Abstract

The objective of this study is to investigate the applicability of one-class classification (OCC) models in traffic accident prediction. So far, the accident prediction problem has been considered as a binary classification problem in the literature. Since real accident datasets often involve only accident situations, we thought that OCC could provide more successful predictions. In this study, the fatal accidents, which occurred in Eskisehir, Turkey between 2005 and 2012 was considered. The accidents were tried to be predicted using one-class Support Vector Machine (SVM). In order to compare the performance of the OCC model, some most used binary classifiers were used. Additionally, a non-accident generation procedure was defined to add non-accident cases to the accident dataset. After training, tests were performed using one-class and binary classifiers for the test set generated from the extended dataset. As a result, the one-class SVM model outperformed the binary classification models. Besides, true and false accident alarms were also calculated. The alarm rates obtained with the OCC model also demonstrated that OCC can be suitable for accident prediction rather than binary classification.

摘要

本研究的目的是研究一类分类 (OCC) 模型在交通事故预测中的适用性。到目前为止，事故预测问题在文献中一直被认为是一个二元分类问题。由于真实的事故数据集通常只涉及事故情况，我们认为 OCC 可以提供更成功的预测。在这项研究中，考虑了 2005 年至 2012 年期间在土耳其埃斯基谢希尔发生的致命事故。尝试使用一类支持向量机（SVM）来预测事故。为了比较 OCC 模型的性能，使用了一些最常用的二元分类器。此外，还定义了一个非事故生成程序，以将非事故案例添加到事故数据集中。训练结束后，对于从扩展数据集生成的测试集，使用一类和二元分类器进行测试。结果，一类 SVM 模型优于二元分类模型。此外，还计算了真假事故警报。使用 OCC 模型获得的报警率也表明 OCC 可以适用于事故预测而不是二元分类。