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k-GCN-LSTM: A k-hop Graph Convolutional Network and Long–Short-Term Memory for ship speed prediction

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  • Zhao, Jiansen
  • Yan, Zhongwei
  • Chen, Xinqiang
  • Han, Bing
  • Wu, Shubo
  • Ke, Ranxuan

Abstract

Ship speed information plays an important role in the maritime intelligent transportation system, and ship speed prediction has attracted extensive attention in maritime community. Previous studies suggest that ship speed is mainly affected by her previous movement status, regardless the interferences from neighboring locations. To address the issue, a novel deep learning framework is proposed to predict ship speed via the support of k-hop graph convolutional network (k-GCN) and long–short term memory (LSTM). Firstly, a ship network model is established with a graph network. The nodes in the proposed networks demonstrate ship trajectory segments, whilst the segments are connected to obtain edges. Secondly, the GCN is used to capture the spatial correlation between the nodes, and the LSTM is used to exploit spatial–temporal correlation among the nodes. Finally, the proposed k-GCN-LSTM model is used to predict ship speed, which are further verified on different real-world ship trajectory data against with other models. Results suggest that the proposed model obtains satisfied prediction performance in terms of typical error measurement indicators. The research findings help maritime traffic participants better avoid potential collisions and enhance maritime traffic safety.

Suggested Citation

  • Zhao, Jiansen & Yan, Zhongwei & Chen, Xinqiang & Han, Bing & Wu, Shubo & Ke, Ranxuan, 2022. "k-GCN-LSTM: A k-hop Graph Convolutional Network and Long–Short-Term Memory for ship speed prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    • Handle: RePEc:eee:phsmap:v:606:y:2022:i:c:s0378437122006872
    DOI: 10.1016/j.physa.2022.128107
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    References listed on IDEAS

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    1. Zhao, Jiansen & Lu, Jinquan & Chen, Xinqiang & Yan, Zhongwei & Yan, Ying & Sun, Yang, 2022. "High-fidelity data supported ship trajectory prediction via an ensemble machine learning framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    2. Yang, Yang & He, Kun & Wang, Yun-peng & Yuan, Zhen-zhou & Yin, Yong-hao & Guo, Man-ze, 2022. "Identification of dynamic traffic crash risk for cross-area freeways based on statistical and machine learning methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    3. Qi, Le & Zheng, Zhongyi & Gang, Longhui, 2017. "Marine traffic model based on cellular automaton: Considering the change of the ship’s velocity under the influence of the weather and sea," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 480-494.
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    Cited by:

    1. Wang, Yukuan & Liu, Jingxian & Liu, Ryan Wen & Wu, Weihuang & Liu, Yang, 2023. "Interval prediction of vessel trajectory based on lower and upper bound estimation and attention-modified LSTM with bayesian optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    2. Ma, Long & Huang, Ling & Shi, Huifeng, 2023. "Multi-node wind speed forecasting based on a novel dynamic spatial–temporal graph network," Energy, Elsevier, vol. 285(C).
    3. Shao, Feng & Shao, Hu & Wang, Dongle & Lam, William H.K. & Cao, Shuhan, 2023. "A generative model for vehicular travel time distribution prediction considering spatial and temporal correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
    4. Shao, Feng & Shao, Hu & Wang, Dongle & Lam, William H.K., 2024. "A multi-task spatio-temporal generative adversarial network for prediction of travel time reliability in peak hour periods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).

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