An Approach to Truck Driving Risk Identification: A Machine Learning Method Based on Optuna Optimization - Zhaofei Wang; Hao Li; Qiuping Wang (IEEE Access)
In order to provide safe development of road freight traffic, this paper proposes a truck driving risk identification method based on Optuna optimization of machine learning model. First, the risk characterization indicators were extracted from the natural driving data of trucks, and the threshold value of each indicator was determined using a box plot-based method. Second, the truck driving risk was quantified into three categories of low level, medium level, and high level risk, and the unbalanced data were processed using a hybrid sampling algorithm. Finally, the tree-based decision tree (DT) model, random forest (RF) model, Light Gradient Boosting Machine (LightGBM) model and eXtreme Gradient Boosting (XGBoost) model were selected for training and Optuna was used for hyperparameter optimization of the model. The results are shown to indicate that the machine learning model based on Optuna optimization can effectively identify truck driving risks. Combining the running time, precision, recall, and F1-Score, the LightGBM model optimized based on the Tree-structured Parzen Estimator (TPE) algorithm has the best performance with a precision of 0.98. In addition, the speed mean has the highest feature importance of 14%, which needs to be focused on when preventing truck driving risks. The research results can provide policy support for transportation management departments to formulate risk control measures for trucks.