Abstract
Accident analysis and prevention are helpful to ensure the sustainable development of transportation. The aim of this research was to investigate the factors associated with the severity of low-visibility-related rural single-vehicle crashes. Firstly, a latent class clustering model was implemented to partition the whole-dataset into a relatively homogeneous sub-dataset. Then, a spatial random parameters logit model was established for each dataset to capture unobserved heterogeneity and spatial correlation. Analysis was conducted based on the crash data (2014–2019) from 110 two-lane road segments. The results show that the proposed method is a superior crash severity modeling approach to accommodate the unobserved heterogeneity and spatial correlation. Three variables—seatbelt not used, motorcycle, and collision with fixed object—have a stable positive correlation with crash severity. Motorcycle leads to a 12.8%, 23.8%, and 12.6% increase in the risk of serious crashes in the whole-dataset, cluster 3, and cluster 4, respectively. In the whole-dataset, cluster 2, and cluster 3, the risk of serious crashes caused by seatbelt not used increased by 5.5%, 0.1%, and 30.6%, respectively, and caused by collision with fixed object increased by 33.2%, 1.2%, and 13.2%, respectively. The results can provide valuable information for engineers and policy makers to develop targeted measures.
Highlights
Published: 2 July 2021The impact of traffic crashes on sustainable development cannot be ignored because accidents will cause significant property damage and personal injury
Various risk factors will affect the severity of rural SV crashes, and corresponding research was conducted to clarify the relationship between risk factors and crash severity [8,9]
There is a significant correlation between driver characteristics and rural SV crash severity, which has been widely recognized by transportation professionals
Summary
Published: 2 July 2021The impact of traffic crashes on sustainable development cannot be ignored because accidents will cause significant property damage and personal injury. The impact of SV crashes on road safety is daunting. This phenomenon is obvious in rural areas of China because road infrastructure and medical assistance in these areas are worrisome. According to the characteristics of risk factors, three components can be roughly identified: driver characteristics, crash characteristics, and environmenta characteristics. There is a significant correlation between driver characteristics and rural SV crash severity, which has been widely recognized by transportation professionals. A positive correlation between male driver and serious crashes was revealed [6]. This phenomenon is related to the aggressive driving behavior of male drivers. A latent class logit function was established, and three risk variables—
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