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Cited by 6Maritime Transport Emissions and EfficiencyMaritime Navigation and SafetyMaritime Ports and Logistics

Theory and Application of Vessel Speed Dynamic Control considering Safety and Environmental Factors

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Zhou, Tianrui, Hu, Qinyou, Hu, Zhihui, Zhi, Jiamao·2022·Journal of Advanced Transportation
DOI 10.1155/2022/5333171
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Abstract

The implementation of ship speed control is extremely important in the shipping industry. It is affected by various factors, such as water depth, obstacles, and environmental factors. Traditional speed control methods only consider geographical constraints, which is difficult to achieve the goal of safe navigation and maritime traffic efficiency simultaneously. Accordingly, a two-stage speed dynamic control model is proposed in this study. In the first stage, certain safety navigation factors, including obstacles, sea environment conditions, and limit of estimated time of arrival to destination port, are considered. In the second stage, the speed dynamic control model considering safety and environmental factors is established by combining multisource data and particle swarm optimisation algorithm. The model’s superiority and advantage are validated by experiments conducted on an ocean-going ship. The experimental results show that the proposed dynamic speed control model can reduce the ship’s fuel consumption and improve energy efficiency while ensuring the safety navigation. The study is anticipated to be used as a reference for speed dynamic control.

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  • ML can be used in ship navigation to learn from past experiences and improve navigation in real-time.

    “ML can be used in ship navigation to learn from past experiences and improve navigation in real-time [104] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [104]

  • ML algorithms can learn from previous ship routes and weather conditions to make more accurate predictions about future routes.

    “For example, ML algorithms can learn from previous ship routes and weather conditions to make more accurate predictions about future routes.”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [104]

    Section 2.1 discusses planning routes around weather and sea conditions, while Section 6.1 uses supervised learning to predict fuel consumption—not future routes. Searches for historical routes, weather inputs, and machine-learning route prediction found no passage stating the attributed claim. Section 8 says the study uses a fixed route and proposes developing a multiple-route model in future work. (LLM, conf=0.76)

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Cited by

Sorted by citations

Authors
Zhou, T., Hu, Q., Hu, Z., & Zhi, J.
Affiliations
  • Shanghai Maritime University (CN)
  • China Shipping Telecommunication Co., Ltd, Shanghai 200093, China
Corresponding author
Hu, Qinyou
ORCID
Venue
Journal of Advanced Transportation
Publisher
Wiley
Published
2022-04-23
Volume
2022
Pages
1-12
ISSN
  • 0197-6729 (ISSN-L)
  • 2042-3195
DOI
10.1155/2022/5333171
OpenAlex
W4224301514
Cited by
6
References
0
Citations by year
2025: 2 · 2024: 1 · 2023: 1 · 2022: 2
FWCI
0.45
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Advanced Transportation (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Shanghai Science and Technology Commission (18DZ1206300)
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Maritime Transport Emissions and Efficiency
  • Physical Sciences › Engineering › Ocean Engineering › Maritime Navigation and Safety
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Maritime Ports and Logistics
Keywords
ship speed control, dynamic speed control, maritime navigation safety, fuel consumption reduction, energy efficiency, particle swarm optimization, estimated time of arrival, ocean-going ships, multi-source data fusion

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