The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluations.

Developing adequate ship domain models may significantly benefit vessel navigation safety. In essence, navigation safety is collectively affected by the navigable waterway condition, the size and shape of the ship, and operators' skills. The existing ship domains mainly use constant values for the model input parameters, making them incapable of handling site-specific conditions. This study proposes dynamic ship domain models that take into consideration navigable waterway conditions, ship behaviours, ship types and sizes, and operators' skills in a holistic manner. Specifically, the conditions of restricted waterways are classified into navigating along the channel, crossing the channel, joining another flow and turning. The ship types considered include ships that transport non-hazardous goods and Liquid Natural Gas (LNG) ships that are in need of additional security zones. A computational experiment is conducted for model application using data on water channel design and ship traffic volumes related to navigating along the channel, joining another flow and turning. Comparisons of results obtained between the proposed dynamic models with real ship traffic counts reveal that the proposed models could achieve a higher level of accuracy in estimating the capacity of restricted water channels. It therefore could potentially deliver safety enhancements of waterway transportation.

Many factors affect the navigation safety of the port waters and most of them are indeterminate. Therefore, we can take them as part of an uncertain system, which consists of ship subsystem, human subsystem, environment subsystem, pilot service subsystem and port service subsystem. The paper puts forward a Set Pair Analysis (SPA) theory on the evaluation of navigation safety of port waters, which can make full use of information on factors influencing navigation safety of the port waters. By using SPA in evaluation of navigation safety of the port waters, not only the factors influencing navigation safety of the port waters but also the factors' weight is fully considered and the relationship among them is reflected. SPA can improve the navigation safety of the port waters. It is helpful to improve the safety situation of the port waters and the navigation efficiency of the port waters to prevent the occurrence of water traffic accidents. It is a new method and is also feasible to evaluate navigation safety of the port waters based on SPA. And prospecting the application of SPA in port safety assessment, it can be expanded in space and time aspect.

The conception of the ship domain model is introduced, and the collision probability model is constructed making use of the ship domain model and ship motion characteristics. The collision probability calculation method based on ship domain is put forward by using the geometric probability model, combining overlapping areas of two ship domains. In addition, collision probability is analyzed in crossing situations in port waters based on ship domain. The application of the collision probability model is put forward in ship collision avoidance and maritime safety supervision to provide a reference for the prevention of ship collisions and other traffic accidents and for the supervision for Maritime administration departments.

Time pattern of traffic flow is one of the important features of vessel traffic flow. It indicates the aggregation and dispersion degree and reflects the risk of the vessel traffic flow. The model is constructed for doing statistical analysis and prediction to the time pattern of traffic flow by Mathematical statistics and Theory of Forecasting. The prediction model can be used to forecast the future time pattern of traffic flow by parameters of the statistic model. The time pattern of traffic flow of Tianjin Port is statistically analyzed and forecasted to judge risk of time pattern of traffic flow.