Comparison Analysis of Tree Based and Ensembled Regression Algorithms for Traffic Accident Severity Prediction
This work addresses traffic safety management by predicting accident severity, but it is incremental as it applies existing methods to a specific dataset.
The study compared tree-based ensemble models and statistical ensemble methods for predicting traffic accident severity, finding that Random Forest performed best with 0.974 accuracy, 0.954 precision, 0.930 recall, and 0.942 F-score using 20 significant features.
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.