| Project/Area Number |
17K12979
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Social systems engineering/Safety system
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| Research Institution | Shizuoka University |
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Principal Investigator |
Sudo Akihito 静岡大学, 情報学部, 講師 (80588369)
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| Project Period (FY) |
2017-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2018: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 機械学習 / 空間情報 / 人の行動 |
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| Outline of Final Research Achievements |
In this study, we developed a machine learning model as a basis for a highly accurate behavioral prediction model that reflects the irregular movement of people in the city. The technical key to solving this problem is a nonlinear model that can discover a small number of feature values that can be predicted with high accuracy, while our previous studies have not been able to predict individual people's behavior to a practical level. In this study, we aim to construct a method of neural networks that extends sparse modeling in a nonlinear manner.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究で構築した手法により、交通量のリアルタイム推定とプライバシー保護を両立できる技術が確立できたと考える。実応用の際には、少数の観測地点で交通量を取得しておき、その交通量データを用いて都市全体の交通量を推定すればよい。本研究の結果は、GPSデータの収集が行われている国であれば適用でき、交通混雑の予測、消費者行動の把握、インフルエンザやエボラ出血熱等の感染経路の特定、大災害時の救援部隊や物資の配分といった実応用のほか、複雑な人の動きの理解という学術的な関心にも寄与する。
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