Intelligent Prediction of Train Delay Changes and Propagation Using RVFLNs With Improved Transfer Learning and Ensemble Learning

In railway operation, train delays may occur due to various reasons (e.g. severe weather, infrastructure failure, human factors, etc.) and may change and spread rapidly to subsequent trains. Prediction of train delay changes and propagation is important to provide decision-making support for railway dispatchers to reschedule. In this paper, a multi-stage intelligent method is proposed for predicting the dynamic changes and propagation of train delays using random vector functional-link networks (RVFLNs) with improved transfer learning and ensemble learning. First, to improve the prediction performance of the single RVFLNs model, a novel improved Stacking ensemble learning RVFLNs (SRN) regression algorithm is proposed for prediction modeling of the delay changes. Then, to ensure the classification accuracy of unlabeled and class-imbalanced train data, an improved transfer learning RVFLNs (ITRN) classifier is proposed to decide whether the initial delay will lead to associated delays, with the aid of the improved SMOTE algorithm for processing imbalanced data. If associated delays are identified by the ITRN classifier, the proposed SRN algorithm will be further to predict the subsequent associated delays. By iterating the classification and prediction procedures, the propagation range and chain of train delays can be obtained. The effectiveness and practicability of the proposed method are verified by using two experiments against actual train graph data from different railway lines.