Weather routing is a critical guarantee for the safe and economical navigation of ships. Existing methods for weather routing still face challenges in selecting the appropriate planning granularity. A granularity that is overly coarse may result in routes passing through coastal and island-rich waters, such as coastal zones and reefs, thus compromising navigational safety. Conversely, a granularity that is excessively fine leads to an exponential increase in computational complexity, rendering the problem intractable. To address this issue, this paper proposes an efficient method for weather routing in coastal and island-rich waters, guided by ship trajectory big data: First, an adaptive quadtree is used to partition the navigable space into an adaptive grid, based on which a route network is constructed using ship trajectory big data. Next, a ship motion model is introduced to build both static and dynamic marine environmental fields, which are used to dynamically update the time weights of the route network. Finally, using the updated route network as a guide, the method aims to minimize voyage time and employs an improved time-varying A* algorithm for weather routing. Experimental results show that the proposed method effectively adapts to coastal and island-rich waters, outperforming the baseline SIMROUTE in safety, optimization, and efficiency. Unlike SIMROUTE, which crosses restricted areas, it avoids such risks entirely. It achieves average reductions of 6.8% in route length and 4.3% in navigation time and is 5.8 times faster than SIMROUTE for fine-grained planning. This balances voyage time, safety, and efficiency, offering a practical weather routing solution.
Zhou et al. (2025) studied this question.