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Open AccessArticle

Using Deep Learning to Forecast Maritime Vessel Flows

1
Navigation College, Dalian Maritime University, Dalian 116026, China
2
Centre for Maritime Studies, National University of Singapore, Singapore 118414, Singapore
3
Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore 117576, Singapore
4
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1761; https://doi.org/10.3390/s20061761
Received: 13 February 2020 / Revised: 13 March 2020 / Accepted: 18 March 2020 / Published: 22 March 2020
Forecasting vessel flows is important to the development of intelligent transportation systems in the maritime field, as real-time and accurate traffic information has favorable potential in helping a maritime authority to alleviate congestion, mitigate emission of GHG (greenhouse gases) and enhance public safety, as well as assisting individual vessel users to plan better routes and reduce additional costs due to delays. In this paper, we propose three deep learning-based solutions to forecast the inflow and outflow of vessels within a given region, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and the integration of a bidirectional LSTM network with a CNN (BDLSTM-CNN). To apply those solutions, we first divide the given maritime region into M × N grids, then we forecast the inflow and outflow for all the grids. Experimental results based on the real AIS (Automatic Identification System) data of marine vessels in Singapore demonstrate that the three deep learning-based solutions significantly outperform the conventional method in terms of mean absolute error and root mean square error, with the performance of the BDLSTM-CNN-based hybrid solution being the best. View Full-Text
Keywords: maritime vessel flows; intelligent transportation systems; deep learning maritime vessel flows; intelligent transportation systems; deep learning
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Zhou, X.; Liu, Z.; Wang, F.; Xie, Y.; Zhang, X. Using Deep Learning to Forecast Maritime Vessel Flows. Sensors 2020, 20, 1761.

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