Based on AIS data ship path optimization algorithm considering wind speed and ocean current environmental factors to reduce carbon emissions
風速と海流の環境要因を考慮したAISデータに基づく船舶航路最適化アルゴリズムによる炭素排出削減 (AI 翻訳)
Shiwei Zhou, Xinglong Liu, Feng Zhang, Xiangyan Zou
🤖 gxceed AI 要約
日本語
本論文は、AISデータと遺伝的アルゴリズム(GA)を用いて風速や海流を考慮した船舶の最適航路を探索し、燃料消費とCO2排出を削減する手法を提案する。実データによる評価では、従来のダイクストラ法などと比較して燃料消費を15%削減しつつ、同等の航行時間を達成した。適応的突然変異率とエリート保存戦略により収束性も確保している。
English
This paper proposes a genetic algorithm (GA)-based ship path optimization framework that uses AIS data to incorporate wind speed and ocean currents, aiming to minimize fuel consumption and carbon emissions. Evaluated on real AIS datasets, it achieves 15% fuel savings compared to Dijkstra and A* while maintaining travel times, with adaptive mutation and elite preservation for convergence.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本は世界有数の海運国であり、GHG削減目標達成のため本手法は有力な選択肢となる。IMOの規制強化を踏まえ、日本郵船や商船三井などの実船適用が期待される。
In the global GX context
This work directly addresses IMO's decarbonization targets for shipping, offering a data-driven path optimization that reduces fuel consumption and emissions. It is relevant to global shipping companies seeking cost-effective compliance with increasingly stringent environmental regulations.
👥 読者別の含意
🔬研究者:Novel integration of AIS data and GA for maritime path optimization under dynamic environmental factors, with a strong empirical validation showing 15% fuel reduction.
🏢実務担当者:Shipping companies can adopt this algorithm to reduce fuel costs and emissions, leveraging existing AIS data without major infrastructure investment.
🏛政策担当者:The results support the feasibility of data-driven operational measures for IMO carbon intensity reduction targets, providing a scalable approach for the global fleet.
📄 Abstract（原文）
Maritime transportation plays a pivotal role in global trade, yet it contributes significantly to carbon emissions due to inefficient routing. This paper proposes a genetic algorithm (GA)-based ship path optimization framework that leverages Automatic Identification System (AIS) data to incorporate environmental factors such as wind speed and ocean currents, aiming to minimize fuel consumption and carbon emissions. The methodology integrates a graph-based representation of maritime grids, where nodes account for dynamic environmental penalties, and GA evolves optimal paths by balancing distance, fuel efficiency, and emission reduction. Experimental evaluations on real AIS datasets demonstrate that the proposed algorithm achieves a 15% reduction in fuel consumption compared to traditional baselines like Dijkstra and A*algorithms, while maintaining comparable travel times. Key innovations include an adaptive mutation rate and elite preser-vation strategy, ensuring convergence within computational constraints. The results highlight the potential for sustainable shipping practices, with implications for reducing the maritime sector’s environmental footprint. Sensitivity analyses further validate the model’s robustness across varying wind scales, underscoring its practicality for real-world deployment. This work advances data-driven optimization in maritime logistics, promoting greener and more efficient global supply chains.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1117/12.3095796first seen 2026-06-29 06:18:31
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