2019 Fiscal Year Annual Research Report
DeepMob: Learning Deep Models from Big and Heterogeneous Data for Next-generation Urban Emergency Management
| Project/Area Number |
17H01784
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| Research Institution | The University of Tokyo |
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Principal Investigator |
宋 軒 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
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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 Annual Research Achievements |
In the 2019 fiscal year, the research progress of this project is very good.(1)We developed a novel approach to extract the deep trend only from the current momentary observations.(2)We developed a novel decentralized attention-based human mobility predictor.(3)We developed a novel approach for analyzing the potential reduction in emissions associated with the adoption of a bicycle-sharing system.
Our research results were published in the eminent publications for computer science including ACM KDD 2019, ACM IMWUT 2019 and Applied Energy.
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| Research Progress Status |
令和元年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和元年度が最終年度であるため、記入しない。
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