Map-Constrained Trajectory Recovery Mechanism Based on Transformer
Trajectory reconstruction is a research field for trajectory restoration of low-sampling rate trajectory data.In recent years,in order to improve the accuracy of trajectory reconstruction,some work used deep learning models such as Seq2Seq to improve the efficiency and accuracy of trajectory recovery.However,most of the existing work ignores the long-distance dependencies between trajectory points,resulting in poor accuracy for trajectory reconstruction.Therefore,this paper proposes a trajectory recovery model,called ZTrajRec(Zero-based trajectory recovery)based on Transformer,which captures the long-distance dependency between trajectories through Transformer encoder,and uses the attention mechanism to take into account the similarity between current trajectory and historical trajectories to reconstruct the trajectory directly on the road network.Experimental results show that,on the real Beijing taxi dataset,ZTrajRec improves the recall rate by 3%—4%,compared to the results of the benchmark models.Finally,the result is visually analyzed to demonstrate its plausibility.
万方数据
维普
