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Marine traffic model based on cellular automaton: Considering the change of the ship’s velocity under the influence of the weather and sea

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  • Qi, Le
  • Zheng, Zhongyi
  • Gang, Longhui

Abstract

It was found that the ships’ velocity change, which is impacted by the weather and sea, e.g., wind, sea wave, sea current, tide, etc., is significant and must be considered in the marine traffic model. Therefore, a new marine traffic model based on cellular automaton (CA) was proposed in this paper. The characteristics of the ship’s velocity change are taken into account in the model. First, the acceleration of a ship was divided into two components: regular component and random component. Second, the mathematical functions and statistical distribution parameters of the two components were confirmed by spectral analysis, curve fitting and auto-correlation analysis methods. Third, by combining the two components, the acceleration was regenerated in the update rules for ships’ movement. To test the performance of the model, the ship traffic flows in the Dover Strait, the Changshan Channel and the Qiongzhou Strait were studied and simulated. The results show that the characteristics of ships’ velocities in the simulations are consistent with the measured data by Automatic Identification System (AIS). Although the characteristics of the traffic flow in different areas are different, the velocities of ships can be simulated correctly. It proves that the velocities of ships under the influence of weather and sea can be simulated successfully using the proposed model.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:483:y:2017:i:c:p:480-494
    DOI: 10.1016/j.physa.2017.04.125
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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. Jin, Cheng-Jie & Yang, Wenzhang & Jiang, Rui & Liao, Peng & Zheng, Shiteng & Wang, Hao, 2023. "Vessel-following dynamics: Experiment and modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).

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