Ship Movement Analysis Based on Automatic Identification System (AIS) Data Using Convolutional Neural Network and Multiple Thread Processing

Abstract

Automatic Identification System (AIS) data is one of the most common and widely used datasets in the maritime industry. This dataset is a useful source of information regarding maritime traffic for both individuals and businesses. The reliability of this data and the long-distance transmission over the sea are the primary motivating factors behind its utilization. A wide variety of research projects are currently being carried out on this AIS data. Some of the applications that are being investigated include the detection of ship travel anomalies, the monitoring of ship security, the detection of ship collisions, and the pursuit of shipment trajectory tracking. A number of different methods of machine learning and deep learning are also being utilized in order to perform the analysis of the data. Nevertheless, the vast majority of the studies that have been done up to now have been carried out without any analysis of the consequences of concurrent processing of AIS data. This study conducted a ship movement analysis using AIS data. This study performed investigation and evaluation in order to see the impact of different numbers of threads processing during the analysis of AIS data. The number of threads used corresponds to the number of cores available on the CPU. Deep learning CNN model used for ship movement classification analysis. This study captured the speed, accuracy, and CPU utilization while performing AIS data analysis. The result shows a noticeable reduction of approximately 40% in processing time while the number of threads increased with no impact on accuracy. The study also found that CPU utilization increased with the increase in the number of threads used to do analysis.