%0 Generic
%A Xue, Jie
%A van Gelder, P. (Pieter)
%A Reniers, G.L.L. (Genserink)
%A Papadimitriou, E. (Eleonora)
%A Wu, C. (Chaozhong)
%D 2019
%T Dataset of maritime traffic safety influencing factors for autonomous ships' maneuvering decisions
%U https://data.4tu.nl/articles/dataset/Dataset_of_maritime_traffic_safety_influencing_factors_for_autonomous_ships_maneuvering_decisions/12712409/1
%R 10.4121/uuid:a9b0458d-a6d8-4758-97a6-a09b0efbd085
%K autonomous ships
%K decision-making
%K grey relational analysis
%K maritime safety
%K quantitative assessment
%X Maritime shipping is the lifeblood of the global economy, and ship maneuvering decisions are influenced by several factors. Autonomous ships have outstanding advantages in improving operational efficiency, safety management, decision-making efficiency, and energy consumption management of ships, so research for autonomous ships has become an inevitable tendency for future ship development, and gained the interest of many researchers in both academia and private sectors. Based on experimental data of experienced seafarers (captains/chief officers) and using a simulation platform under the scenario of the Shanghai Waigaoqiao wharf (the ship was downstream of the berthing into the port), we collected the data of 33 main influencing factors (contains the categories of natural environment, ship motion, force parameters, draft, and position) for autonomous ships’ maneuvering decisions. We use the OS1 ship as our experimental ship (displacement: 33,089.0 tons; length: 182.9 meters; breadth: 22.6 meters). The database contains: (i) the name and the category of influencing factors, (ii) the standardized value for each influencing factor, and (iii) the grey relational coefficient and grey grade of each influencing factor.
%I 4TU.Centre for Research Data