LAUSR

The advent of the COVID-19 pandemic disrupted global commercial activities and the tourism industry heavily. Impacts on maritime transportation were huge, as seaborne trade represents over 80% of global merchandise trade. Investigating how COVID-19 has affected ship behaviours is significant for economic condition evaluation, port management. This paper develops an analysis method to mine knowledge from raw Automation Identification System (AIS) data. First, berths are identified by improved density-based spatial clustering of applications with noise by Pythagoras distance (PD-DBSCAN). Data features, such as ship deadweight, arrival time, dwelling time, ship types, etc., can then be extracted using information matching and statistical analysis. Next, the dynamic time warping method is employed to analyse abnormal ship behaviour patterns and quantify the impacts of COVID-19. After that, a significance test is employed to determine an impact threshold through year-on-year analysis on ship flow, daily throughout and berthing time of quays. Finally, statistical analysis is used for the short-term impact analysis. This research examines a case study based on four-year AIS data in the Oslo port area. The results show that the proposed method can identify abnormal patterns caused by COVID-19 and estimate its impacts. Passenger ships are influenced heavily compared with cargo ships. The variation of passenger ships’ flow is over 90% during 2020, larger than the average variation before 2020. The discovered knowledge could be used for future decision-making and preplanning in the next health crisis.