A real-time optimization for disaster-relief distribution in heterogeneous crowdsourcing
| Project/Area Number |
19K15260
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 25030:Disaster prevention engineering-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Zhang Haoran 東京大学, 空間情報科学研究センター, 客員研究員 (40837457)
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| Project Period (FY) |
2019-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥3,900,000 (Direct Cost: ¥3,000,000、Indirect Cost: ¥900,000)
Fiscal Year 2021: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | インテリジェントエマージェンシーマネジメント / 都市のためのビッグデータ / System Integration / Visualization System / 緊急時の対応 / Disaster response |
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| Outline of Research at the Start |
This research has sought to conceptualize and develop a hybrid relief distribution optimization method by integrating the multi-source crowdsourcing datasets and exploring detection, prediction, and optimization algorithms.
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| Outline of Final Research Achievements |
This research has sought to conceptualize and develop a hybrid relief distribution optimization method by integrating the multi-source crowdsourcing datasets and exploring detection, prediction, and optimization algorithms. Based on the huge volumes of heterogeneous and unstructured datasets, the method is able to detect and predict the relief demand, and then real-time optimize the relief distribution plan. Additionally, under the support of this project, we developed the Small World AI to leverage the latest big data, artificial intelligence and communications technologies to help us sense, understand and predict natural and social change.
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| Academic Significance and Societal Importance of the Research Achievements |
データ収集、ネットワーク伝送、およびコラボレーションテクノロジーが洗練されたことで、現実世界のシナリオを推定、予測、およびデジタル複製する必要性がかつてないほど高まっています。 特に災害対応では、デジタルの世界により、人々と政府は災害の影響をすばやく理解し、複雑で変化する環境での緊急対応の有効性、柔軟性、包括性を維持できます。 そのため、このプロジェクトの支援を受けて、SmallWorldAIシステムを開発しました。 Small World AIは、災害対応の脅威と影響を推定する上で重要な役割を果たし、意思決定をさらに支援してきました。
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Report
(4 results)
Research Products
(12 results)
