A deep learning method to predict ship short-term trajectory for proactive maritime traffic management

Authors

Ahmad BahooToroody (ORCID: https://orcid.org/0000-0002-7126-4244)
Mingyang Zhang (ORCID: https://orcid.org/0000-0001-5820-2789)
Zhe‐Xiong Lian (ORCID: https://orcid.org/0000-0002-9525-1421)
Xu Du (ORCID: https://orcid.org/0000-0001-6822-0665)
Yuting Jiang (ORCID: https://orcid.org/0009-0006-0871-4982)
Quandang Ma

Publication Details

Journal: Reliability Engineering & System Safety
Published: 2025-08-08
DOI: https://doi.org/10.1016/j.ress.2025.111542
Citations: 2
Primary Topic: Maritime Navigation and Safety
Type: article
Field-Weighted Citation Impact: 2.29

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article

A deep learning method to predict ship short-term trajectory for proactive maritime traffic management

Ahmad BahooToroody, Mingyang Zhang, Zhe‐Xiong Lian, Xu Du et al.

Aug 8, 2025

2 citations

Reliability Engineering & System Safety

Maritime Navigation and Safety

2.29

article

A deep learning method to predict ship short-term trajectory for proactive maritime traffic management

Ahmad BahooToroody, Mingyang Zhang, Zhe‐Xiong Lian, Xu Du, Yuting Jiang, Quandang Ma

August 8, 2025article en

2 citations

Abstract

No abstract available for this paper.

Reliability Engineering & System SafetyVol. 265

Openalex Percentile: Top 11%

Maritime Navigation and Safety

2.29

DOI Open Access PDF OpenAlex

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A deep learning method to predict ship short-term trajectory for proactive maritime traffic management — Ahmad BahooToroody, Mingyang Zhang, et al. · Reliability Engineering & System Safety (2025) | TGRS Research Map | TGRS