Time series and support vector machines to predict powered-two-wheeler accident risk and accident type propensity: A combined approach

Abstract

Predicting road accident probability by exploiting high-resolution traffic data has been a continuously researched topic in the last years. However, there is no specific focus on powered-two-wheelers. Furthermore, urban arterials have not received adequate attention so far because the majority of relevant studies considers freeways. This study aims to contribute to the current knowledge by utilizing support vector machine (SVM) models for predicting powered-two-wheeler (PTW) accident risk and PTW accident type propensity on urban arterials. The proposed methodology is applied on original and transformed time series of real-time traffic data collected from urban arterials in Athens, Greece, for 2006 to 2011. Findings suggest that PTW accident risk and PTW accident type propensity can be adequately defined by the prevailing traffic conditions. When predicting PTW accident risk, the original traffic time series performed better than the transformed time series. On the other hand, when PTW accident type is investigated, neither of the two approaches clearly outperformed the other, but the transformed time series perform slightly better. The results of the study indicate that the combination of SVM models and time-series data can be used for road safety purposes especially by utilizing real-time traffic data.