The Development of Regional Vessel Traffic Congestion Forecasts Using Hybrid Data from an Automatic Identification System and a Port Management Information System

• Published in: Journal of marine science and engineering Vol. 10; no. 12; p. 1956
• Main Authors: Son, Joonbae, Kim, Dong-Ham, Yun, Sang-Woong, Kim, Hye-Jin, Kim, Sewon
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-12-2022
• Subjects: Accuracy; Algorithms; autonomous vessel; Data processing; Deep learning; Forecasts and trends; Identification; Information management; Information systems; Management information systems; Oil consumption; port arrival and departure; port MIS (port management information system); Ports; Regional development; Registration; ship AIS (automatic identification system); Ships; Traffic congestion; vessel congestion forecast; vessel traffic congestion; Vessels; South Korea
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse10121956

The present study proposes a new method that forecasts congestion in the area near a port by combining the automatic identification systems of ships and port management information data. The proposed method achieves 85% accuracy for one-day-long ship congestion forecasts. This accuracy level is high enough to act as a reference value for both manned and unmanned operation situations for autonomous vessels in port areas. The proposed forecast algorithm achieves 95% accuracy when used for a one-hour ship congestion forecast. However, the accuracy of the algorithm is degraded to almost half when the automatic identification system or the port management system is used independently.