Exploring the Spatiotemporal Characteristics and Causes of Rear-End Collisions on Urban Roadways

Wenhui Zhang, Tuo Liu () and Jing Yi
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Wenhui Zhang: School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China
Tuo Liu: School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China
Jing Yi: School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China

Sustainability, 2022, vol. 14, issue 18, 1-23

Abstract: Rear-end collisions are caused by drivers misjudging urgent risks while following vehicles ahead in most cases. However, compared with other accident types, rear-end collisions have higher preventability. This study aims to reveal the prone segments and hours of rear-end collisions. First, we extracted 1236 cases from traffic accident records in Harbin from 2015 to 2019. These accidents are classified as property damage accidents, injury accidents and fatal accidents according to the collision severity. Second, density analysis in GIS was used to demonstrate the spatial distribution of rear-end collisions. The collision spots considering the density and severity were visually displayed. We counted the hourly and seasonal distribution characteristics according to the statistical data. Finally, LightGBM and random forest classifier models were used to evaluate the substantial factors affecting accident severity. The results have potential practical value in rear-end collision warning and prevention.

Keywords: rear-end collision; spatiotemporal analysis; machine learning model; collision prediction; urban traffic analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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