###### Abstract  
Maritime traffic pattern recognition plays a major role in intelligent transportation services, ship monitoring, route planning, and other fields. Facilitated by the establishment of terrestrial networks and satellite constellations of the automatic identification system (AIS), large quantities of spatial and temporal information make ships' paths trackable and are useful in maritime traffic pattern research. The maritime traffic pattern may vary with changes in the traffic environment, so the recognition method of the maritime traffic pattern should be adaptable to changes in the traffic environment. To achieve this goal, a dynamic maritime traffic pattern recognition method is presented using AIS data, which are cleaned, compressed, partitioned, and clustered online. Old patterns are removed as expired trajectories are deleted, and new patterns are created as new trajectories are added. This method is suitable for processing massive stream data. Experiments show that when the marine traffic route changes due to the navigation environment, the maritime traffic pattern adjusts automatically. intelligent transportation system; maritime traffic pattern; AIS; trajectory data mining; trajectory clustering 20  
mandatory requirements of the Convention on the Safety of Life at Sea in 2002, all ships of 300 gross tonnage and upward engaged on international voyages, cargo ships of 500 gross tonnage and upward not engaged on international voyages, as well as passenger ships, irrespective of size, should be fitted with an AIS. With the widespread use of AIS base stations and satellites, a ship's dynamic information can be easily obtained, facilitating marine scientific research and engineering applications. In marine scientific research using AIS data, the following three aspects are focused on: marine risk analysis, anomaly detection, and maritime traffic pattern recognition. In marine risk analysis, [PERSON], et al. [1] used statistical dependency to analyze the relationship between the characteristics of marine traffic (density and distribution) and the frequency of grounding accidents. Fixed and irregular grids were used to analyze marine traffic situations, aid the understanding of ship navigation patterns, and give the necessary input to assist in marine risk prediction [2; 3; 4; 5]. [PERSON], et al. [6] used statistical methods to analyze the relationship between external factors (wind and visibility) and vessel behavior. With artificial intelligence and big data development, marine anomaly detection, one of the main tasks for Marine Situational Awareness (MSA), is a recent research focus [7]. [PERSON] and [PERSON] [8] used DTW as the distance measure between trajectories. Kernel k-means, SVM, and one class SVM are used in clustering, classification, and abnormal behavior identification, respectively. [PERSON], et al. [9] used a density-based algorithm to cluster ship routes. KDE and Bayes rule were used to estimate the density function of clusters and identify abnormal behavior, respectively. [PERSON], et al. [10] used adaptive KDE to cluster ship routes and proposed a probability method to detect anomalous behavior. [PERSON] [11] used the Gaussian Mixture Model (GMM) as a cluster model, and a greedy version of the Expectation-Maximization was used as the training algorithm. [PERSON], et al. [12] compared the performances of the GMM and KDE in ship anomaly detection. [PERSON], et al. [13] used a Bayesian network to identify a ship's anomalous behavior. In addition, ship trajectory clustering plays an important role in shipping network extraction [14], maritime traffic knowledge discovery [15], vessel trajectory prediction [16], ship path planning [17] and so on. Maritime traffic patterns must be recognized to analyze marine traffic risk and identify vessel anomalous behavior. As clustering is an unsupervised approach that does not  
Figure 1: AIS communication link. require prior knowledge, it is often used in the study of mobile pattern analysis [18].