A Ship Trajectory Prediction Method Based on an Optuna–BILSTM Model

Author:

Zhou Yipeng1,Dong Ze1,Bao Xiongguan1

Affiliation:

1. Maritime Academy, Ningbo University, Ningbo 315000, China

Abstract

In the field of maritime traffic management, overcoming the challenges of low prediction accuracy and computational inefficiency in ship trajectory prediction is crucial for collision avoidance. This paper presents an advanced solution using a deep bidirectional long- and short-term memory network (BILSTM) and the Optuna hyperparameter automatic optimized framework. Utilizing automatic identification system (AIS) data to analyze ship navigation patterns, the study applies Optuna to fine-tune the hyperparameters of the BILSTM network to improve prediction accuracy and efficiency. The developed Optuna–BILSTM model shows a remarkable 7% increase in prediction accuracy over traditional back propagation (BP) neural networks and standard BILSTM models. These results not only improve ship navigation and safety but also have significant implications for the development of autonomous ship collision avoidance systems, marking a significant step toward safer and more efficient maritime traffic management.

Funder

Ningbo International Science and Technology Cooperation Project: Theoretical and Technological Research on Cooperative Traffic Control of Port-Collector-Supply Highway

National Natural Science Foundation of China (NNSF) Top Project: Traffic State Estimation and Control Methods for Port-Collector-Supply Roads in Project Networked Vehicle Environment

Publisher

MDPI AG

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