One limitation of current data-driven automatic crowd modeling methods is that the models generated have low interpretability, which limits the practical applications of the models. In this article, we propose a new data-driven crowd modeling approach that can generate universal behavior rules with better interpretability. Higher interpretability helps people better understand and analyze the rules. Furthermore, the proposed approach considers both static and dynamic features during modeling to generate a realistic crowd, based on the assumption that humans tend to consider different features with respect to their states. In the proposed method, the automatic behavior rule generation problem is formulated as a symbolic regression problem. Then, the problem is solved by multi-objective genetic programming. On one hand, to improve the interpretability of the behavior rules found, a new mechanism is proposed to guide the algorithm to find concise and dimensionally consistent solutions. On the other hand, decisions made by considering static and dynamic features respectively are combined to improve the generated crowd realism. To validate the effectiveness of the proposed method, three real-world datasets are utilized for training and testing. The simulation results demonstrate that the proposed method is able to find universal behavior rules that are competitive to previous work in accuracy while having better interpretability.

中文翻译：

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用于自动人群行为建模的维度感知多目标遗传规划

当前数据驱动的自动人群建模方法的一个局限性是生成的模型可解释性低，这限制了模型的实际应用。在本文中，我们提出了一种新的数据驱动的人群建模方法，可以生成具有更好可解释性的通用行为规则。更高的可解释性有助于人们更好地理解和分析规则。此外，所提出的方法在建模过程中同时考虑静态和动态特征以生成逼真的人群，基于人类倾向于考虑与其状态相关的不同特征的假设。在所提出的方法中，自动行为规则生成问题被表述为一个符号回归问题。然后，通过多目标遗传规划解决该问题。一方面，为了提高找到的行为规则的可解释性，提出了一种新的机制来指导算法找到简洁和尺寸一致的解决方案。另一方面，通过分别考虑静态和动态特征做出的决策被结合起来，以提高生成的人群真实感。为了验证所提出方法的有效性，三个真实世界的数据集用于训练和测试。仿真结果表明，所提出的方法能够找到在准确性上与以前的工作相媲美的通用行为规则，同时具有更好的可解释性。通过分别考虑静态和动态特征做出的决策被结合起来，以提高生成的人群真实感。为了验证所提出方法的有效性，三个真实世界的数据集用于训练和测试。仿真结果表明，所提出的方法能够找到在准确性上与以前的工作相媲美的通用行为规则，同时具有更好的可解释性。通过分别考虑静态和动态特征做出的决策被结合起来，以提高生成的人群真实感。为了验证所提出方法的有效性，三个真实世界的数据集用于训练和测试。仿真结果表明，所提出的方法能够找到在准确性上与以前的工作相媲美的通用行为规则，同时具有更好的可解释性。