Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks
AbstractCrashes that occur on curved roadways are often more severe than straight road accidents. Previously, most studies focused on the associations between curved sections and roadway geometric characteristics. In this study, significant factors such as driver behavior, roadway features, vehicle factors, and environmental characteristics are identified and involved in analyzing traffic accident severity. Bayesian network analysis was conducted to deal with data, to explore the associations between variables, and to make predictions using these relationships. The results indicated that factors including point of impact, site of location, accident side of road, alcohol/drugs condition, etc., are relatively critical in crashes on horizontal curves. Accident severity increases when crashes occur on bridges. The sensitivity of accident severity to vehicle use, traffic control, point of impact, and alcohol/drugs condition is relatively high. Moreover, a combination of negative factors will aggravate accident severities. The results also proposed some suggestions regarding the design of vehicles, as well as the construction and improvement of curved roadways. View Full-Text
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Zhu, L.; Lu, L.; Zhang, W.; Zhao, Y.; Song, M. Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks. Sustainability 2019, 11, 2223.
Zhu L, Lu L, Zhang W, Zhao Y, Song M. Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks. Sustainability. 2019; 11(8):2223.Chicago/Turabian Style
Zhu, Lian; Lu, Linjun; Zhang, Wenying; Zhao, Yurou; Song, Meining. 2019. "Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks." Sustainability 11, no. 8: 2223.
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