Deep representation learning of activity trajectory similarity computation

Massive trajectory data stems from the prevalence of equipment supporting GPS and wireless communication technology. Based on these data, the computation of trajectory similarity has become a research hotspot in spatial database during recent years. The trajectory sampling problem caused by the different sampling strategies of the device has many negative effects on the similarity measurement. Although many recent studies have solved this problem by trajectory complements, these methods still have drawbacks because only spatial and temporal features are considered. Activity trajectory, with the development of LBSN (Location-based Social Network), endows traditional trajectory data with additional semantic information. In this paper, we fuse the spatio-temporal characteristics with extra activity information of the activity trajectory to solve the shortcomings in the process of trajectory complement. Specifically, we utilize vectors containing these three kinds of semantic information as the input of deep learning model for acquiring final trajectory representation, which is not only robust to low sampling, but also can capture implicit features in the trajectory. What's more, for the purpose of meeting the actual situation of network training, we propose a novel loss function based on the attention mechanism in natural language processing to distinguish these three kinds of information. Our framework, called At2vec, demonstrates better results than existing baselines when making extensive experiments on real trajectory databases.