LAUSR

Abstract Recognition and understanding of ship mobility pattern have great significance for intelligent maritime applications, i.e. route discovery and anomaly detection. Besides a number of pattern discovery techniques currently derived from ship trajectory, topic modeling popular in the field of Natural Language Processing may provide a novel way to detect implicit patters underlying massive ship trajectories treated as documents. This paper is motivated to apply a semantic analysis method to explore potential mobility patterns from ship trajectories in inland river by combining semantic transformation and topic model. A coarse-grained semantic transformation model is firstly defined to translate each ship trajectory into a document containing a series of sequential motion words. A motion word is generally a synthetic semantic representation of three trajectory features (location, course and speed). All ship trajectories can then be examined and analyzed as a document corpus. A classic topic model (i.e. Latent Dirichlet Allocation, LDA) is employed to explore hidden ship mobility patterns from trajectory document corpus. The effectiveness of this approach is illustrated through a case study at Wuhan waterway, located at middle stream of Yangtze River in China.