Quantifying the life-saving impact of seatbelt usage: A random forest analysis of unobserved heterogeneity and latent risk factors in vehicular fatalities

Ittirit Mohamad
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引用次数: 0

Abstract

Seatbelt use significantly reduces the severity of injuries and fatalities in vehicular accidents. This study leverages the Random Forest algorithm to evaluate the impact of seatbelt usage on fatality probabilities in Thailand, with a novel focus on drivers who caused the accidents. The model demonstrated high accuracy, correctly identifying 95.10 % of non-fatal cases and 91.60 % of fatal cases, though some misclassifications were observed. A key contribution of this research is the identification of hidden risk factors influencing fatality rates, including temporal patterns that revealed a surge in fatalities after 17:00, with increased risks associated with non-seatbelt use during late evening and early morning hours. Younger drivers, particularly active at night, were found to exhibit higher rates of non-seatbelt usage and were more likely to be involved in severe accidents. These findings highlight the critical need for targeted seatbelt enforcement and safety interventions during high-risk periods, especially among younger drivers who are at fault in accidents.
量化安全带使用对生命的影响:车辆死亡中未观察到的异质性和潜在风险因素的随机森林分析
安全带的使用大大降低了车辆事故中受伤和死亡的严重程度。本研究利用随机森林算法来评估安全带使用对泰国死亡概率的影响,重点关注造成事故的司机。该模型显示出较高的准确率,正确识别了95.10%的非致命病例和91.60%的致命病例,尽管存在一些错误分类。这项研究的一个关键贡献是确定了影响死亡率的潜在风险因素,包括时间模式,揭示了17:00之后死亡人数激增,深夜和清晨不使用安全带的风险增加。研究发现,年轻司机,尤其是夜间活跃的司机,使用非安全带的比例更高,更有可能发生严重事故。这些发现强调了在高风险时期,特别是在事故中有过错的年轻司机中,有针对性地实施安全带和安全干预的迫切需要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
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