Generation of Ship’s Passage Plan Using Data-Driven Shortest Path Algorithms - Hyeong-Tak Lee; Hey-Min Choi; Jeong-Seok Lee; Hyun Yang; Ik-Soon Cho (IEEE Access, 2022)
In this study, an approach for generating the shortest-distance passage plan for a ship is proposed considering the navigable area obtained based on automatic identification system data. The navigable area is designated based on the information from the electronic navigational chart and by considering the ship traffic density using Jenks natural breaks classification. The shortest-path algorithms used in our experiment are the Dijkstra, $\text{A}^{*} $ , and improved $\text{A}^{*} $ . Then, the Douglas–Peucker algorithm is applied to generate an optimal result by supplementing the passage plan generated by the best-performing shortest-path algorithm. The experiment was divided into two cases depending on whether or not the passage plan covered the target area of this study, namely, the vessel traffic service system control area of Busan Port between Busan New Port and Ulsan Port. The improved $\text{A}^{*} $ performed better than Dijkstra and $\text{A}^{*} $ in these two cases. Subsequently, the distance of the final passage plan was 98.649 km in the case of passing through the control area and 105.365 km in the case of bypassing, which were less than the distances in the passage plan of the actual ship by approximately 13.18% and 7.27%, respectively. The experimental results of the proposed approach show the possibility of automatically establishing the shortest-distance passage plan for a ship considering the navigable area. In addition, this study suggests an effective approach for coastal navigation, which is more complex than ocean navigation. Further, this study can serve as a basis for generating passage plans for maritime autonomous surface ships.