Ensemble Stacking with the Multi-Layer Perceptron Neural Network Meta-Learner for Passenger Train Delay Prediction

Accurately predicting delays is crucial for improving passenger service quality and railway traffic management. The import of artificial neural networks into train delay prediction helps to improve prediction accuracy. In this paper, we propose a novel stacking ensemble regression model (ST-NN) that uses MultiLayer Perceptron’s (MLP) neural networks as single learners and MLP as the meta-learner, which enhances the accuracy of Passenger Train arrival delay prediction time in minutes. We evaluated the model performance using Amtrak data to compare with other combinations of stacking ensembles and single learners including, Decision Trees (DT), Random Forest (RF), Gradient Boosting Machines (GBM), XGBoost (XGB), LightGBM (LGBM), regression algorithms, Artificial Neural Network (ANN), and MLP to determine enhanced model accuracy of our model. The experiments demonstrate that our ST-NN regression model significantly improves model evaluation indicators by producing a 63.4-82.37% decrease in Mean Absolute Error. Furthermore, the accuracy outperforms the best benchmark models regarding train delay prediction.