Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a classic density-based clustering method that can identify clusters of arbitrary shapes in noisy datasets. However, DBSCAN requires two input parameters: the neighborhood distance value (Eps) and the minimum number of sample points in its neighborhood (MinPts), to perform clustering on a dataset. The quality of clustering is highly sensitive to these two parameters. To tackle this issue, this paper introduces a parameter-adaptive DBSCAN clustering algorithm based on the Whale Optimization Algorithm (WOA-DBSCAN). The algorithm determines the parameter range based on the dataset distribution and utilizes the silhouette coefficient as the objective function. It iteratively selects the two input parameters of DBSCAN within the parameter range using the WOA. This approach ultimately achieves adaptive clustering of DBSCAN. Experimental results on five typical artificial datasets and six real UCI datasets demonstrate the effectiveness of the proposed WOA-DBSCAN algorithm. Compared with DBSCAN and its related optimization algorithms, WOA-DBSCAN shows significant improvements. The F-values of WOA-DBSCAN increased by 9.8%, 13.2%, and 2% respectively in two-dimensional artificial datasets. Additionally, the accuracy values on low to medium dimensional real datasets increased by 22.3%, 10%, and 23.3%. Hence, WOA-DBSCAN can maintain the clustering ability of DBSCAN while achieving adaptive parameter clustering.

Vessel trajectory data are currently the most important data source for vessel trajectory data mining research. However, vessel AIS data have a short sampling time interval and a large amount of data redundancy, which hampers the efficient utilization of AIS data. In order to effectively remove redundant information from AIS data and improve its usage efficiency, a compression algorithm for vessel trajectory data compression algorithm considering critical region identification (VATDC_CCRI) is proposed. The VATDC_CCRI algorithm identifies the critical regions of a vessel’s trajectory by analyzing the distribution of node variation rates. It employs the Douglas–Peucker (DP) algorithm to compress the data in these critical regions, reducing the distortion of the trajectory after compression. Additionally, the algorithm utilizes a sliding window approach to process the initial trajectory to improve the quality of the compressed vessel trajectories and retain as many spatiotemporal characteristics of the original trajectories as possible. It combines the feature nodes from the crucial regions in the vessel’s trajectory with the results obtained from the sliding window algorithm, effectively compressing the vessel’s trajectory. Experiments conducted on individual and multiple trajectories demonstrate that the VATDC_CCRI algorithm achieves higher compression rates and exhibits faster processing speeds compared to other classical vessel trajectory compression algorithms while preserving the shape of the vessel’s trajectory significantly.

High-completeness ship trajectory data are critical for analyzing navigation behavior characteristics and enhancing effective maritime management. To address the common issues of prolonged AIS data loss for small coastal vessels in nearshore waters, an intelligent trajectory repairing model based on the artificial potential field-enhanced A* algorithm (APF-A*) has been proposed. Kernel density estimation was utilized to quantify the distribution characteristics of vessels, thereby constructing an attractive potential field based on historical trajectories and a repulsive potential field based on coastal terrain. Speed distribution characteristics were extracted from historical trajectory points in different regions; on the basis of this, the A* algorithm, integrated with attractive and repulsive fields, was proposed to repair missing trajectory segments. Based on the speed distribution characteristics, time intervals, and distance information, the temporal information of the vessel trajectories was effectively reconstructed. The present study fills the research gap in AIS data reconstruction for small coastal vessels in complex coastal waters. A case study has been conducted in Luoyuan Bay, Fujian Province, China, to further validate the proposed model. The results demonstrate that the trajectory repairing model based on the artificial potential field-enhanced A* algorithm outperformed other models. More specifically, the Hausdorff Distance and Dynamic Time Warping (DTW) metrics decreased by 81.67% and 91.56%, respectively. The present study shares useful insights into intelligent maritime management and further supports accident prevention in coastal waters.