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 into the consequences of concurrent processing of AIS data. The purpose of this study is to investigate and evaluate the impact that different numbers of thread processing have on the accuracy as well as the processing time. For the analysis of ship movement classification, the deep learning CNN model will be utilized. This study will check the speed, accuracy, and CPU utilization while performing AIS data analysis.