Trafic incidents detection  using machine learning techniques

Timely and accurate detection of traffic incidents can effectively reduce personal casualties and property losses, and improve the ability of macro-control and scientific decision-making of traffic. The unbalance of traffic incident data has a great influence on the detection effect. Therefore, a traffic incident detection method based on factor analysis and weighted random forest (FA-WRF) is designed. Through the analysis of the change rule of traffic flow parameters to build the initial incident variable. The factor analysis (FA) method is used to reduce the dimension of the initial incident variables. Using Bootstrap improved algorithm to predetermine the data extraction standard of the training set. The MCC coefficient value is calculated for the classification effect of the decision tree after training, and is assigned to each tree as a weight value, so as to ensure that the trees with better classification ability have more voting power in the voting process, thus improve the overall classification performance of the random forest (RF) algorithm for unbalanced data. The detection performance is evaluated by the common criteria including the detection rate, the false alarm rate, the classification rate and the area under the curve of the receiver operating characteristic. The experimental results indicate that the model based on FA-WRF has the better classification effect. Meanwhile it is competitive in processing unbalanced data classification compared with Support Vector Machine.

Keywords: Random Forest, Logistic Regression, XGBoost, and Support Vector Machines.