Dynamic Cellular Learning Automata for Evacuation Simulation
Dynamic Cellular Learning Automata for Evacuation Simulation
复制标题
用于疏散模拟的动态细胞学习自动机
DOI：
10.1109/mits.2019.2919523
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发表时间：
2019
影响因子：
3.6
通讯作者：
Huizhao Tu
中科院分区：
文献类型：
--
作者：
Xin Ruan;Zeren Jin;Yue Li;Huizhao Tu
行人行为对疏散过程的演变和最终的疏散时间至关重要。这种行为主要由行人在特定位置处可获得的信息来确定。为了获得一个更全面的基础，合理的运动选择，行人预计将扩大范围的信息获取，这可以反映在空间和时间的概念。现有的疏散模拟模型主要解决了信息在空间概念上的扩展。事实上，行人更有可能根据在整个疏散过程中积累的经验来更新他的运动选择，如时间概念中的信息扩展所定义的。本文开发了一个模型，以实现自适应的选择过程。提出了两种学习自动机来更新某些行人的模仿偏好，并在独立运动和模仿之间做出决定。使用几个关键参数进行敏感性分析，以了解所提出的模型的机制。该模型与地面场模型在疏散时间和疏散过程方面进行了比较，表明该模型在描述疏散过程的不同阶段的行人运动特性方面具有较高的性能。
Pedestrian behaviors are essential for the evolution of an evacuation process and the eventual evacuation time. Such behaviors are mainly determined by the information available to a pedestrian at a certain position. To obtain a more comprehensive base for sensible movement choices, a pedestrian is expected to extend the range of information acquirement, which can be reflected in spatial and temporal concepts. The existing models for evacuation simulations mainly address the extension of information in a spatial concept. In fact, a pedestrian is more likely to update his movement choice based on the experiences accumulated throughout the evacuation process, as defined by information extension in a temporal concept. This paper develops a model to achieve an adaptive choice-making process. Two learning automata are proposed to update the imitation preferences for certain pedestrians and to make decisions between independent movement and imitation. Sensitivity analyses are performed using several key parameters to understand the mechanism of the proposed model. The proposed model is compared with the floor field model in terms of evacuation time and process, showing a high performance in describing pedestrian movement characteristics at different stages of an evacuation process.