TrajGDM: A New Trajectory Foundation Model for Simulating Human Mobility

Chen Chu, Hengcai Zhang, Feng Lu

Abstract: Capturing the universal movement pattern and simulating human mobility is one of the most important trajectory data-mining tasks. Most of the current mobility modeling methods are specially designed to solve a specific task, which leads to questions regarding generalizability. Aiming to construct a general trajectory foundation model to overcome this weakness, we proposed a generative Trajectory Generation framework based on Diffusion Model (TrajGDM) to capture the universal mobility pattern and simulate human mobility. It is capable of solving multiple trajectory tasks through learning the generation of the trajectory. The generation process of a trajectory is modeled as a step-by-step uncertainty reducing process. A trajectory generator network is proposed to estimate the uncertainty in each step, and a trajectory diffusion and generation process is defined to train the model to simulate the real dataset. Finally, we compared the proposed method with 6 baselines on 2 public trajectory datasets: T-Drive and Geo-life. By comparing 5 different evaluation metrics, the result showed that the similarity between generated and real trajectories' movement character measured by Jensen-Shannon Divergence (JSD) improved by at least 50.3% in both datasets. It also addresses the problem of generating diverse trajectories, which is ignored by most previous models. Moreover, we applied zero-shot inferences on two basic trajectory tasks: trajectory prediction and trajectory reconstruction. The zero-shot prediction accuracy of our model is up to 23.4% higher than the benchmark, and the reconstruction accuracy improves by a maximum of 25.6%.