Development of a maximum likelihood regression tree-based model for predicting subway incident delay
We develop a maximum likelihood regression tree-based model to predict subway incident delays. An AFT model is assigned to each terminal node in the maximum likelihood regression tree. Our tree-based model outperforms the traditional AFT models with fixed and random effects. Our tree-based model can account for the heterogeneity effect as well as avoid the over-fitting problem.
maximum likelihood regression tree subway incidents delay accelerated failure time