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Citation |
Xu C, Bao J, Liu P, Wang W. Int. J. Inj. Control Safe. Promot. 2018; 25(2): 141-153. |
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Affiliation |
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies , Southeast University , Nanjing , China. |
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Copyright |
(Copyright © 2018, Informa - Taylor and Francis Group) |
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DOI |
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PMID |
28853321 |
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Abstract |
This study aimed to investigate the contributing factors to serious casualty crashes in China. Crashes with deaths greater than 10 people are defined as serious casualty crashes in China. The serious casualty crash data were collected from 2009 to 2014. The random forest analysis was first conducted to select the candidate variables that affect the risks of serious casualty crashes. The Bayesian random parameters accelerated failure time (AFT) model was then developed to link the probability of the serious casualty crash with road geometric conditions, pavement conditions, environmental characteristics, collision characteristics, vehicle conditions, and driver characteristics. The AFT model estimation results indicate that overload driving, country road, northwest china region, turnover crash, private car, snowy or icy road surface and sight distance conditions have significant fixed effects on the likelihood of serious casualty crashes. In addition to these fixed-parameter variables, freeway, clear weather conditions, coach drivers, and upgrade horizontal curve affect the likelihood of serious casualty crashes with varying magnitude across observations. One of the important findings is that the serious casualty crash likelihood does not always decrease with an increase in the driving experience (number of years driven). Before the inflection point of 7 years, the serious casualty crash likelihood increases as the driving experience grows. The results of this study can help to develop effective countermeasures and policy initiatives for the prevention of serious casualty crashes. Language: en |
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Keywords |
Bayesian survival analysis; Serious casualty crashes; accelerated failure time model; duration until-crash occurrence; random-parameter regression; traffic safety |