Data and code underlying chapters 3-5 of the PhD thesis: Human-MASS Interaction in Decision-Making for Safety and Efficiency in Mixed Waterborne Transport Systems

This dataset supports the doctoral research of Rongxin Song, M.Sc., at Delft University of Technology (2021–2025), focusing on enhancing maritime situational awareness, collision avoidance, and human-MASS (Maritime Autonomous Surface Ships) interaction. It includes AIS (Automatic Identification System) data from the Rotterdam area (spanning 51.897°–51.913° N, 4.411°–4.425° E and 51.833°–52.167° N, 3.167°–4° E) collected between 1 and 15 October 2023. The dataset also contains Python scripts for DWA-based path planning and trajectory prediction, MATLAB scripts for modelling and visualizing trust dynamics, and supporting files in .csv, .png, .py, and .m formats. The research integrates ontology-driven knowledge maps, machine learning for preference-aware ship navigation, and trust behaviour analysis to address challenges in mixed waterborne transport system. This dataset provides a structured resource for replicating experiments in dynamic maritime environments, with a README file included for usage guidance.

本数据集支持荷兰代尔夫特理工大学（Delft University of Technology）理学硕士宋荣鑫（Rongxin Song）2021—2025年的博士研究，研究聚焦于提升海事态势感知、船舶避碰以及人机与海事自主水面舰艇（Maritime Autonomous Surface Ships，MASS）交互能力。数据集包含2023年10月1日至15日采集的鹿特丹海域自动识别系统（Automatic Identification System，AIS）数据，覆盖两处海域范围：其一为北纬51.897°—51.913°、东经4.411°—4.425°，其二为北纬51.833°—52.167°、东经3.167°—4°。此外，数据集还包含基于动态窗口法（Dynamic Window Approach，DWA）的路径规划与轨迹预测Python脚本、用于信任动力学建模与可视化的MATLAB脚本，以及.csv、.png、.py、.m格式的配套文件。该研究融合了本体驱动的知识图谱、面向偏好感知船舶航行的机器学习方法以及信任行为分析，以解决混合水上交通系统中的各类挑战。本数据集为动态海事环境下的实验复现提供了结构化资源，并附带README文件以提供使用指引。