Research on ship traffic flow prediction based on GTO-CNN-LSTM

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Research on ship traffic flow prediction based on GTO-CNN-LSTM

Ding, Runzhen / Xie, Haibo / Dai, Cheng / Qiao, Guanzhou

To gain a more accurate understanding of maritime traffic flow, this paper proposes a ship traffic flow prediction method based on GTO-CNN-LSTM. The method utilizes ship traffic flow data, which is preprocessed and used as input for CNN-LSTM. By establishing a connection between the input and output through high-dimensional mapping, ship traffic flow is predicted. To further optimize the chosen model, the Artificial Gorilla Troop optimizer (GTO) is employed to adjust the model's hyperparameters, selecting the optimal ones for maximizing the model's performance. The method is validated using actual data from the vicinity of the Port of Los Angeles. The results demonstrate that the model achieves a prediction accuracy of 95.82%. The experiment confirms that the CNN-LSTM prediction model optimized with the GTO exhibits higher accuracy.

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Dokumentinformationen

Titel :

Research on ship traffic flow prediction based on GTO-CNN-LSTM

Beteiligte:

Ghanizadeh, Ali Reza (Herausgeber:in) / Jia, Hongfei (Herausgeber:in) / Ding, Runzhen (Autor:in) / Xie, Haibo (Autor:in) / Dai, Cheng (Autor:in) / Qiao, Guanzhou (Autor:in)

Kongress:

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023) ; 2023 ; Dalian, China

Erschienen in:

Proc. SPIE ; 13064

Erscheinungsdatum :

2024-02-20

ISBN :

9781510674462

ISSN :

0277-786X

DOI :

https://doi.org/10.1117/12.3015728