Inclement winter weather such as snow, sleet, and freezing rain significantly impacts roadway safety. To assess the safety implications of winter weather, maintenance operations, and traffic operations, various crash frequency models have been developed. In this study, several datasets, including for weather, snowplow operations, and traffic information, were combined to develop a robust crash frequency model for winter weather conditions. When developing statistical models using such large-scale multivariate datasets, one of the challenges is to determine which explanatory variables should be included in the model. This paper presents a feature selection framework using a machine-learning algorithm known as the Boruta algorithm and exhaustive search to select a list of variables to be included in a negative binomial crash frequency model. This paper’s proposed feature selection framework generates consistent and intuitive results because the feature selection process reduces the complexity of interactions among different variables in the dataset. This enables our crash frequency model to better help agencies identify effective ways to improve roadway safety via winter maintenance operations. For example, increased plowing operations before the start of storms are associated with a decrease in crash rates. Thus, pretreatment operations can play a significant role in mitigating the impact of winter storms.

中文翻译：

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使用特征选择算法开发冬季天气状况的道路安全模型

严峻的冬季天气（如雪，雨夹雪和冻雨）严重影响道路安全。为了评估冬季天气，维护运营和交通运营的安全隐患，已经开发了各种碰撞频率模型。在这项研究中，包括天气，扫雪作业和交通信息在内的几个数据集被组合起来，以开发出针对冬季天气状况的稳健的撞车频率模型。使用此类大规模多元数据集开发统计模型时，面临的挑战之一是确定模型中应包含哪些解释变量。本文提出了一种特征选择框架，该框架使用称为Boruta算法的机器学习算法和穷举搜索来选择要包含在负二项式碰撞频率模型中的变量列表。本文提出的特征选择框架产生一致且直观的结果，因为特征选择过程降低了数据集中不同变量之间交互的复杂性。这使我们的碰撞频率模型能够更好地帮助机构确定通过冬季维护操作来提高道路安全性的有效方法。例如，在暴风雨开始之前增加耕种操作会导致坠毁率降低。因此，预处理操作可以在缓解冬季风暴的影响方面发挥重要作用。这使我们的碰撞频率模型能够更好地帮助机构确定通过冬季维护操作来提高道路安全性的有效方法。例如，在暴风雨开始之前增加耕种操作会导致坠毁率降低。因此，预处理操作可以在缓解冬季风暴的影响方面发挥重要作用。这使我们的碰撞频率模型能够更好地帮助机构确定通过冬季维护操作来提高道路安全性的有效方法。例如，在暴风雨开始之前增加耕种操作会导致坠毁率降低。因此，预处理操作可以在缓解冬季风暴的影响方面发挥重要作用。