Abstract: Destination prediction of moving trajectories is an important part of location-based ser- vices. The existing prediction methods have two problems: one is that the historical trajectory cannot completely cover all possible query traces (data sparse problem), and the other one is that the difference in the influence of prefix trajectory points on the prediction results is not taken into account (long-term dependence problem). As a result, a trajectory distributed representation method is proposed. Firstly, the trajectory sequence is divided into grids, and the high-dimensional one-hot code vectors representing the location is reduced to generate low-dimensional embedding vectors which contain geographical topo- logical relationships; Secondly, the destinations are clustered, and the cluster centers are used as the labels of the trajectories in the cluster, which reduces the difference of similar trajectories, highlights the characteristics of dissimilar trajectories, and effectively overcomes the problem of data sparseness. In the destination prediction, the self-attention mechanism is introduced into the LSTM network, and a destination prediction model(SATN-LSTM) based on the LSTM network are proposed. Mining the key points from the sequence and assigning weights according to their importance, which solves the long-term dependency problem better. Finally, several experiments are carried out on the real trajectory datasets to verify the effectiveness of our model. Compared with the existing models, it is verified that our model gets higher accuracy.

Key words: destination prediction, mesh division, self-attention mechanism, moving trajectory ,