期刊
INTERNATIONAL JOURNAL OF RAIL TRANSPORTATION
卷 -, 期 -, 页码 -出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/23248378.2022.2094484
关键词
Train arrival delay; multi-output; LSTM; Bayesian optimization
资金
- National Natural Science Foundation of China [U1834209]
- Science & Technology Department of Sichuan Province [2022NSFSC1867]
- Fundamental Research Funds for the Central Universities [2682021CX051]
This paper proposes a Bayesian optimization-based multi-output deep learning model to predict the arrival delays of multiple trains simultaneously, and the test results show that the proposed model outperforms the standard train delay prediction benchmark model.
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.
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