| Project/Area Number |
16K01295
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Social systems engineering/Safety system
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| Research Institution | Hannan University |
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Principal Investigator |
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2018: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2017: ¥260,000 (Direct Cost: ¥200,000、Indirect Cost: ¥60,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 避難シミュレーション / マルチエージェントシステム / 流動モデル / 遺伝的アルゴリズム / 避難実験 / パラメータ推定 / 避難モデル / モデル精緻化 |
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| Outline of Final Research Achievements |
Several kinds of disaster simulations have been developed for this reason. Many evacuation simulation methods are based on multi-agent systems. The social force model is one approach that considers the influences and interactions between evacuees. When aiming to improve the precision of social force model simulations, the model parameters should be appropriately determined.
In this research, a method for determining the parameters used in the social force model is proposed by applying an evolutionary computing method to collected evacuee flow data. The proposed parameters estimation method were applied to some evacuee flow data which were observed in the evacuation experiments, and the distribution of agents parameters, which determine the dynamics of agent flow, would be estimated consequently. We could simulate the evacuation situation using the parameter distribution as a stochastic process.
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| Academic Significance and Societal Importance of the Research Achievements |
災害時の避難状況を想定するために避難シミュレーションが提案，実装されている．避難シミュレーションの結果は，避難者の行動特性や避難状況を表す環境特性を表す多数のパラメータの設定に依存するが，その設定方法についての指針は示されていない．本研究では小規模避難実験を通して，避難者モデルパラメータの分布を推定し定性的な評価を行ったものであるが，この成果は，駅舎などの多数要素を含む人流データに適用することで，大規模シミュレーションに必要なパラメータ設定の可能性を示唆したものである．今後，種々の環境における多数の流動データを分析することでモデルパラメータの環境依存性を考察できる方策を構築した．
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