SDFA: A Vessel Trajectory Clustering Technology and Application Oriented to Multi-feature Fusion Model

With the rapid development of ocean transportation, vessel trajectory mining and analysis have become more and more important. Trajectory clustering is the basis of practical applications. Traditional trajectory similarity calculation methods are relatively low in accuracy and efficiency, the existing deep learning methods mostly extract features of static ones, ignoring the multi-feature combination of dynamic and static features. This paper proposes a static-dynamic-feature fusion method based on convolutional auto-encoder, which can extract more perfect trajectory features in artificial intelligence field. Experiments on real datasets have demonstrated that compared with other methods, there is at least a 5%-10% improvement in terms of classic clustering metrics and lower time consumption.