[论文解读] Artificial Intelligence-based Smart Port Logistics Metaverse for Enhancing Productivity, Environment, and Safety in Port Logistics: A Case Study of Busan Port

Sunghyun Sim, Dohee Kim+2 more|arXiv (Cornell University)|Aug 29, 2024

Marine and Coastal Research被引用 5

一句话总结

该论文提出一种基于人工智能的港口物流元宇宙框架（PLMF），以提升生产力、环境可持续性和安全性，并以釜山港数据为例，显示准点性提升和潜在收入增长。

ABSTRACT

The increase in global trade, the impact of COVID-19, and the tightening of environmental and safety regulations have brought significant changes to the maritime transportation market. To address these challenges, the port logistics sector is rapidly adopting advanced technologies such as big data, Internet of Things, and AI. However, despite these efforts, solving several issues related to productivity, environment, and safety in the port logistics sector requires collaboration among various stakeholders. In this study, we introduce an AI-based port logistics metaverse framework (PLMF) that facilitates communication, data sharing, and decision-making among diverse stakeholders in port logistics. The developed PLMF includes 11 AI-based metaverse content modules related to productivity, environment, and safety, enabling the monitoring, simulation, and decision making of real port logistics processes. Examples of these modules include the prediction of expected time of arrival, dynamic port operation planning, monitoring and prediction of ship fuel consumption and port equipment emissions, and detection and monitoring of hazardous ship routes and accidents between workers and port equipment. We conducted a case study using historical data from Busan Port to analyze the effectiveness of the PLMF. By predicting the expected arrival time of ships within the PLMF and optimizing port operations accordingly, we observed that the framework could generate additional direct revenue of approximately 7.3 million dollars annually, along with a 79% improvement in ship punctuality, resulting in certain environmental benefits for the port. These findings indicate that PLMF not only provides a platform for various stakeholders in port logistics to participate and collaborate but also significantly enhances the accuracy and sustainability of decision-making in port logistics through AI-based simulations.

研究动机与目标

• 在全球贸易增长、COVID-19影响和更严格法规背景下，推动港口物流的生产力、环境可持续性与安全性提升。
• 提出一个协作、AI驱动的元宇宙框架（PLMF），以实现数据共享、沟通与多方共同决策。
• 通过釜山港案例研究，利用历史数据评估PLMF的性能与经济影响，验证其能力。

提出的方法

• 引入基于AI的港口物流元宇宙框架（PLMF），包含11个AI驱动的元宇宙内容模块。
• 实现对真实港口物流流程的监控、仿真与决策。
• 提供ETA预测、动态港口运营规划、船舶燃料消耗与排放监测/预测，以及工人与设备之间的危险路线/事故检测模块。
• 利用釜山港历史数据评估PLMF在预测到达时间和优化运营方面的有效性。

实验结果

研究问题

• RQ1 AI驱动的元宇宙框架如何提升港口物流的可预测性与运营效率？
• RQ2 在港口运营中使用PLMF会带来哪些环境与安全方面的收益？
• RQ3 在一个大型港口部署PLMF的经济影响（如潜在收入、成本节约）是什么？
• RQ4 PLMF在多方港口利益相关者之间的协作与决策制定上能达到何种程度的提升？

主要发现

• PLMF预测了船舶到港时间，并实现了港口运营的优化。
• 该框架每年可能产生约$7.3百万的额外直接收入。
• 在使用PLMF时，船舶准点性提升约79%。
• 通过对燃料消耗与排放的监测与预测，该方法为更可持续的港口运营提供环境收益。
• PLMF通过基于AI的仿真增强利益相关者参与、协作以及决策的准确性和可持续性。