A Global Modeling Pruning Ensemble Stacking With Deep Learning and Neural Network Meta-Learner for Passenger Train Delay Prediction

Train Operators can improve railway passengers’ service quality and traffic management by accurately predicting travel arrangements and delays. Precise prediction of train delays is vital for creating feasible scheduled timetables. The import of pruning stacked ensemble deep neural networks into train delay prediction helps improve model prediction accuracy and computational time. In this study, we propose a novel pruning stacked ensemble learning model that uses pruned multilayer perceptron (MLP) neural networks as a meta-learner and heterogeneous neural networks as sub-models to improve the prediction accuracy for passenger train arrival delays and evaluate the model performance using Amtrak, the most extensive US passenger railroad service data, to determine the enhanced model accuracy. We used stacked generalization regression neural networks for artificial neural networks and deep learning neural networks to create the stacked ensemble models. We optimized the models using OPTUNA and produced a pruning stacked ensemble model using the best neural network sub-models and an MLP meta-learner. Our methodology comprises data preprocessing, feature engineering, modeling, case studies, and evaluation phases. Our experiments demonstrate that our proposed pruning stacked ensemble multilayer perceptron Neural Network (PST-NN) model has various degrees of improvement in model evaluation indicators compared to the existing model by 35.20%, 53.40% in terms of accuracy and prediction error and outperforms the benchmark models by 85.22% in terms prediction error in Passenger train delay prediction respectively, which provides a new approach to building models efficiently in Passenger train arrival delay prediction.