DeepMob: Learning Deep Models from Big and Heterogeneous Data for Next-generation Urban Emergency Management
| Project/Area Number |
17H01784
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Song Xuan 東京大学, 空間情報科学研究センター, 准教授 (20600737)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
|
| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥12,350,000 (Direct Cost: ¥9,500,000、Indirect Cost: ¥2,850,000)
Fiscal Year 2019: ¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2018: ¥3,120,000 (Direct Cost: ¥2,400,000、Indirect Cost: ¥720,000)
Fiscal Year 2017: ¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
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| Keywords | Disaster Informatics / Big Data and Data Mining / Artificial Intelligence / Urban Computing / Internet of Things |
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| Outline of Final Research Achievements |
The research progress of this project is very good. Our research results were published in the eminent publications for computer science including AAAI 2018, ACM IMWUT 2018, ACM KDD 2019, ACM IMWUT 2019 and Applied Energy 2018 and 2019.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は、フロンティアビッグデータ応用分野において大きな意義を持ち、大規模災害や緊急事態発生後の経済損失、交通機関の混乱、廃業などを最小限に抑えることで、重大な社会的・経済的影響を与える可能性を秘めている。
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Report
(4 results)
Research Products
(12 results)
