Stochastic particle-based models are useful tools for describing the collective movement of large crowds of pedestrians in crowded confined environments. Using descriptions based on the simple exclusion process, two populations of particles, mimicking pedestrians walking in a built environment, enter a room from two opposite sides. One population is passive — being unaware of the local environment; particles belonging to this group perform a symmetric random walk. The other population has information on the local geometry in the sense that as soon as particles enter a visibility zone, a drift activates them. Their self-propulsion leads them towards the exit. This second type of species is referred here as active. The assumed crowdedness corresponds to a near-jammed scenario. The main question we ask in this paper is: Can we induce modifications of the dynamics of the active particles to improve the outgoing current of the passive particles? To address this question, we compute occupation number profiles and currents for both populations in selected parameter ranges. Besides observing the more classical faster-is-slower effect, new features appear as prominent like the non-monotonicity of currents, self-induced phase separation within the active population, as well as acceleration of passive particles for large-drift regimes of active particles.

基于随机粒子的模型是用于描述拥挤密闭环境中大量行人的集体运动的有用工具。使用基于简单排除过程的描述，模仿在建筑环境中行走的行人的两个粒子群从两个相对的侧面进入一个房间。一个人是被动的，他们不了解当地环境。属于该组的粒子执行对称随机游动。其他粒子具有关于局部几何形状的信息，从某种意义上说，粒子一旦进入可见区，就会通过漂移激活它们。他们的自我推进将他们引向出口。第二种物种在这里被称为活跃物种。假定的拥挤程度对应于近乎拥挤的情况。我们在本文中提出的主要问题是：是否可以通过诱导有源粒子动力学的变化来改善无源粒子的输出电流？为了解决这个问题，我们计算了选定参数范围内两个种群的职业数分布图和当前值。除了观察到更经典的“快即慢”效果外，新功能还表现出突出的特征，例如电流的非单调性，有源粒子群中的自感应相分离以及对于有源粒子的大漂移状态的无源粒子的加速。