A traditional A-Star (A*) algorithm generates an optimal path by minimizing the path cost. For a vessel, factors of path length, obstacle collision risk, traffic separation rule and manoeuvrability restriction should be all taken into account for path planning. Meanwhile, the water current also plays an important role in voyaging and berthing for vessels. In consideration of these defects of the traditional A-Star algorithm when it is used for vessel path planning, an improved A-Star algorithm has been proposed. To be specific, the risk models of obstacles (bridge pier, moored or anchored ship, port, shore, etc.) considering currents, traffic separation, berthing, manoeuvrability restriction have been built firstly. Then, the normal path generation and the berthing path generation with the proposed improved A-Star algorithm have been represented, respectively. Moreover, the problem of combining the normal path and the berthing path has been also solved. To verify the effectiveness of the proposed A-Star path planning methods, four cases have been studied in simulation and real scenarios. The results of experiments show that the proposed A-Star path planning methods can deal with the problems denoted in this article well, and realize the trade-off between the path length and the navigation safety.

In recent years, as intelligent ship-navigation technology has advanced, the challenge of accurately modeling and predicting the dynamic environment and motion status of ships has emerged as a prominent area of research. In response to the diverse time scales required for the prediction of ship motion, various methods for modeling ship navigation environments, ship motion, and ship traffic flow have been explored and analyzed. Additionally, these motion-prediction methods are applied for motion control, collision-avoidance planning, and route optimization. Key issues are summarized regarding ship-motion prediction, including online modeling of motion models, real ship validation, and consistency in modeling, optimization, and control. Future technology trends are predicted in mechanism-data fusion modeling, large-scale model, multi-objective motion prediction, etc.

Daily water level forecasting is of significant importance for the comprehensive utilization of water resources. An improved least squares support vector machine (LSSVM) model was introduced by including an extra bias error control term in the objective function. The tuning parameters were determined by the cross-validation scheme. Both conventional and improved LSSVM models were applied in the short term forecasting of the water level in the middle reaches of the Yangtze River, China. Evaluations were made with both models through metrics such as RMSE (Root Mean Squared Error), MAPE (Mean Absolute Percent Error) and index of agreement (d). More accurate forecasts were obtained although the improvement is regarded as moderate. Results indicate the capability and flexibility of LSSVM-type models in resolving time sequence problems. The improved LSSVM model is expected to provide useful water level information for the managements of hydroelectric resources in Rivers.

Low quality automatic identification system (AIS) data often mislead analysts to a misunderstanding of ship behavior analysis and to making incorrect navigation risk assessments. It is therefore necessary to accurately understand and judge the quality problems in AIS data before a further analysis of ship behavior. Outliers were filtered in the existing methods of AIS quality analysis based only on mathematical models where AIS data related quality problems are not utilized and there is a lack of visual exploration. Thus, the human brain’s ability cannot be fully utilized to think visually and for reasoning. In this regard, a visual analytics (VA) approach called AIS Data Quality visualization (ADQvis) was designed and implemented here to support evaluations and explorations of AIS data quality. The system interface is overviewed and then the visualization model and corresponding human-computer interaction method are described in detail. Finally, case studies were carried out to demonstrate the effectiveness of our visual analytics approach for AIS quality problems.

Collision risk identification is an important basis for intelligent ship navigation decision-making, which evaluates results that play a crucial role in the safe navigation of ships. However, the curvature, narrowness, and restricted water conditions of complex waterways bring uncertainty and ambiguity to the judgment of the danger of intelligent ship navigation situation, making it difficult to calculate such risk accurately and efficiently with a unified standard. This study proposes a new method for identifying ship navigation risks by combining the ship domain with AIS data to increase the prediction accuracy of collision risk identification for ship navigation in complex waterways. In this method, a ship domain model is constructed based on the ship density map drawn using AIS data. Then, the collision time with the target ship is calculated based on the collision hazard detection line and safety distance boundary, forming a method for dividing the danger level of the ship navigation situation. In addition, the effectiveness of this method was verified through simulation of ships navigation in complex waterways, and correct collision avoidance decisions can be made with the Regulations for Preventing Collisions in Inland Rivers of the People’s Republic of China, indicating the advantages of the proposed risk identification method in practical applications.