On the data-driven investigation of factors affecting the need for icebreaker assistance in ice-covered waters

• Published in: Cold Regions Science and Technology
• Main Authors: Liu, Cong, Kulkarni, Ketki, Suominen, Mikko, Kujala, Pentti, Musharraf, Mashrura
• Other Authors: Department of Mechanical Engineering, Marine and Arctic Technology, Aalto-yliopisto, Aalto University
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2024
• Subjects: Baltic Sea; Ice-covered waters; Icebreaker assistance; Merchant vessels; Statistical analysis; Icebreaker; Sea ice; ice covered waters
• Online Access: https://aaltodoc.aalto.fi/handle/123456789/127902; https://doi.org/10.1016/j.coldregions.2024.104173

Publisher Copyright: © 2024 The Author(s) Merchant vessels often require icebreaker (IB) assistance to create safe pathways and improve efficiency when navigating in the Baltic Sea. Since IB resources are limited, an accurate estimation on the need for IB assistance is important. Whether IB assistance is needed depends on multiple factors. While practical experience from captains is naturally a source of valuable information for the decision on the need for IB assistance, systematic analysis of the reasoning is limited. The primary aim of this paper is to holistically investigate the influencing factors and their effect on estimating the need for IB assistance through data-driven techniques. Based on a comprehensive list of potential factors, different of data such as traffic history, environmental conditions, and ship specifications are gathered to present complex navigational scenarios. Each scenario is labeled by different navigation modes (independent navigation or IB assistance), laying the foundation for influencing factor identification and effect quantification. Logistic regression is applied to evaluate the effect of the factors on the need for IB assistance. The results show that the impact of the factors is diverse, and ridged ice concentration has the most significant impact. The effectiveness of identified factors is measured by comparing it to that of the factors that have been implemented by the existing studies (e.g., the combination of ice concentration, thickness, and ship ice class, or only ship speed). By considering the factors in this study, the classification performance can be improved by at least 5.6%. The findings in this paper can provide insights for predicting IB workloads and optimizing IB resources and have the potential to support the development of an intelligent decision-support system for winter navigation. Peer reviewed