Study of Traffic Incident Detection with Machine Learning Methods

Traffic incidents occur frequently, which lead to inefficient road operations and and bring serious harm to society and individuals. An effective detection model is built for the traffic incident detection, including Synthetic Minority Oversampling Technique (SMOTE) as an oversampling technique, Tomek links as a data cleaning technique and Random Forest as a classifier. The experimental results indicate that the proposed method improves the overall performance than other methods in the automatic traffic incident detection for unbalanced datasets significantly.