This study describes the experimental application of Machine Learning techniques to build prediction models that can assess the injury risk associated with traffic accidents. This work uses an freely available data set of traffic accident records that took place in the city of Porto Alegre RS (Brazil) during the year of 2013. This study also provides an analysis of the most important attributes of a traffic accident that could produce an outcome of injury to the people involved in the accident.
@cite_10: This research tries to view accident data collection and analysis as a system that requires a special view towards understanding the whole and making sense out of it for improved decision making in the effort of reducing the problem of road safety. Under the umbrella of an information architecture research for road safety in developing countries, the objective of this machine learning experimental research is to explore and predict the role of road users on possible injury risks. The research employed Classification and Adaptive Regression Trees (CART) and RandomForest approaches. To identify relevant patterns and illustrate the performance of the techniques for the road safety domain, road accident data collected from Addis Ababa Traffic Office is exposed to many sided analyses. Empirical results showed that the models could classify accidents with promising accuracy.
@cite_10 explored a rich data set, comprising of 14.254 accident cases described with 48 attributes containing information related to road users (drivers, pedestrians and passengers), vehicles and road. In their study, two predictive modeling methods were used: CART and Random Forests. The experimental results done using CART analysis to assess the injury risk, scored with respect to the area under the ROC curve (AUC) a result of 0.8827. While running Random Forests, the authors also found that the age of the victim, victim occupation, among others, were the attributes with the most predictive power.