Hong Xia

Self-Supervised Cross-City Trajectory Representation Learning Based on Meta-Learning

Trajectory representation learning transforms complex spatio-temporal features of trajectories into dense, low-dimensional embeddings, enabling applications in intelligent transportation systems. With advances in this field and the availability of large-scale traffic data, intelligent urban systems