Personalized individual trajectory prediction via meta-learning

He Zhu, Liyu Zhang, Zipei Fan

Abstract: Individual trajectory prediction is a sequential forecasting task, which uses a moving agent's past trajectory to predict possible future trajectories. Existing work trains one predictor for all users, while few studies consider a personalized predictor that automatically extracts the personal trajectory characteristics for each individual. Also, individual trajectories are highly random and in-homogeneous, resulting in some real target locations are not in the training data set totally, making the model difficult to converge. To address above difficulties, we propose a pre-trained trajectory prediction model via meta-learning, which not only can learn a more generalized initialization parameters to extract the trajectory features of multiple individuals, but also solve the problem of in-homogeneous distribution using pre-trained grid-based classification.