How simple rules determine pedestrian behavior and crowd disasters
How simple rules determine pedestrian behavior and crowd disasters
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DOI：
10.1073/pnas.1016507108
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发表时间：
2011-04-26
影响因子：
11.1
通讯作者：
Theraulaz, Guy
中科院分区：
文献类型：
--
作者：
Moussaid, Mehdi;Helbing, Dirk;Theraulaz, Guy
随着群体性事件规模的不断扩大和发生频率的不断提高，人群灾害的研究和行人流的模拟已经成为重要的研究领域。然而，即使是成功的建模方法，如牛顿力模型的启发，仍然不完全符合经验观察，有时很难校准。本文提出了一种基于行为认知学的认知科学方法。我们建议，引导视觉信息，即障碍物在候选视线的距离，行人应用两个简单的认知程序，以适应他们的步行速度和方向。虽然比以前的方法更简单，这个模型预测的个人轨迹和集体的运动模式与大量的经验和实验数据的定量一致。该模型预测了自组织现象的出现，例如单向车道或走走停停波的自发形成。此外，行人碰撞与身体碰撞的结合产生了极端密度的人群湍流，这是在最近的人群灾难中观察到的现象。通过提出多个个体之间同时相互作用的综合处理，我们的方法克服了当前物理学启发的对相互作用模型的局限性。因此，通过认知行为学来理解人群动态不仅对更好地准备安全的群体事件至关重要。它还为更现实的集体社会行为建模扫清了道路，特别是人类群体和生物群体。此外，我们的行为学可能有助于提高自主机器人的导航。
With the increasing size and frequency of mass events, the study of crowd disasters and the simulation of pedestrian flows have become important research areas. However, even successful modeling approaches such as those inspired by Newtonian force models are still not fully consistent with empirical observations and are sometimes hard to calibrate. Here, a cognitive science approach is proposed, which is based on behavioral heuristics. We suggest that, guided by visual information, namely the distance of obstructions in candidate lines of sight, pedestrians apply two simple cognitive procedures to adapt their walking speeds and directions. Although simpler than previous approaches, this model predicts individual trajectories and collective patterns of motion in good quantitative agreement with a large variety of empirical and experimental data. This model predicts the emergence of self-organization phenomena, such as the spontaneous formation of unidirectional lanes or stop-and-go waves. Moreover, the combination of pedestrian heuristics with body collisions generates crowd turbulence at extreme densities-a phenomenon that has been observed during recent crowd disasters. By proposing an integrated treatment of simultaneous interactions between multiple individuals, our approach overcomes limitations of current physics-inspired pair interaction models. Understanding crowd dynamics through cognitive heuristics is therefore not only crucial for a better preparation of safe mass events. It also clears the way for a more realistic modeling of collective social behaviors, in particular of human crowds and biological swarms. Furthermore, our behavioral heuristics may serve to improve the navigation of autonomous robots.