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2018 Fiscal Year Final Research Report

Anticipatory resource optimization based on model predictive control in virtual mobile networks

Research Project

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Project/Area Number 17H07156
Research Category

Grant-in-Aid for Research Activity Start-up

Allocation TypeSingle-year Grants
Research Field Information network
Research InstitutionTokyo City University

Principal Investigator

Shiomoto Kohei  東京都市大学, 知識工学部, 教授 (00535750)

Project Period (FY) 2017-08-25 – 2019-03-31
Keywordsモバイルネットワーク / モデル予測制御 / 移動データ / 系列パターンマイニング / 資源割り当て
Outline of Final Research Achievements

We developed a method that predicts the future location of human. The proposed method employs a sequential pattern mining algorithm called BIDE method to extract frequent trajectory patterns from a large amount of human trajectory data and calculates a score of frequent trajectory pattern to predict the future location of human. We evaluate the proposed method using about 18,000 trajectory data collected from 200 users during five and a half years and demonstrate that the proposed method yields 70% accuracy of prediction.
Conventional resource allocation methods that use only traffic data could perform poorly when the numbers of users in the areas fluctuate. We develop a model predictive control for resource allocation method that uses not only traffic data but also predicted number of human in areas. We demonstrate that the proposed method, with help of exponential smoothing, allocates sufficient resource even when short-term fluctuation exists.

Free Research Field

コンピュータネットワーク

Academic Significance and Societal Importance of the Research Achievements

個人の移動データを系列パターンとしてモデル化し、マイニングアルゴリズムで分析することで、頻出移動パターンを抽出し、それをもとに移動予測ができることを示した。モバイル通信の設計や保守だけでなく、防災・安全、交通・運輸などへの応用が考えらる。自治体などと連携し防災・避難計画に役立てたり、鉄道会社と連携して沿線の都市計画へ活用することが考えられる。今後は、日本国内の大都市圏での人の移動データの分析に取り組み、これらのモバイル通信網の設計以外の応用にも取り組む。
また、精度の向上や計算時間の短縮などの性能向上に取り組む一方で、使い勝手の良いソフトウェアの作成に取り組む。

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Published: 2020-03-30  

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