Real-Time Traffic Accident Severity Prediction Using Data Mining Technologies

Xiaoling Xia, Bing Nan, Cui Xu
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Abstract

With the urban transport development in the recent years, frequent traffic accidents and other problems need to be improved. Understanding the causes of traffic accidents and making an early alarm model for the driver will be crucial to solve traffic accident problems in some way. In this paper, we focus on the factors that can be collected in real-time and process the factors using data mining technologies. Finally, we evaluate the performance of different classifiers. The results show that our feature processing is effective in improving the classification accuracy and we can use the model to predict the severity of traffic accident furthermore prevent traffic accidents.
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基于数据挖掘技术的交通事故严重程度实时预测
随着近年来城市交通的发展,交通事故频发等问题亟待改善。了解交通事故的原因,为驾驶员建立早期报警模型,在某种程度上对于解决交通事故问题至关重要。本文主要研究可实时采集的因素,并利用数据挖掘技术对这些因素进行处理。最后,我们评估了不同分类器的性能。结果表明,我们的特征处理有效地提高了分类精度,可以利用该模型预测交通事故的严重程度,进而预防交通事故的发生。
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