Charting the Future of Winter Navigation: Keeping Humans at the Center of Digital Change
November 2025
Winter navigation demands difficult, high-stakes decisions, especially judging when a vessel truly needs icebreaker assistance. As experienced navigators become fewer and conditions grow more unpredictable, the urgency increases to develop tools that support human judgment rather than replace it.
Winter navigation in ice-covered seas involves complex, high-stakes decisions. One of the most difficult is judging when a vessel truly needs icebreaker assistance. If the call comes too late, a ship may become stuck in ice. If it comes too early, the response may disrupt operations elsewhere. These choices are further complicated by growing operational pressure: icebreakers are limited and often shared across borders, winters are increasingly unpredictable, and new regulations, combined with a shrinking pool of skilled ice navigators, make every decision count. Often, more ships require help than can be served, forcing trade-offs between safety, delays, and fuel use.
Traditionally, these decisions rely on experienced captains drawing on years of seafaring and tacit knowledge. They weigh weather, ice conditions, and vessel capability using judgment shaped through practice, not only through formal rules. While this process remains largely human-led, the decline in experienced ice navigators has created urgency around preserving and transferring that expertise. At the same time, advances in big data and artificial intelligence are driving efforts to support winter navigation while raising important questions about how such systems can complement, rather than replace, human judgment, especially in complex, changing ice conditions.
The Promise and Limits of Data-Driven Decision Support
Modern data analytics and machine learning are opening new possibilities for anticipating ice conditions and ship performance. For example, researchers have used historical data from ship movements, ice charts, and weather reports to train algorithms that detect when icebreaker assistance is likely needed. Liu et al. (2025, 2022) show examples of successfully identifying past escort events by analyzing operation patterns based on AIS (Automatic Identification System) data. This shows how an algorithm can learn the conditions that typically necessitate assistance, potentially alerting authorities in advance and improving icebreaker resource coordination.
Data analysis also helps clarify which conditions matter most. In one study, models examined factors such as ice type, weather, traffic, and vessel specifications (Liu et al., 2024). The findings confirmed what many mariners already know: ice conditions, especially ridged ice, are among the most decisive. Including detailed ice features and vessel characteristics significantly improved predictive accuracy. Insights like these can help prioritize icebreaker deployment and reduce unnecessary delays.
Still, data-driven models have limits. Severe ice events are rare, so there may be too little data to reliably model high-risk scenarios. Low-quality input can also lead to misleading results. Moreover, purely automated “black box” recommendations can erode trust, mariners may hesitate to follow an algorithm’s advice if they do not understand the reasoning. In routine situations, predictive tools can be helpful. But when conditions shift unexpectedly, it is human judgment that ensures decisions remain safe and context aware.
The Role of Experience and Human Judgment
Experienced icebreaker captains have an intuitive grasp of winter navigation that no algorithm can fully replicate. They notice subtle cues, the sound of ice against the hull, a change in ship vibration, a sudden wind shift, and draw on decades of hard-earned lessons. To better understand this experience-driven decision process, a recent study interviewed ice navigation officers about how they judge the need for assistance (Musharraf et al., 2025). The researchers identified the main features these experts consider and found a broadly consistent set of factors guiding their decisions.
For these mariners, ice conditions are often the most decisive, especially the presence of ridged ice that can quickly immobilize a vessel. Ship-specific characteristics such as engine power and ice class also play a critical role in determining whether a ship can proceed safely. Weather remains an important influence, as sudden changes in wind or current can rapidly escalate the risk. Crews also take into account operational factors like fuel levels, loading conditions, scheduling pressures, and official ice navigation rules.
What sets human decision-making apart is the ability to integrate diverse inputs in real time and respond to evolving conditions. An experienced captain might identify a non-obvious route to avoid pressure zones or coordinate a convoy to improve efficiency—tactics that are difficult for automated systems to anticipate in unfamiliar or fast-changing situations. This flexibility and context awareness make human judgment indispensable in winter navigation.
At the same time, human judgment is not without its challenges. Decisions may vary depending on a captain’s personal experience or risk tolerance. Intuition can sometimes lean too cautious or too confident. As a result, while experiential knowledge is vital, it benefits from the structure and consistency that data-driven support can provide, helping ensure that no critical factor is missed, even under pressure.
Bridging Data and Experience: The Way Forward
Data-driven models and human expertise often converge on the same key factors, both recognize ridged ice, for instance, as a major risk indicator. This alignment suggests that well-designed models are beginning to capture cues that experienced mariners have long relied on. Yet each approach brings distinct strengths. While a captain may emphasize context-specific factors like vessel responsiveness or recent ice behavior, algorithms can scan thousands of past voyages to uncover patterns no individual could detect. Each help compensate for the other’s blind spots.
The real potential lies in combining these perspectives. Integrating experience-based insights into data models can make predictions more reliable and context-aware. This reinforces the need for human-centered decision support systems, tools designed not to replace but to assist mariners in making informed judgments under pressure.
Such systems should follow a few key principles. They must keep a human in the loop, allowing operators to intervene or override when conditions fall outside the system’s scope. They should be transparent, offering clear reasoning behind recommendations and involving seasoned ice navigators in their design. And they must learn continuously, adapting based on operator feedback and evolving ice conditions.
Looking Ahead: Aligning Technology with Expertise
The goal is not full autonomy, but a balanced system where data-driven tools support, rather than substitute, human decision-making. Achieving this balance will require more than just technical innovation. It calls for close collaboration between researchers, technology developers, mariners, regulators, and ice-navigation authorities. Involving stakeholders throughout the design process ensures that new tools reflect operational realities, earn trust, and ultimately strengthen the human role. As the maritime sector moves toward increasingly intelligent systems, keeping that collaboration at the center will be key to both safety and success.
Mahsrura Musharraf
Assistant Professor
Aalto University
References:
Liu, C., Kulkarni, K., Suominen, M., Kujala, P., & Musharraf, M. (2024). On the data-driven investigation of factors affecting the need for icebreaker assistance in ice-covered waters. Cold Regions Science and Technology, 221, 104173.
Liu, C., Musharraf, M., Li, F., & Kujala, P. (2022). A data mining method for automatic identification and analysis of icebreaker assistance operation in ice-covered waters. Ocean Engineering, 266, 112914.
Liu, C., Suominen, M., & Musharraf, M. (2025). An ensemble machine learning model for predicting the need for icebreaker assistance in ice-covered waters. Engineering Applications of Artificial Intelligence, 158, 111489.
Musharraf, M., Liu, C., & Smith, J. A. (2025). Understanding Crew Estimations for Icebreaker Assistance in Ice-Covered Waters.