probability of fatalities, whereas the provincial highway, country road, urban road, cement, wet, and head-on variables will decrease the likelihood of death. Our findings are useful for industry-related departments in formulating and implementing corresponding countermeasures, such as strengthening the inspection of commercial trucks, increasing the penalties for overloaded trucks, and installing certain protective equipment and facilities on crash-prone sections.

Keywords: injury severity; overloaded-truck-related crashes; random parameter logit model; mountainous highways; unobserved heterogeneity (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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