Artificial Intelligence-based Smart Port Logistics Metaverse for Enhancing Productivity, Environment, and Safety in Port Logistics: A Case Study of Busan Port
{"title":"Artificial Intelligence-based Smart Port Logistics Metaverse for Enhancing Productivity, Environment, and Safety in Port Logistics: A Case Study of Busan Port","authors":"Sunghyun Sim, Dohee Kim, Kikun Park, Hyerim Bae","doi":"arxiv-2409.10519","DOIUrl":null,"url":null,"abstract":"The increase in global trade, the impact of COVID-19, and the tightening of\nenvironmental and safety regulations have brought significant changes to the\nmaritime transportation market. To address these challenges, the port logistics\nsector is rapidly adopting advanced technologies such as big data, Internet of\nThings, and AI. However, despite these efforts, solving several issues related\nto productivity, environment, and safety in the port logistics sector requires\ncollaboration among various stakeholders. In this study, we introduce an\nAI-based port logistics metaverse framework (PLMF) that facilitates\ncommunication, data sharing, and decision-making among diverse stakeholders in\nport logistics. The developed PLMF includes 11 AI-based metaverse content\nmodules related to productivity, environment, and safety, enabling the\nmonitoring, simulation, and decision making of real port logistics processes.\nExamples of these modules include the prediction of expected time of arrival,\ndynamic port operation planning, monitoring and prediction of ship fuel\nconsumption and port equipment emissions, and detection and monitoring of\nhazardous ship routes and accidents between workers and port equipment. We\nconducted a case study using historical data from Busan Port to analyze the\neffectiveness of the PLMF. By predicting the expected arrival time of ships\nwithin the PLMF and optimizing port operations accordingly, we observed that\nthe framework could generate additional direct revenue of approximately 7.3\nmillion dollars annually, along with a 79% improvement in ship punctuality,\nresulting in certain environmental benefits for the port. These findings\nindicate that PLMF not only provides a platform for various stakeholders in\nport logistics to participate and collaborate but also significantly enhances\nthe accuracy and sustainability of decision-making in port logistics through\nAI-based simulations.","PeriodicalId":501310,"journal":{"name":"arXiv - CS - Other Computer Science","volume":"11 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Other Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.10519","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.