Multiclass Classification by Various Machine Learning Algorithms and Interpretation of the Risk Factors of Pedestrian Accidents Using Explainable AI

Pedestrian injuries and fatalities due to traffic accidents remain at a high level. Therefore, the need for efforts to reduce this ratio is on the rise. Machine learning models can facilitate the exploration of the various factors that influence the occurrence of pedestrian accidents. In this study, we used data on pedestrian traffic accidents classified into three categories of injury severity: minor, severe, and fatal. To compare the performance of various types of models, logistic regression, Naïve Bayes, XGBoost, CatBoost, and LightGBM were used for analysis. Five machine learning methods were applied to the analysis, and hyperparameter tuning was performed to improve the performance of the model. The performances of the five models were 0.688, 0.577, 0.705, 0.708, and 0.707, respectively, and LightGBM showed the best classification accuracy at 0.708 in this study. Based on SHAP (Shapley additive explanation), one of the explainable artificial intelligence (XAI) techniques, we were able to obtain the variable importance of the LightGBM model, through which we identified the main factors affecting each level of injury severity. In addition, by using LIME (local interpretable model-agnostic explanation), another XAI technique, it was found that the age of the driver and pedestrian was the factor that had the most significant influence on the model’s classification prediction. Specifically, as the size of the vehicle increases, the severity of the accident increases. When the driver is older, the severity of the accident is small, and when the driver is young, the severity of the accident is high.