A Data-Driven Approach for Safe Ship Operation of Berthing Phase Using Artificial Intelligence Technique

Abstract

Herein, a study was conducted to derive quantitative solutions by collecting ship operation-related data and constructing algorithms using artificial intelligence (AI) techniques, such as machine learning and deep learning, so that ships can safely operate in the ports. The Fourth Industrial Revolution expanded and increased research on unmanned autonomous ships in the fields of ships, maritime, and ports. The biggest advantage of the emergence of maritime autonomous surface ships (MASSs) is the possibility to operate safely. This can be accomplished by superseding the existing crew’s role with AI to reduce human errors and hence prevent accidents. The field of AI-based navigation technology required to manufacture MASSs needs continuous accumulation of maritime data and professional knowledge of navigation officers. However, most of the past studies have focused on navigation in the ocean-going and only a few studies have been conducted related to safe berthing and maneuvering in ports. Therefore, in this study, four algorithms for safe ship operation in port were proposed by applying AI techniques based on a ship’s berthing and trajectory data. The ship’s berthing data were collected from March 2017 to August 2021 for a tanker jetty in the Republic of Korea, while the ship trajectory data include the arrivals and departures of ships at the target pier of Busan New Port from January to April 2020. Ships with a gross tonnage of ≥10k are the target group. A ship’s berthing energy generated is calculated using the kinematic method, wherein the most influential factor is the berthing velocity. If a ship does not berth at an appropriate velocity, accidents could happen such as damage to port facilities and the hull. At this time, since the pilot’s voyage ends at the point when the ship completes berthing, the pilot and berthing velocity have a close relationship. Therefore, the maneuvering pattern for the pilot’s berthing velocity was analyzed with the k-means algorithm, an unsupervised machine learning method. Forty-seven pilots working at the target pier were classified into low risk, moderate risk, and high risk according to the analysis results. When a ship is berthing, accidents can be prevented if the danger range of the berthing velocity can be predicted. The operating range of the berthing velocity of the target pier for which data was collected is categorized into “Safety,” “Warning,” and “Critical,” and nine classification algorithms corresponding to supervised machine learning were used to predict this. The input parameters of the algorithm were factors affecting the berthing velocity, including the pilot’s maneuvering pattern. After evaluating the algorithms using the confusion matrix, gradient boosting, support vector machine, random forest, and bagging classifier were classified as four models with high performance. Water depth restrictions, marine structures, and the presence or absence of a designated route, such as a non-navigable area, affected the route planning and restricted the navigation speed of a ship maneuvering in the port, which is the preberthing stage. This is different from choosing the shortest path according to fuel consumption and time, like in the ocean-going. Therefore, the DBSCAN algorithm, an unsupervised machine learning, was used to analyze the maneuvering patterns of arriving and departing ships. Ship trajectory data based on the automatic identification system was divided into phases through DBSCAN, and changes in speed over the ground and course over the ground were analyzed in time series. The need for quantitative maneuvering guidelines was raised after a crane collision accident of a large container ship occurred in 2020 at Busan New Port. As per the analysis of ship maneuvering patterns in port, quantile regression using general additive models and deep learning algorithms to suggest guidelines for ship’s position changes, including operating guidelines for speed over the ground and course over the ground, was used. Quantile regression is suitable for analyzing ship trajectory data with uncertainty and variability. As a result of applying the algorithm of quantile regression, the performance of the quantile regression neural network was found to be higher than that of the general additive models, making it possible to suggest quantitative ship maneuvering guidelines in port. The practical aspects of this study can contribute to the safe operation of ships’ berthing phase. In other words, the algorithm derived from this study can make quantitative decisions based on data rather than empirical factors regarding the safety of ship operations. Moreover, this paper presented a basic application plan for the development of MASSs in the long term through the integration of AI techniques and ship data.