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Investigating pedestrian navigation in indoor open space environments using big data

机译：使用大数据调查室内开放空间环境中的行人导航

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摘要

Reliable empirical data are required to understand and model realistic pedestrian navigation behaviours at public crowd-gathering places such as public transportation hubs and shopping malls. Naturalistic walking big data could provide reliable information for investigating realistic pedestrian behaviours and to overcome critical shortcomings of the data collected through controlled laboratory experiments. In this work, we investigate pedestrian navigation in indoor open spaces using naturalistic walking big data collected through video recordings. The extracted data include 299,082 trajectories of individual pedestrians who navigated through the atrium of the Informatics Forum building of the University of Edinburgh. We compare several pedestrian vector fields by calibrating several cellular automaton (CA) models and we finally identify a generalized vector field for pedestrians who are walking in indoor open space environments under normal walking conditions.The output of this study could be useful in enhancing CA-based pedestrian simulation models by representing pedestrian navigation as well as route-choice behaviours more realistically in those models. Simulation tools based on such enhanced models can facilitate practitioners, such as public building designers, to optimize designs considering naturalistic pedestrian behaviours in open spaces.
机译：需要可靠的经验数据来理解和建模在公共集会场所（如公共交通枢纽和购物中心）的实际行人导航行为。大自然的步行大数据可以提供可靠的信息，以调查现实的行人行为，并克服通过受控实验室实验收集的数据的严重缺陷。在这项工作中，我们使用通过录像收集的自然步行大数据来研究室内开放空间中的行人导航。提取的数据包括在爱丁堡大学信息学论坛大楼中庭导航的单个行人的299,082条轨迹。我们通过校准几个元胞自动机（CA）模型比较了多个行人矢量场，最后确定了在正常行走条件下在室内开放空间环境中行走的行人的广义矢量场。本研究的结果可能对增强CA-通过在这些模型中更实际地表示行人导航以及路线选择行为，来实现基于行人的仿真模型。基于此类增强模型的仿真工具可以帮助从业人员（例如公共建筑设计师）考虑开放空间中自然的行人行为来优化设计。

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