With the widespread use of shipborne Automatic Identification System (AIS) and the establishment of shore-based AIS networks in the past twenty years, a large amount of AIS trajectory data has been generated and accumulated in the shore-based systems. However, big data size increases the cost of storing, querying, and processing AIS data in practical applications. AIS data includes not only the ship spatial position information but also the course, speed, heading information et al. In other words, the knowledge of ship handling behavior is hidden in AIS data. In fact, many practical applications, such as maritime accident investigation and evidence collection, route extraction, ship behavior analysis, will require a compression algorithm to retain the ship key handling points in the original trajectory. To address these research challenges, the ship trajectory compression algorithm considering handling patterns is proposed in this paper. In order to explain the implicit handling patterns of AIS trajectories, the suggested method adopts the rate of course change (ROCC) and the rate of speed change (ROSC) in the sliding window as the criterion of whether the current trajectory point can be simplified. Numerical experiments are performed to verify the effectiveness of the proposed algorithm. Compared with Douglas-Peucker (DP) algorithm, Sliding Window (SW) algorithm, Opening Window Time Ratio (OPW-TR) algorithm, the results show that the proposed algorithm can efficiently compress trajectories by considering ship behavior patterns under application requirement.

After collisions at sea, situation evaluation and analysis are very important to follow-up rescue operations. At present, there are few ways give weights in the current situational safety evaluation methods after collisions of marine ships. Most safety evaluation models ignore the blurred boundaries of evaluation grades. To solve these issues, this paper identifies the safety evaluation indicator system and evaluation standards, and establishes an after-collisions safety evaluation model of maritime ships based on the extension cloud theory. This model combines the extension cloud model, the analytic hierarchy process, the entropy weight method, and game theory. Using this model, the situation safety of two collisions was evaluated. The evaluation results reflect the effectiveness of the model. In order to ensure the safety of the lives and property of marine personnel, suggestions have been made to strengthen crew training, improve ship’s self-rescue ability at sea, and establish a complete marine emergency response rescue system.