Channel pattern recognition based on wavelet clustering algorithm

Trajectory distribution of vessels shares a large difference between coastal waters and exposed waters, thus a segment algorithm is needed when mining the waterway patterns to analysis the traffic flow. Considering traditional spatiotemporal trajectory cluster algorithm cannot meet the requirements on big data processing speed, this paper proposes a wavelet clustering-based vessel channel pattern extraction algorithm. Firstly, a marine navigation feature space is built by means of dividing the target area into grids, which is transformed into a transform domain using two-dimensional discrete wavelet transform algorithm in the next step. Then we select an appropriate threshold to find the connected region and sub-regions with different density distributions are obtained. Drawing on the idea of image processing, this algorithm takes the channel boundary and distribution matrix as the overall channel characteristics. In the end, real historical AIS data is used to verify the method. The results show that such can reduce the processing complexity of spatiotemporal trajectory data, and greatly shorten the response time to find vessel channel patterns.