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

Abstract

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 metalearner. 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.

Simplifying YOLOv5 for deployment in a real crop monitoring setting

“…Model simplification and compression methods have been proposed to reduce the computational requirement of deep learning object detection models and increase detection speed, while maintaining high detection accuracy. Available model simplification and compression techniques include tensor decomposition [10][11][12][13], network pruning [14][15][16][17][18][19], knowledge distillation [20][21][22][23], and neural architecture search (NAS) [24][25][26][27]. Tensor decomposition techniques such as low-rank matrix decomposition and tensorized decomposition simplify complex models by reducing a weight matrix or high-dimensional tensor to multiple low-rank matrices or low-dimensional tensors respectively [28].…”