Adaptive marine traffic behaviour pattern recognition based on multidimensional dynamic time warping and DBSCAN algorithm

Identifying marine traffic behaviour patterns is important for intelligent maritime traffic management systems. The introduction of Automatic Identification System (AIS), which can record speed over ground (SOG) and course over ground (COG) data, makes these patterns accessible. Based on that, a novel adaptive marine traffic behaviour pattern recognition algorithm is proposed to explore potential traffic behaviour patterns. In the proposed recognition algorithm, multidimensional dynamic time warping is introduced to measure the similarity among trajectories; this process considers both the spatial and motion characteristics of trajectories. However, the computational complexity of multidimensional dynamic time warping is high. To reduce the number of required computations, a data simplification algorithm considering ship behaviours is adopted to compress trajectories and, therefore, to achieve an improved computation ability. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is utilized to classify marine traffic behaviour patterns. Due to the poor adaptation of the DBSCAN algorithm, we design a local mean density to evaluate the trajectory density distribution in the given dataset and then determine the optimal parameters in DBSCAN according to the local mean density gradient to improve the adaptivity of the algorithm. The proposed approach is illustrated by numerical experiments involving U.S. east coastal waters. The experimental results indicate that our approach is more accurate and adaptive in terms of recognizing hidden traffic behaviour patterns than other methods.