结合路线选择模型和相似度评估的大规模真实数据人群仿真

Ryo Nishida, Masaki Onishi, Koichi Hashimoto
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引用次数: 0

摘要

用数学模型表达人群运动的建模和仿真方法被广泛而积极地研究,以了解人群运动和解决人群事故。现有的人群建模文献只关注步行行为的决策。然而,路径选择决策作为一个更高层次的决策,也应该建模,以构建更实际的仿真。此外,结合实际数据的路线选择模型对人群仿真的再现性评价不足。因此,我们推广并提出了一个包括实际人群运动测量、路线选择模型估计和人群模拟器构建的人群模拟框架。我们使用离散选择模型作为路径选择模型,使用社会力模型作为步行模型。在实验中,我们测量了剧院疏散演习和数万人移动的烟花活动中的人群运动,并证明了结合路线选择模型的人群模拟可以更准确地再现真实的大规模人群运动。
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Crowd simulation incorporating a route choice model and similarity evaluation using real large-scale data
Modeling and simulation approaches that express crowd movement with mathematical models are widely and actively studied to understand crowd movement and resolve crowd accidents. Existing literature on crowd modeling focuses on only the decision-making of walking behavior. However, the decision-making of route choice, which is a higher-level decision, should also be modeled for constructing more practical simulations. Furthermore, the reproducibility evaluation of the crowd simulation incorporating the route choice model using real data is insufficient. Therefore, we generalize and propose a crowd simulation framework that includes actual crowd movement measurements, route choice model estimation, and crowd simulator construction. We use the Discrete choice model as the route choice model and the Social force model as the walking model. In experiments, we measure crowd movements during an evacuation drill in a theater and a firework event where tens of thousands of people moved and prove that the crowd simulation incorporating the route choice model can reproduce the real large-scale crowd movement more accurately.
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