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首页> 外文期刊>Simulation modelling practice and theory: International journal of the Federation of European Simulation Societies >A cognitive pedestrian behavior model for exploratory navigation: Visibility graph based heuristics approach
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A cognitive pedestrian behavior model for exploratory navigation: Visibility graph based heuristics approach

机译：一种探索性导航的认知行人行为模型：基于可见性图的启发式方法

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摘要

Navigation is a complex issue in simulating pedestrian dynamics. Many existing studies have investigated how pedestrians navigate from an origin to a specific destination, i.e., traveling to familiar or novel destinations. However, pedestrians' navigation through built environment without specific destinations still remains as an open issue. This exploratory navigation usually involves more spatial cognitive behaviors and perceptual considerations. To represent realistic route choice and natural movement of theses pedestrians, this paper presents a cognitive pedestrian behavior model with a focus on the space visibility and individual characteristics. Then the sensitivity of model parameters is analyzed, indicating that regional target, memory of visiting records and individual crowd tendency can have varied influences on pedestrians' navigation decisions and movement patterns. As a case study, the proposed model is implemented using the pedestrian movement data collected from an observational study of Tate Gallery Museum. The simulation results show good consistency with the actual data at the aggregate level and those obtained individually, indicating that our proposed model is credible and can benefit real applications such as master planning of a museum. (C) 2017 Elsevier B.V. All rights reserved.
机译：导航是模拟行人动态的复杂问题。许多现有的研究已经调查了行人如何从起源导航到特定目的地，即旅行到熟悉或新的目的地。然而，人行人通过没有特定目的地的建筑环境导航仍然是一个开放问题。该探索性导航通常涉及更多的空间认知行为和感知考虑因素。代表逼真的路线选择和追逐行人的自然运动，本文介绍了一个认知的行人行为模型，重点是空间可见性和个人特征。然后分析了模型参数的敏感性，表明区域目标，访问记录和个人人群倾向的记忆可以对行人的导航决策和运动模式具有各种影响。作为一个案例研究，所提出的模型是利用从泰特画廊博物馆的观察研究中收集的行人运动数据来实现。仿真结果表现出与总体水平的实际数据的良好一致性，并且单独获得的数据良好，表明我们所提出的模型是可信的，可以使博物馆的硕士规划等实际应用。 （c）2017 Elsevier B.v.保留所有权利。

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