Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping

Perceiving the future trend of Vessel Traffic Flow (VTF) in advance has great application values in the maritime industry. However, using such big data from the Automatic Identification System (AIS) for accurate VTF prediction remains challenging. Deep training networks can learn valuable features f...

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Main Authors: Li, Yan, Liang, Maohan, Li, Huanhuan, Yang, Zaili, Du, Liang, Chen, Zhongshuo
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/174055
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1740552024-03-15T15:33:33Z Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping Li, Yan Liang, Maohan Li, Huanhuan Yang, Zaili Du, Liang Chen, Zhongshuo School of Civil and Environmental Engineering Engineering Vessel traffic flow prediction Automatic identification system Perceiving the future trend of Vessel Traffic Flow (VTF) in advance has great application values in the maritime industry. However, using such big data from the Automatic Identification System (AIS) for accurate VTF prediction remains challenging. Deep training networks can learn valuable features from extensive historical data. This paper proposes a new learning-based prediction network, improved Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with similarity grouping, including three views. To effectively enable the training network to capture the temporal and periodic (i.e. a spatial attribute) change characteristics of VTF, the CNN and LSTM are employed to compose spatial and temporal views, respectively. Hence, the original one-dimensional data is transformed into a matrix (hour of the day ✕ day) to adapt the input of the proposed methodology. In practical applications, VTF of multiple adjacent target regions need to be predicted simultaneously, and the changes of VTF in different areas may influence each other. To explore their hidden relationships, the similarity grouping view aims to find the target area that exhibits the most similarity with the VTF change trend of the current research area. Furthermore, similar information is combined with the features generated from the other two views to obtain the prediction results. In summary, the new advantage lies in mining the spatiotemporal attributes of data and fusing the similarity information of adjacent regions. Comparative experiments with eleven other methods on realistic VTF datasets show that the proposed method demonstrates superior prediction accuracy and stability performance. Published version This work is supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 864724) and a Royal Society International Exchanges 2021 Cost Share (NSFC) project (IEC\NSFC\211211). 2024-03-13T00:50:15Z 2024-03-13T00:50:15Z 2023 Journal Article Li, Y., Liang, M., Li, H., Yang, Z., Du, L. & Chen, Z. (2023). Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping. Engineering Applications of Artificial Intelligence, 126, 107012-. https://dx.doi.org/10.1016/j.engappai.2023.107012 0952-1976 https://hdl.handle.net/10356/174055 10.1016/j.engappai.2023.107012 2-s2.0-85168415005 126 107012 en Engineering Applications of Artificial Intelligence © 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Vessel traffic flow prediction
Automatic identification system
spellingShingle Engineering
Vessel traffic flow prediction
Automatic identification system
Li, Yan
Liang, Maohan
Li, Huanhuan
Yang, Zaili
Du, Liang
Chen, Zhongshuo
Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping
description Perceiving the future trend of Vessel Traffic Flow (VTF) in advance has great application values in the maritime industry. However, using such big data from the Automatic Identification System (AIS) for accurate VTF prediction remains challenging. Deep training networks can learn valuable features from extensive historical data. This paper proposes a new learning-based prediction network, improved Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with similarity grouping, including three views. To effectively enable the training network to capture the temporal and periodic (i.e. a spatial attribute) change characteristics of VTF, the CNN and LSTM are employed to compose spatial and temporal views, respectively. Hence, the original one-dimensional data is transformed into a matrix (hour of the day ✕ day) to adapt the input of the proposed methodology. In practical applications, VTF of multiple adjacent target regions need to be predicted simultaneously, and the changes of VTF in different areas may influence each other. To explore their hidden relationships, the similarity grouping view aims to find the target area that exhibits the most similarity with the VTF change trend of the current research area. Furthermore, similar information is combined with the features generated from the other two views to obtain the prediction results. In summary, the new advantage lies in mining the spatiotemporal attributes of data and fusing the similarity information of adjacent regions. Comparative experiments with eleven other methods on realistic VTF datasets show that the proposed method demonstrates superior prediction accuracy and stability performance.
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Li, Yan
Liang, Maohan
Li, Huanhuan
Yang, Zaili
Du, Liang
Chen, Zhongshuo
format Article
author Li, Yan
Liang, Maohan
Li, Huanhuan
Yang, Zaili
Du, Liang
Chen, Zhongshuo
author_sort Li, Yan
title Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping
title_short Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping
title_full Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping
title_fullStr Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping
title_full_unstemmed Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping
title_sort deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping
publishDate 2024
url https://hdl.handle.net/10356/174055
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