A vessel route tracking scenario was implemented based on large-scale AIS/GPS operation history to meet the requirements of Pusan National University's Sustainable Carbon-Neutral Global Supply Chain Research Center (SCSC). By adjusting logistics variables such as vessel speed and port waiting time, the system simulates changes in carbon emissions and cost, creating a data-driven environment for multidimensional supply chain analysis.
The system provides real-time vessel route search and monitoring based on large-scale AIS/GPS data. It connects with the research center's six core AI prediction models to visualize virtual simulation results and logistics data intuitively within a metaverse environment.
Historical navigation data is used to display key vessel information, including IMO number, position, and speed. Based on actual operation history, users can track vessel status, and intuitively review the movement information of the selected vessel.
The vessel movement path is tracked based on stored navigation information. The screen view is automatically adjusted to follow the route flow, helping users understand vessel movement and operational patterns in a 3D metaverse environment.
The system guides users through the progress of data retrieval and navigation visualization. By showing the flow of large-scale operation data processing, users can clearly understand data processing stages and system status during analysis.
Vessel location information is displayed while route data is being retrieved or visualized. Current latitude and longitude information helps users understand the spatial relationship between vessel routes and location data.
The system is configured to intuitively visualize six data query scenarios connected with the research center's major AI prediction models based on vessel navigation data.
Visualizes vessel routes and estimated time of arrival (ETA) based on real-time operation data (AIS/GPS).
Connects weather and vessel data to display expected fuel consumption by real-time status and route segment in 3D.
Uses LLM technology to identify marine accident risks along routes and support safety management decisions.
Visualizes optimal speed and route data for each vessel to maximize fuel efficiency and support on-time port arrival.
Predicts real-time movement paths and optimal routes for port transport equipment to improve cargo handling efficiency.
Visualizes vessel emissions and port air pollution data in real time, enabling efficient environmental monitoring and trend analysis.
By combining real operation data, AI prediction models, metaverse visualization, and web streaming access, the system provides a supply chain analysis foundation that can be used for scenario analysis and decision-making at the research center.
This portfolio presents a vessel route tracking scenario built with large-scale real operation history (AIS/GPS) to meet the requirements of Pusan National University's Sustainable Carbon-Neutral Global Supply Chain Research Center (SCSC). By combining AI prediction models with a Pixel Streaming-based 3D environment, the metaverse simulation platform was enhanced for supply chain analysis and decision-making.
To connect large-scale operation history data (AIS/GPS) generated during real maritime movement with a metaverse virtual environment, the route data cleansing pipeline and backend API server integration structure were advanced.
This enabled an analysis environment for tracking selected vessel routes and simulating carbon emissions and cost changes.
A VARLOS®-based PoC portfolio that advances supply chain analysis by connecting real-time operation data and AI prediction models to a metaverse environment.
This project cleanses large-scale AIS/GPS data and connects it with the research center's six AI prediction models to intuitively visualize route, fuel, accident risk, port equipment, and air pollution data. Pixel Streaming was also applied so users can access a high-quality 3D environment through a web browser, establishing an analysis infrastructure that supports scenario analysis and decision-making without spatial constraints.
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