Jointly spatial-temporal representation learning for individual trajectories

Fei Huang, Jianrong Lv, Yang Yue

Abstract: Highlights•A novel method provides generailized trajectory representations with explicitly learned spatial-temporal dependencies.•Jointly modeling the structrually organizated trajectory graph across space and time scales.•Neural responses to spatial-temporal patterns discovered in latent space can reval dynamics between humans and urban environments.