G2Vec: Spatial-Temporal Trajectory Generation Network based on Multi-Resolution Feature Correlation

The computation of trajectory similarity in road network is a crucial task, while existing methods have been designed primarily for trajectories in Euclidean space, which overlooks the fact that real-world trajectories are often generated on road networks. In this paper, a new trajectory coding mechanism, G2Vec, is proposed to solve the existing problems in road network trajectory similarity calculation. Aiming at the nonlinear errors between road network information and real trajectory information, as well as the balance between global and individual information, a multi-scale feature fusion learning mechanism is proposed. GCN is used to extract overall road network features, GGNN is used to extract individual track features, and a learnable residual layer is added for position correction. At the same time, we introduce attention mechanisms to capture temporal and spatial correlations and use LSTM to learn the final representation of the trajectory. The G2Vec is evaluated using two real-world trajectory datasets, and the experimental results demonstrate that G2Vec outperforms all the state-of-the-art methods significantly.