  Rapid increase of traffic volume on urban roads over time has changed the
traffic scenario globally. It has also increased the ratio of road accidents
that can be severe and fatal in the worst case. To improve traffic safety and
its management on urban roads, there is a need for prediction of severity level
of accidents. Various machine learning models are being used for accident
prediction. In this study, tree based ensemble models (Random Forest, AdaBoost,
Extra Tree, and Gradient Boosting) and ensemble of two statistical models
(Logistic Regression Stochastic Gradient Descent) as voting classifiers are
compared for prediction of road accident severity. Significant features that
are strongly correlated with the accident severity are identified by Random
Forest. Analysis proved Random Forest as the best performing model with highest
classification results with 0.974 accuracy, 0.954 precision, 0.930 recall and
0.942 F-score using 20 most significant features as compared to other
techniques classification of road accidents severity.
