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首页> 外文期刊>The Open Transportation Journal >Using Generalized Linear Mixed Models to Predict the Number of Roadway Accidents: A Case Study in Hamilton County, Tennessee
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Using Generalized Linear Mixed Models to Predict the Number of Roadway Accidents: A Case Study in Hamilton County, Tennessee

机译：使用广义的线性混合模型预测道路事故的数量：田纳西州汉密尔顿县的案例研究

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Introduction: A method for identifying significant predictors of roadway accident counts has been presented. This process is applied to real-world accident data collected from roadways in Hamilton County, TN. Methods: In preprocessing, an aggregation procedure based on segmenting roadways into fixed lengths has been introduced, and then accident counts within each segment have been observed according to predefined weather conditions. Based on the physical roadway characteristics associated with each individual accident record, a collection of roadway features is assigned to each segment. A mixed-effects Negative Binomial regression form is assumed to approximate the relationship between accident counts and several explanatory variables including roadway characteristics, weather conditions, and several interactions between them. Standard diagnostics and a validation procedure show that our model form is properly specified and suitably fits the data. Results: Interpreting interaction terms leads to the follow findings: 1) rural roads with cloudy conditions are associated with relative increases in accident frequency; 2) lower/moderate AADT and rainy weather are associated with relative decreases in accident frequency, while high AADT and rain are associated with relative increases in accident frequency; 3) higher AADT and wider pavements are associated with relative increases in accident frequency; and 4) higher speed limits in residential areas are associated with relative increases in accident frequency. Conclusion: Results illustrate the complicated relationship between accident frequency and both roadway features and weather. Therefore, it is not sufficient to observe the effects of weather and roadway features independently as these variables interact with one another.
机译：简介：介绍了一种识别巷道事故计数大量预测因素的方法。该过程适用于从汉密尔顿县TN的道路收集的现实事故数据。方法：在预处理中，已经引入了基于分段道路进入固定长度的聚集过程，然后根据预定义的天气条件观察每个段内的事故计数。基于与每个人事故记录相关的物理道路特征，将巷道特征的集合分配给每个段。假设混合效应负二进制回归形式近似意外计数与几个解释性变量之间的关系，包括道路特征，天气条件以及它们之间的几个相互作用。标准诊断和验证程序表明，我们的模型形式被正确指定，适当地符合数据。结果：解释互动条款导致遵循调查结果：1）具有多云条件的农村道路与事故频率相对增加有关; 2）较低/中度AADT和多雨天气与事故频率相对减少有关，而高AADT和雨与事故频率的相对增加有关; 3）较高的Aadt和更宽的路面与事故频率的相对增加相关; 4）住宅区的更高速度限制与事故频率的相对增加相关。结论：结果说明了事故频率与道路特征与天气的复杂关系。因此，当这些变量相互交互时，不足以观察天气和巷道特征的影响。

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