A Trajectory Classification Model Using Grammar Parsing

Abstract

Trajectory classification is a hot topic in the field of spatiotemporal data mining. Existing models exert spatial or temporal computation on trajectory data, which require huge efforts and are often time consuming and lack of efficiency. This article proposes a model to classify unknown ship trajectories through a syntax recognition approach. By using the background semantic information in the rasterized sea chart, the model transforms the ship trajectories into symbolic sentences containing both spatiotemporal and semantic information, and reduces their scale. The class feature is expressed as a context-free grammar and the data classification is implemented through syntax parsing. The parsing requires less computation and is more efficient. Experiments are carried out to verify the model’s practicability, and the results show that it is valid and effective.