Prediction of fatal traffic accidents using one-class SVMs: a case study in Eskisehir, Turkey

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.

Automated summary

This study investigates the applicability of one-class classification models in traffic accident prediction and finds that the one-class SVM model outperforms binary classification models, with alarm rates confirming the suitability of OCC for accident prediction.