A hybrid model combining the advantages of mixture of experts and light gradient boosting machine for predicting secondary crash

Secondary crashes can lead to great casualties and economic losses. Although nonparametric models have achieved suitable effects on crash prediction in previous studies, neither single nonparametric models nor hybrid nonparametric models have made full use of the different advantages of the machine learning method and deep learning method. In this study, the hybrid model was developed by combining the Mixture of Experts (MoE) and the Light Gradient Boosting Machine (LightGBM). The original voting classifier cannot directly accept deep learning models as the basis classifier. Thus, a new voting classifier was constructed by MoE. The traffic data were analyzed and modeled through the LightGBM, and the advanced features in data were learned through the MoE. Results showed that the hybrid model developed in this study has better predictive performance in terms of AUC, specificity, and sensitivity than single machine learning models and other hybrid models. The results of the ablation experiment verified the validity of the submodels in the hybrid model, and the hybrid model obtained better prediction performance in original sampling than in smote sampling and undersampling. In addition, SHapley Additive exPlanation was used to analyze the secondary crash impact features.