Journal
INFORMATION SCIENCES
Volume 516, Issue -, Pages 234-253Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2019.12.053
Keywords
Dynamical systems; Multi-attribute data; Deep learning; Train delay prediction
Categories
Funding
- National Nature Science Foundation of China [71871188, U1834209]
- Science & Technology Department of Sichuan Province [2018JY0567]
- Doctoral Innovation Fund Program of Southwest Jiaotong University [D-CX201827]
- China Scholarship Council [201707000038]
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Dynamical systems that contain moving objects generate multi-attribute data, including static, time-series, and spatiotemporal formats. The diversity of the data formats creates challenges for the accurate modeling of these systems, for example, the state/location/trajectory prediction of moving objects. We developed a deep learning (DL) approach that combines 3-dimensional convolutional neural networks (3D CNN), long short-term memory (LSTM) recurrent neural network, and fully-connected neural network (FCNN) architectures to address this problem. The proposed model, named CLF-Net, uses individual factors with different attributes as input to achieve better predictions. The spatiotemporal features are fed into the 3D CNN, the time-series variables are fed into the LSTM, and the non-time-series factors are fed into the FCNN, respectively. A case study of train delay prediction for four railway lines with different operational features shows that the CLF-Net outperforms conventional machine learning models and the state-of-the-art DL models with regard to the performance metrics of the root mean squared error and mean absolute error. (C) 2019 Elsevier Inc. All rights reserved.
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