The uploaded files consists of the Project Report on a MarTREC project - Noval Big Data and Artificial Intelligence Analytics Methods for Tracking and Monitoribg Maritime Traffics and an Excel Dataset. 1 Project Description Maritime transportation represents a substantial portion of international trade. Sustainable development of marine transportation requires systematic modeling and surveillance for maritime situational awareness. Big data and artificial intelligence (AI) are crucial components of data-driven decision-making in most industries. AI is gradually transforming the traditional operational process of the maritime industry. Consequently, the amount of research on the application of big data and AI has increased significantly since 2012. For example, a common method in the evaluation of ship speed involves computing the total resistance of a ship using theoretical analysis; however, using theoretical equations cannot be applied for most ships under various operating conditions. So, machine learning approaches have been proposed to predict ship speed over the ground and in monitoring and tracking vessels. The past decade has seen an explosion of machine learning research and applications; especially, deep learning methods have enabled key advances in many application domains, such as computer vision, speech processing, and in maritime vessel trajectories. However, the performance of many machine learning methods is very sensitive to a plethora of design decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural architectures, training procedures, regularization methods, and hyper-parameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset. The purpose of the field of Automated Machine Learning (AutoML) is to make these decisions in a data-driven, objective, and automated way. That is, the user simply provides data, and the AutoML system automatically determines the approach that performs best for this particular application. Therefore, the purpose of this project is to develop an AutoML model for monitoring and tracking maritime traffic, a state-of-the-art machine learning approach that is accessible to users of maritime historical and real-time databases interested in applying machine learning but do not have the resources to learn about the methods and technologies behind it in detail. This can be seen as a democratization of machine learning where the state-of-the-art machine learning is at every maritime database user’s fingertip. Since the configuration of the global maritime network is organized along a circum-equatorial corridor linking North America, Europe, and Pacific Asia through the Suez Canal, the Strait of Malacca, and the Panama Canal (that is, linking all the choke points). The Machine Learning and hence the AutoML models are modified to capture maritime traffic in all global waters, including inland vessel traffics equip with the AIS transponders. The project has engaged three CDS&E Ph.D. students with excellent academic records in a yearlong research activity in the use of the Automatic Identification System (AIS) datasets to develop maritime traffic tracking and monitoring models. Consequently, there will be three Ph.D. dissertations resulting from the outcomes of the project. The program Graduate Assistants will acquire broad data science and big data analytics skills geared towards effective applications of the CDS&E methods in novel maritime traffic modeling and analysis.