Port Performance Indicators Construction based on the AIS-generated Trajectory Segmentation and Classification

Abstract

With coverage of worldwide shipping routes and straightforward accessibility, vessels' navigational data from AIS have emerged as a potential leading source of knowledge for maritime stakeholders. However, transforming the raw AIS messages into meaningful indicators requires comprehensive work that involves enrichment with multiple relevant data sources. In this study, a fain-grained technique to process AIS tracks for the purpose of constructing port performance indicators is proposed. The technique involves trajectory segmentation to separate the stopping and underway segments of AIS-generated trajectories and trajectory classification to identify moored and at-anchor vessels from the stopping segments. With the fusion of relevant information, the method estimates the port boundary and identifies the associated berthing and anchorage locations to estimate turnaround time and construct port connectivity indicators. AIS datasets recorded over eight months covering the waters of Indonesia, Malaysia, and Singapore, known as one of the world's busiest shipping routes, are selected to evaluate the proposed method. The trajectory segmentation demonstrated 98 - 99% accuracy, while the classification achieved 95 - 97%. The estimation of the vessel turnaround time closely matched the UNCTAD data with an error rate of 2.9%. These results prove the proposed approach's practicality in contributing to the real-world scenario.