Synthesizing Human Trajectories Based on Variational Point Processes

Synthesized human trajectories are instrumental for a large number of applications. However, existing trajectory synthesizing models are limited in either modeling variable-length trajectories with continuous temporal distribution or incorporating multi-dimensional context information. In this paper, we propose a novel probabilistic model based on the variational temporal point process to synthesize human trajectories. This model combines the classical temporal point process with the novel neural variational inference framework, leading to its strong ability to model human trajectories with continuous temporal distribution, variable length, and multi-dimensional context information. Extensive experimental results on two real-world trajectory datasets show that our proposed model can synthesize trajectories most similar to real-world human trajectories compared with four representative baseline algorithms in terms of a number of usability metrics, demonstrating its effectiveness. The code and datasets are available at https://github.com/tsinghua-fib-lab/TrajSynVAE .