This paper realizes the simultaneous optimization of a vessel’s course and speed for a whole voyage within the estimated time of arrival (ETA), which can ensure the voyage is safe and energy-saving through proper planning of the route and speed. Firstly, a dynamic sea area model with meteorological and oceanographic data sets is established to delineate the navigable and prohibited areas; secondly, some data are extracted from the records of previous voyages, to train two artificial neural network models to predict fuel consumption rate and revolutions per minute (RPM), which are the keys to route optimization. After that, speed configuration is introduced to the optimization process, and a simultaneous optimization model for the ship’s course and speed is proposed. Then, based on a customized version of the A* algorithm, the optimization is solved in simulation. Two simulations of a ship crossing the North Pacific show that the proposed methods can make navigation decisions in advance that ensure the voyage’s safety, and compared with a naive route, the optimized navigation program can reduce fuel consumption while retaining an approximately constant time to destination and adapting to variations in oceanic conditions.
Recognition and understanding of ship motion patterns have excellent application value for ship navigation and maritime supervision, i.e., route planning and maritime risk assessment. This paper proposes a semantic recognition method for ship motion patterns entering and leavingport based on a probabilistic topic model. The method enables the discovery of ship motion patterns from a large amount of trajectory data in an unsupervised manner and makes the results more interpretable. The method includes three modules: trajectory preprocessing, semantic process, and knowledge discovery. Firstly, based on the activity types and characteristics of ships in the harbor waters, we propose a multi-criteria ship motion state recognition and voyage division algorithm (McSMSRVD), and ship trajectory is divided into three sub-trajectories: hoteling, maneuvering, and normal-speed sailing. Secondly, considering the influence of port traffic rules on ship motion, the semantic transformation and enrichment of port traffic rules and ship location, course, and speed are combined to construct the trajectory text document. Ship motion patterns hidden in the trajectory document set are recognized using the Latent Dirichlet allocation (LDA) topic model. Meanwhile, topic coherence and topic correlation metrics are introduced to optimize the number of topics. Thirdly, a visualization platform based on ArcGIS and Electronic Navigational Charts (ENCs) is designed to analyze the knowledge of ship motion patterns. Finally, the Tianjin port in northern China is used as the experimental object, and the results show that the method is able to identify 17 representative inbound and outbound motion patterns from AIS data and discover the ship motion details in each pattern.
Maritime Autonomous Surface Ships (MASS) have been an important direction for the development of intelligent shipping. However, most current international research on MASS focuses on navigation assistance technologies such as perception and decision-making, ignoring MASS’s traffic organization and management. The traffic organization service (TOS) under the e-Navigation strategy also has not researched MASS. In this paper, we propose the notion of on-demand service to MASS with different degrees of autonomy (DoA) and develop a new maritime service (MS) applicable to the MASS with various (DoA) following the e-Navigation technical architecture. We first analyze MASS requirements with different degrees of autonomy in traffic organization to define the service information. Then, based on the traditional TOS, we developed the MASS traffic organization service (MTOS), consisting of an operational architecture, five subsystems, and four services. In particular, we proposed a phased service trigger mechanism to solve the problem of publishing untimely and redundant service information. Tianjin port and Huanghua port were selected as cases study for simulation experiments; the study finding revealed that MTOS could provide standardized, accurate, and efficient traffic organization service for MASS with different degrees of autonomy on demand. The contribution can be applied in the port operation to improve traffic safety.
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