A multi-output deep learning model based on Bayesian optimization for sequential train delays prediction
Jie Luo, Ping Huang, Qiyuan Peng
Abstract
Accurate train arrival delay predictions can provide timely information for passengers and train dispatchers. Previous work mainly focused on predicting the delay of a single train, which is not enough to assist dispatchers, because making more comprehensive decisions considering more trains needs more future delay information of a group of trains. Therefore, this paper proposes a Bayesian optimization-based multi-output deep learning model, which includes a fully connected neural network (FCNN) and two long–short-term memory (LSTM) components, to predict the arrival delays of multiple trains simultaneously. The proposed model is calibrated and validated with the train operation data from the Wuhan–Guangzhou (W-G) high-speed railway. The test results show that the mean absolute error and the root mean square error of the proposed model is 1.379 and 2.021 min, over the four subsequent trains. Moreover, the proposed model outperforms the standard train delay prediction benchmark model.