The navigational safety of ships on waterways plays a crucial role in ensuring the operational efficiency of ports. Ship anomalous behavior detection is an important method of water traffic surveillance that can effectively identify abnormal ship behavior, such as sudden acceleration or deceleration. In order to detect potential anomalous ship behavior in real time, a method for ship anomalous behavior detection in waterways is proposed based on text similarity and kernel density estimation. Under the assumption of known traffic patterns entering and leaving the port, this method can identify ship behaviors that violate traffic patterns in real time. Firstly, kernel density estimation is applied to construct a traffic pattern density model for ship trajectories entering and leaving the port, used to estimate the density values of ship motion states. Simultaneously, a semantic transformation method is used to convert traffic pattern trajectory into pattern trajectory text, which is used to identify the ship’s traffic pattern. Subsequently, the historical trajectory data of the target ship are transformed into textual trajectories, and text similarity is used to identify ship inbound and outbound traffic patterns. Furthermore, the constructed traffic pattern density model is used to estimate real-time density values of the state of ship motion, and the trajectory points that exceed the threshold of the anomaly factor are marked as anomalies. Finally, the effectiveness of the proposed method is validated using simulation data, and the results indicate an accuracy of more than 90% for the comprehensive detection of anomalous behavior. This study, approaching the detection of potential ship anomalous behavior from the perspective of port traffic patterns, enriches the methods of ship anomalous behavior detection in port waterways.

Detection of small vessels is of great significance for maritime safety assurance, abnormal vessel tracking, illegal fishing supervision, and combating smuggling. However, the radar reflection intensity of small vessels is low, making them difficult to detected with the radar’s constant false-alarm rate (CFAR) algorithm. To enhance the detection capability for small vessels, we propose an improved CFAR scheme. Specifically, we first compared traditional CFAR processing results of radar data with automatic identification system (AIS) data to identify some special targets. These special targets, which possessed AIS information, but remained undetected by radar, enabled an iTransformer model to generate more reasonable CFAR threshold adjustments. iTransformer adaptively lowered the threshold of the areas around these targets until they were detected by radar. This process made it easier to discover the small boats in the surrounding area. Experimental results showed that our method reduces the missed detection rate of small vessels by 73.4% and the false-alarm rate by 60.7% in simulated scenarios, significantly enhancing the CFAR detection capability. Overall, our study provides a new solution for ensuring maritime navigation safety and strengthening illegal supervision, while also offering new technical references for the field of radar detection.

Spatio-temporal features are crucial for maritime trajectory forecasting, especially in scenarios involving curved waterways or abrupt changes in ship motion patterns. Although Automatic Identification System (AIS) data, which are widely used for trajectory prediction, inherently include temporal and spatial information, effectively strengthening these features and integrating them into prediction models remains challenging. To address this challenge, we propose a Convolutional Neural Network (CNN)-Series-cOre Fused Time Series forecaster (SOFTS)-based framework that explicitly couples spatial and temporal features to achieve high-fidelity maritime trajectory forecasting, especially in scenarios with complex spatial patterns. We first employ a CNN-based spatial encoder to hierarchically abstract spatial density distributions through convolution and pooling operations, thereby learning global spatial structure patterns of ship movements. This encoder emphasizes overall spatial morphology rather than precise individual trajectory points. Second, we employ the SOFTS model to incorporate angular velocity, acceleration, and angular acceleration as input features to characterize ship motion states, which can capture the temporal dependencies of ship motion states from multivariate time series. Finally, the spatial embedding features extracted by the CNN are concatenated with the temporal feature representations learned by SOFTS along the feature dimension to form a joint spatiotemporal representation. This representation is then fed into a fusion regression module composed of fully connected layers to predict future ship trajectories. Experimental results on the validation dataset show that the proposed method achieves an MSE of 0.020 and an MAE of 0.060, outperforming several advanced time series forecasting models in prediction accuracy and computational efficiency. The introduction of angular velocity, acceleration, and angular acceleration features reduces the MSE and MAE by approximately 10.22% and 9.49%, respectively, validating the effectiveness of the introduced dynamic features in improving trajectory prediction performance. These results underscore the proposed method’s potential for intelligent navigation and traffic management systems by effectively enhancing inland river navigation safety and strengthening waterborne traffic monitoring capabilities.

Understanding complex vessel behaviors in port waters is important for maritime traffic supervision, navigation safety management, and intelligent maritime decision-making. However, existing AIS-based studies often focus on isolated behavior recognition or trajectory-level analysis, with limited integration of vessel attributes, navigation scenarios, motion states, and temporally organized behavioral processes. We develop a knowledge graph-driven framework for complex vessel behavior recognition and frequent behavior sequence pattern mining using AIS data. BehaviorEvents are constructed from continuous-navigation segments and integrated with water-area scenarios, motion states, vessel attributes, and temporal relationships to form a unified semantic representation. Based on this representation, interpretable semantic rules are used for event-level complex behavior recognition, while PrefixSpan is applied to Scene–SpeedState–TurningState token sequences to discover recurrent multi-event behavior patterns. Independent expert evaluation, semantic ablation, and sensitivity analyses are used to assess recognition credibility, contextual semantic constraints, and robustness, while a vessel-level Discovery–Validation strategy evaluates the reproducibility of frequent patterns. Experiments on AIS data from Xiamen Port waters involve 16,400 vessels, 239,877 continuous-navigation segments, and 2,457,965 BehaviorEvents, of which 624,561 match at least one predefined semantic rule or candidate condition. Independent expert evaluation of R1–R7 yields a macro-average confirmation rate of 92.11% and a Cohen’s κ of 0.746. Semantic ablation shows that Scene and VesselTypeClass provide important contextual constraints on broad motion-based rule activations, while sensitivity analyses indicate that the main recognition results remain stable under perturbations of motion-state, duration, and temporal-segmentation parameters. From 75,144 valid compressed behavior-token sequences, PrefixSpan identifies recurrent patterns involving medium-speed transit with course adjustments, low-speed–stop combinations, and maneuvering-related behaviors. The dominant Top-20 patterns showed substantial overlap and broadly consistent ranking across the vessel-level Discovery and Validation subsets, with a Jaccard overlap of 0.9048 and a Spearman rank correlation of 0.9654. Comparative evaluation with a normalized relational representation further shows equivalent analytical results, while the knowledge graph provides explicit organization of semantic relationships, temporal paths, and event-level traceability. These results indicate that the proposed framework provides a unified and interpretable semantic basis for connecting event-level complex vessel behavior recognition with sequence-level frequent behavior pattern mining in complex port environments.