Big-data-driven Arctic maritime transportation

Due to global warming, the quantity of Arctic sea ice has been drastically reduced in recent decades. Consequently, navigating the Arctic is becoming increasingly commercially feasible during summer seasons. It will bring huge transportation benefits due to reduction in navigational time, fossil energy consumption, and related carbon emissions. Arctic sea ice prone regions are a significant challenge for charting Arctic routes, so it is necessary to forecast the extent, thickness, volume, and drift patterns of sea ice along the Arctic navigational routes by near real-time data-mining of various big data (for example, meteorology/climate, ocean, remote sensing, environment, economy, computer-based modeling). Moreover, since Arctic sea ice always moves the currents and winds and different meteorological conditions can cause the melting and freezing of sea ice, the trans-Arctic sea routes need to be adjusted dynamically from the standard routes to minimize costs and risks. Based on big data mining, we establish a near real-time dynamic optimal trans-Arctic route (DOTAR) system to guarantee safe, secure, and efficient trans-Arctic navigation. Such dynamic routes will help navigators to maintain safe distances from icebergs and large-size ice floes and to save time, fuel, operational costs and risks.