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arXiv 1808.02466physics.soc-phphysics.data-an
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行人两两交互的基于物理的建模和数据表示

Physics-based modeling and data representation of pedestrian pairwise interactions

Alessandro Corbetta, Jasper Meeusen, Chung-min Lee, Roberto Benzi, Federico Toschi

摘要:

理解和定量建模人群中行走的行人之间的相互作用的物理学的可能性具有引人注目的相关应用,例如,与民用基础设施的设计和安全有关。在这项工作中,我们研究了稀释人群中的观察实验数据的行人-行人相互作用。在运动中,行人调整他们的步行路径,试图保持相互舒适的距离,避免碰撞。在数学模型中,这种行为通常通过“社会”相互作用力来建模。利用高质量,高统计数据集-由数百万从最先进的观测实验中获得的真实轨迹组成-我们开发了一个定量模型,能够解决二元碰撞避免情况下的相互作用。我们根据长距离和短距离力对相互作用进行建模,我们将其应用于非相互作用行人运动的Langevin模型[Corbetta et al. Phys.Rev.E 95,032316,2017]。我们提出的新模型的特点是朗之万动力学与“快”的随机速度波动叠加到“慢”的动态隐藏的模型变量:“预期”的步行路径。该模型能够再现相关的统计数据的碰撞避免运动,如统计的侧位移和通过速度。罕见的碰撞事件也被恢复。此外,与现实生活中的轨道的大数据集进行比较涉及到一个迄今为止被忽视的额外挑战:在包含非常异构的条件的数据库中,仅识别与二进制回避相互作用相对应的相关事件。为了应对这一挑战,我们提出了一种新的方法,基于行人轨迹的图形表示,这使我们能够操作的复杂性降低有效的数据选择。

英文摘要:

The possibility to understand and to quantitatively model the physics of the interactions between pedestrians walking in crowds has compelling relevant applications, e.g. related to the design and safety of civil infrastructures. In this work we study pedestrian-pedestrian interactions from observational experimental data in diluted crowds. While in motion, pedestrians adapt their walking paths trying to preserve mutual comfort distances and to avoid collisions. In mathematical models this behavior is typically modeled via "social" interaction forces. Leveraging on a high-quality, high-statistics dataset - composed of few millions of real-life trajectories acquired from state-of-the-art observational experiments - we develop a quantitative model capable of addressing interactions in the case of binary collision avoidance. We model interactions in terms of both long- and short-range forces, which we superimpose to our Langevin model for non-interacting pedestrian motion [Corbetta et al. Phys.Rev.E 95, 032316, 2017]. The new model that we propose here features a Langevin dynamics with "fast" random velocity fluctuations that are superimposed to the "slow" dynamics of a hidden model variable: the "intended" walking path. The model is capable of reproducing relevant statistics of the collision avoidance motion, such as the statistics of the side displacement and of the passing speed. Rare occurrences of bumping events are also recovered. Furthermore, comparing with large datasets of real-life tracks involves an additional challenge so far neglected: identifying, within a database containing very heterogeneous conditions, only the relevant events corresponding to binary avoidance interactions. To tackle this challenge, we propose a novel approach based on a graph representation of pedestrian trajectories, which allows us to operate complexity reduction for efficient data selection.

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