Big data analytics of safety assessment for a port of entry: A case study in Keelung Harbor

In order to reduce accidents at the port of entry that may be due to weather factors, it is necessary to not only improve navigation skills but also use current advanced nautical technology to provide inbound vessels with the necessary information and establish a safety assessment model for inbound vessels to assist port authorities in effective control. Therefore, this study plans to use automatic identification system data collected around Keelung Harbor as a basis, coupled with meteorological data with time, and then to use a geographic information system and a decision tree algorithm in big data analytics. This enables analysis of the navigation characteristics of all types of vessels that have entered Keelung Harbor under different meteorological conditions, thereby establishing a safety assessment model for the port of entry in Keelung Harbor. The model can not only provide the vessel traffic service control personnel with real-time analysis on whether the behavior of an inbound vessel at a given time has abnormalities but also be used to establish an operation model for port traffic flow.

Application of Grey Relational Analysis to Evaluate Port Safety in Keelung Harbor

The objective of this paper is to devise a port safety analysis in a harbor. In this paper, the Grey Relational Analysis (GRA) is applied to analyze and discuss the marine casualties occurring in Keelung Harbor. GRA is mainly applied to explore the relativity between marine casualties and accident sites. The paper concludes that the coastal and in-port areas are the main accident sites. Fire or explosion and damage to machinery are the main marine casualties. The results will assist in improving port safety in Keelung Harbor.