Motsch Sebastien, Moussaïd Mehdi, Guillot Elsa G, Moreau Mathieu, Pettré Julien, Theraulaz Guy, Appert-Rolland Cécile, Degond Pierre
School of Mathematical and Statistical Sciences, Arizona State University, Tempe, USA.
Math Biosci Eng. 2018 Dec 1;15(6):1271-1290. doi: 10.3934/mbe.2018059.
Understanding and predicting the collective behaviour of crowds is essential to improve the efficiency of pedestrian flows in urban areas and minimize the risks of accidents at mass events. We advocate for the development of crowd traffic management systems, whereby observations of crowds can be coupled to fast and reliable models to produce rapid predictions of the crowd movement and eventually help crowd managers choose between tailored optimization strategies. Here, we propose a Bi-directional Macroscopic (BM) model as the core of such a system. Its key input is the fundamental diagram for bi-directional flows, i.e. the relation between the pedestrian fluxes and densities. We design and run a laboratory experiments involving a total of 119 participants walking in opposite directions in a circular corridor and show that the model is able to accurately capture the experimental data in a typical crowd forecasting situation. Finally, we propose a simple segregation strategy for enhancing the traffic efficiency, and use the BM model to determine the conditions under which this strategy would be beneficial. The BM model, therefore, could serve as a building block to develop on the fly prediction of crowd movements and help deploying real-time crowd optimization strategies.
理解和预测人群的集体行为对于提高城市地区行人流动效率以及将大型活动中的事故风险降至最低至关重要。我们主张开发人群交通管理系统，通过该系统，对人群的观察结果可以与快速可靠的模型相结合，以快速预测人群运动，并最终帮助人群管理者在定制的优化策略之间做出选择。在此，我们提出一种双向宏观（BM）模型作为此类系统的核心。其关键输入是双向流动的基本图，即行人流量与密度之间的关系。我们设计并进行了一项实验室实验，共有119名参与者在圆形走廊中相向行走，结果表明该模型能够在典型的人群预测情况下准确捕捉实验数据。最后，我们提出了一种简单的隔离策略以提高交通效率，并使用BM模型来确定该策略在何种条件下有益。因此，BM模型可作为一个构建模块，用于实时预测人群运动，并有助于部署实时人群优化策略。
