Model of Transit Transport in Arctic Based on Graph Algorithms

Abstract

Transportation in Arctic seas is connected with variability of routes. Availability of those routes is depending on current environmental conditions. In the paper the algorithm for navigation system, which is intended to advice captain the most economically effective and less risky route with the presence of ice chart, is proposed. Smart navigation system for Arctic seas has specific: shortest ways can occur to be impassable or too risky. More long routes through free waters can finally take less fuel comparing to shorter, but covered with ice. Thus, economical profitability of operating in Arctic seas depends on effectiveness of route choosing. To make estimations about most effective route and its length, the method based on graph algorithms is presented in this paper. The ice chart is covered by the graph, which can have form of grid, with neighbor nodes connected by edges. In general, multiple parameters can be assigned to each edge — length, maximal ice thickness on the way, risk, etc. In this paper two separate cost functions are considered: first is responsible for travel expenses, and the second is responsible for ice passability on the route. To find most economically efficient route with minimal possible ice thickness on the way the method with graph modification and Dijkstra algorithm was used. This route provides Pareto-optimal solution for reduced version of the problem. The software, which implements the method, was built. The example of searching for least expensive and Pareto-optimal route is provided. Results are discussed.

Finding Risk-Expenses Pareto-Optimal Routes in Ice-Covered Waters

“…It is evident that a single objective function is not enough to describe both the economic efficiency and safety of the vessel on the route in an optimization problem, especially for navigation in ice-infested waters. Therefore, it is rational to formulate a problem in the form of the bi-or multi-objective routing problem, which can give an alternative set of optimal routes for the given conditions [9,[11][12][13].…”

Broken Ice Photogrammetry in Model-Scale Experiments with Sloped Structure

“…For nadir images, there are still some issues of recognition demanding manual corrections such as the over-segmentation of peculiar-shaped ice floes or the under-segmentation of tightly positioned floes. The recognition of separate modeled ice pieces in an image of the ice tank surface is not an easy task due to fuzzy edges and errors in the recognition of the closing ice floes [14,16,18].…”

Near-Field Route Optimization-Supported Polar Ice Navigation via Maritime Radar Videos

“…Kotovirta et al presented a greedy optimization algorithm using Powell's method and developed a prototype tool based on ice and ship transit modeling for route optimization through ice fields [16]. Much attention has been paid to the optimization of routes, considering such factors as ice conditions, icebreaker assistance, fuel consumption, and total cost [17][18][19][20][21]. Choi et al proposed an uncertaintybased path planning model to find an optimal route under time-varying stochastic constraints [22], modeling ship path planning in ice-covered waters as a dynamic stochastic path planning problem.…”