Lee Hyeong-Tak, Yang Hyun, Cho Ik-Soon
Ocean Science and Technology School, Korea Maritime and Ocean University, Busan 49112, Korea.
Korea Ocean Satellite Center, Korea Institute of Ocean Science & Technology, Busan 49112, Korea.
Sensors (Basel). 2021 Dec 10;21(24):8254. doi: 10.3390/s21248254.
Marine accidents in ports can cause loss of human life and property and have negative material and environmental impacts. In South Korea, due to a pier collision accident of a large container ship in Busan New Port of South Korea, the need for safe ship operation guidelines in ports emerged. Therefore, to support quantitative safe ship operation guidelines, ship trajectory data based on automatic information system information have been used. However, because this trajectory information is variable and uncertain due to various situations arising during a ship's navigation, there is a limit to deriving results through traditional regression analysis. Considering the characteristics of these data, we analyzed ship trajectories through quantile regression using two models based on generalized additive models and neural networks corresponding to deep learning. Among the automatic information system information, the speed over ground, course over ground, and ship's position were analyzed, and the model was evaluated based on quantile loss. Based on this study, it is possible to suggest safe operation guidelines for the position, speed, and course of the ship. In addition, the results of this work can be further developed as a manual for the in-port-autonomous operation of ships in the future.
港口的海上事故可能导致人员伤亡和财产损失，并对物质和环境产生负面影响。在韩国，由于韩国釜山新港一艘大型集装箱船的码头碰撞事故，出现了制定港口船舶安全操作指南的需求。因此，为了支持定量的船舶安全操作指南，基于自动识别系统信息的船舶轨迹数据被加以利用。然而，由于船舶航行过程中出现的各种情况，这些轨迹信息是可变且不确定的，通过传统回归分析得出结果存在局限性。考虑到这些数据的特点，我们使用基于广义相加模型和对应深度学习的神经网络的两种模型，通过分位数回归分析船舶轨迹。在自动识别系统信息中，分析了对地航速、对地航向和船舶位置，并基于分位数损失对模型进行评估。基于本研究，可以为船舶的位置、速度和航向提出安全操作指南。此外，这项工作的结果未来可进一步发展成为船舶港口自主操作手册。
