###### Abstract  
As the maritime industry enters the era of maritime autonomous surface ships, research into artificial intelligence based on maritime data is being actively conducted, and the advantages of profitability and the prevention of human error are being emphasized. However, although many studies have been conducted relating to oceanic operations by ships, few have addressed maneuvering in ports. Therefore, in an effort to resolve this issue, this study explores ship trajectories derived from automatic identification systems' data collected from ships arriving in and departing from the Busan New Port in South Korea. The collected data were analyzed by dividing them into port arrival and departure categories. To analyze ship trajectory patterns, the density-based spatial clustering of applications with noise (DBSCAN) algorithm, a machine learning clustering method, was employed. As a result, in the case of arrival, seven clusters, including the leg and turning section, were derived, and departure was classified into six clusters. The clusters were then divided into four phases and a pattern analysis was conducted for speed over ground, course over ground, and ship position. The results of this study could be used to develop new port maneuvering guidelines for ships and represent a significant contribution to the maneuvering practices of autonomous ships in port. ship trajectory; automatic identification systems data; DBSCAN algorithm; ship maneuvering guidelines; machine learning; artificial intelligence; maritime autonomous surface ships +
Footnote †: 2021: 12 January 2021  
which the ship actually passed was extracted on the basis of big AIS data. In addition, [PERSON] et al. 's study applied the density-based spatial clustering of applications with Noise (DBSCAN) algorithm, a machine learning clustering method based on AIS data, to classify the characteristics of a route and detect anomalies in vessels that exhibited unusual movement [11]. In a study by [PERSON] et al., similar to [PERSON] et al. 's study, the DBSCAN algorithm was applied to extract the turning section of a ship's trajectory, and a study was conducted to automatically design the route [12]. [PERSON] et al. also analyzed the pattern of a ship trajectory based on AIS data as well as anomaly detection and route prediction [13]. In addition, in the study of [PERSON] et al., AIS data have been elaborated together with safety criteria to ensure safe navigation, even in difficult visibility conditions [14]. All of the preceding studies were analyzed on the basis of data on the oceanic voyages of ships corresponding to pilot-stations-to-pilot-stations. However, relatively little research has been conducted on maneuvering technology in port settings compared to other MASS technologies relating to the patterns of routes. In the case of ship berthing technology, [PERSON] et al. employed machine learning based on berthing velocity data in their analysis and derived predictions from the data [15]. In addition, research to classify berthing velocity by types of pilots is also being conducted [16]. However, few studies have addressed technological development in the context of the patterns of port entry and departure by ships and how they approach piers. In unmanned ship development projects such as the Advanced Autonomous Waterborne Applications Initiative (AAWA), led by Rolls-Royce and Maritime Unmanned Navigation through Intelligence in Networks (MUNIN), promoted by the EU from 2012 to 2015, ship maneuvering in ports and berthing-related components of ship operations were excluded, and studies were conducted on ship automation [17; 18]. In addition, a few cases were applied to actual large ships [1]. The stages of a ship's entry to a port and the berthing process largely comprise the following steps. First, the ship enters the port. Second, the ship approaches its designated docking pier at a safe approaching speed. Third, when the ship is suitably close to the pier, it berths in a parallel position [19].