Modeling how human moves in the space is useful for policy-making in
transportation, public safety, and public health. Human movements can be viewed
as a dynamic process that human transits between states (\eg, locations) over
time. In the human world where intelligent agents like humans or vehicles with
human drivers play an important role, the states of agents mostly describe
human activities, and the state transition is influenced by both the human
decisions and physical constraints from the real-world system (\eg, agents need
to spend time to move over a certain distance). Therefore, the modeling of
state transition should include the modeling of the agent's decision process
and the physical system dynamics. In this paper, we propose \ours to model
state transition in human movement from a novel perspective, by learning the
decision model and integrating the system dynamics. \ours learns the human
movement with Generative Adversarial Imitation Learning and integrates the
stochastic constraints from system dynamics in the learning process. To the
best of our knowledge, we are the first to learn to model the state transition
of moving agents with system dynamics. In extensive experiments on real-world
datasets, we demonstrate that the proposed method can generate trajectories
similar to real-world ones, and outperform the state-of-the-art methods in
predicting the next location and generating long-term future trajectories.Comment: Accepted by AAAI 2021, Appendices included. 12 pages, 8 figures. in
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence
(AAAI'21), Feb 202