Prediction Methods for Traffic Accidents Using Formal Concept Analysis and Machine Learning

Shogo KOTANI, Masaki NAKAMURA, Kazutoshi SAKAKIBARA, Tatsuo MOTOYOSHI, Keisuke HOSHIKAWA
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Abstract

Although the number of traffic accidents is decreasing in Toyama prefecture, the number of accidents related to elderly people is more than the average of Japan. Toward prevention of traffic accidents in consideration of coming high-aging society, we propose a way to analyze traffic accidents by using a data analysis method, called formal concept analysis (FCA), which is known to be useful to analyze relationships between data's attributes. We also propose a way to use FCA for a prediction of traffic accidents by using machine learning (ML). It is known that selection of features is important to obtain higher-precision ML models. We use FCA to obtain suitable features for ML.
基于形式概念分析和机器学习的交通事故预测方法
富山县的交通事故数量虽然在减少,但与老人有关的事故数量却超过了日本的平均水平。考虑到即将到来的高老龄化社会,为了预防交通事故,我们提出了一种使用数据分析方法来分析交通事故的方法,称为形式概念分析(FCA),它有助于分析数据属性之间的关系。我们还提出了一种使用FCA通过机器学习(ML)来预测交通事故的方法。众所周知,特征的选择对于获得更高精度的机器学习模型非常重要。我们使用FCA来获得适合ML的特征。
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