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首页> 外文期刊>Asian Transport Studies >Analysis of Factors Affecting the Severity of Motorcycle Casualties in Phnom Penh Using a Bayesian Approach
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Analysis of Factors Affecting the Severity of Motorcycle Casualties in Phnom Penh Using a Bayesian Approach

机译：贝叶斯方法分析影响金边摩托车伤亡人数的因素

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The number of motorcyclist fatalities in Cambodia accounted for almost 70% in 2010 and 2011. The contributing factors to the severity of motorcycle casualties should be identified to provide worthwhile information for planning or policy making to reduce the severity of motorcyclist crashes. The aim of this research study is to analyze the factors affecting the severity of motorcycle casualties in Phnom Penh, the capital city of Cambodia, using a Bayesian approach to apply the ordered probit (OP) model, which is called the Bayesian OP (BOP) model. The advantage of the BOP approach is to allow the researchers to use prior information about the explanatory variables in fitting the models. Unlike OP, BOP produces reasonable estimated coefficients compared with OP when the sample size is small. This study found that male drivers, middle-aged groups (25 to 59 years), speeding, nighttime, peak hour, weekends, heavy truck crash opponent, crashing alone, and head-on collisions are more likely to result in higher levels of injury severity relative to its reference case.
机译：在2010年和2011年，柬埔寨电单车司机的死亡人数几乎占70％。应确定造成摩托车伤亡严重程度的因素，以便为规划或制定政策提供有价值的信息，以减少电单车司机撞车的严重程度。这项研究的目的是使用贝叶斯方法应用有序概率模型（OP），即贝叶斯OP（BOP），分析影响柬埔寨首都金边的摩托车伤亡严重性的因素。模型。 BOP方法的优点是允许研究人员使用有关解释变量的先验信息来拟合模型。与OP不同，与BOP相比，当样本量较小时，BOP产生合理的估计系数。这项研究发现，男性驾驶员，中年人群（25至59岁），超速驾驶，夜间，繁忙时间，周末，重型卡车撞车对手，独自撞车和正面碰撞更可能导致更高程度的伤害。相对于参考案例的严重性。

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