我们如何移动：用系统动力学建模人类运动
How Do We Move: Modeling Human Movement with System Dynamics
摘要：
对人类在空间中的运动进行建模，对于交通、公共安全和公共卫生方面的政策制定非常有用。人体运动可以被看作是一个动态过程，人类之间的状态（\eg，位置）随着时间的推移。在人类世界中，智能主体（如人类或具有人类驾驶员的车辆）发挥着重要作用，主体的状态主要描述人类活动，并且状态转换受到人类决策和现实世界系统的物理约束（\eg，主体需要花费时间移动一定距离）的影响。因此，状态转移的建模应该包括Agent决策过程的建模和物理系统动力学的建模。在本文中，我们提出了\ours从一个新的角度来模拟人体运动的状态转换，通过学习的决策模型和集成的系统动力学。\ours利用生成对抗模仿学习来学习人体运动，并在学习过程中集成了来自系统动力学的随机约束。据我们所知，我们是第一个学会用系统动力学来模拟移动代理的状态转换的人。在真实世界数据集上的广泛实验中，我们证明了所提出的方法可以生成与真实世界相似的轨迹，并且在预测下一个位置和生成长期未来轨迹方面优于最先进的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
