Most maritime accidents are caused by human errors or failures. Providing early warning and decision support to the officer on watch (OOW) is one of the primary issues to reduce such errors and failures. In this paper, a quantitative real-time multi-ship collision risk analysis and collision avoidance decision-making model is proposed. Firstly, a multi-ship real-time collision risk analysis system was established under the overall requirements of the International Code for Collision Avoidance at Sea (COLREGs) and good seamanship, based on five collision risk influencing factors. Then, the fuzzy logic method is used to calculate the collision risk and analyze these elements in real time. Finally, decisions on changing course or changing speed are made to avoid collision. The results of collision avoidance decisions made at different collision risk thresholds are compared in a series of simulations. The results reflect that the multi-ship collision avoidance decision problem can be well-resolved using the proposed multi-ship collision risk evaluation method. In particular, the model can also make correct decisions when the collision risk thresholds of ships in the same scenario are different. The model can provide a good collision risk warning and decision support for the OOW in real-time mode.

The Yangtze River’s substantial variation in water depth and current speeds means that inland ships face diverse operational conditions within a single voyage. This paper discusses the adoption of controllable-pitch propellers, which adjust their pitch to adapt to varying navigational environments, thereby optimizing energy efficiency. We developed an optimization framework to determine the ideal pitch angle and rotation speed (RPM) under different sailing conditions. The energy performance model for inland ships was enhanced to account for the open-water efficiency of CPPs across various pitch angles and RPMs, considering the impacts of current and shallow water, among other factors. The optimization approach was refined by incorporating an improved genetic algorithm with an annealing algorithm to enhance the initial population, applying the K-means clustering algorithm for population segmentation, and using multi-parent crossover from diverse clusters. The efficacy of the optimization method for CPP operations was validated by analyzing three operational scenarios of a Yangtze sailing ship. Additionally, key components of the ship performance model were calibrated through experimental tests, demonstrating an anticipated fuel consumption reduction of approximately 5% compared to conventional fixed-pitch propellers.

The safe and efficient navigation of ships traversing the Northern Sea Route demands accurate information regarding sea ice concentration. However, the sea ice concentration forecasts employed to support such navigation are often flawed. To address this challenge, this study advances a statistical interpolation method aimed at reducing errors arising from traditional interpolation approaches. Additionally, this study introduces an autoregressive integrated moving average model, derived from ERA5 reanalysis data, for short-term sea ice concentration forecasts along the Northern Sea Route. The validity of the model has been confirmed through comparison with ensemble experiments from the Coupling Model Intercomparison Project Phase 5, yielding reliable outcomes. The route availability is assessed on the basis of the sea ice concentration forecasts, indicating that the route will be available in the upcoming years. The proposed statistical models are also shown the capacity to facilitate effective management of Arctic shipping along the Northern Sea Route.