Spatial modelling of accidents risk caused by driver drowsiness with data mining algorithms

Driver drowsiness causes many road accidents, and preparing a risk map of these accidents with spatial criteria and data mining algorithms highlights accident points well. In this study, accidents risk caused by driver drowsiness in Qazvin province, Iran, was modelled using decision tree (DT), random forest (RF) and support-vector regression (SVR) algorithms in GIS environment. Seven spatial criteria including road segment length, road width, slope angle, speed limit, land use/cover, distance to service area and distance to speed camera were selected as effective criteria in modelling. The effect of criteria in modelling was applied using a fuzzy method, and three risk maps were prepared. Evaluation with ROC-AUC showed that the AUC for RF, SVR and DT models were 0.904, 0.863 and 0.805, respectively, and the RF model overall had the best performance. Examining the importance of criteria showed that the speed limit was the most important criterion for modelling.

Automated summary

In this study, the risk of accidents caused by driver drowsiness in Qazvin province, Iran, was modeled using decision tree, random forest, and support-vector regression algorithms in a GIS environment. Seven spatial criteria were selected as effective criteria in modeling, with speed limit identified as the most important criterion for modeling. The random forest model showed the best overall performance with an AUC value of 0.904.