| Project/Area Number |
18K18160
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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 62020:Web informatics and service informatics-related
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| Research Institution | Nagoya Institute of Technology |
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Principal Investigator |
Mutoh Atsuko 名古屋工業大学, 工学(系)研究科(研究院), 准教授 (90378240)
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| Project Period (FY) |
2018-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2021: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2020: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2019: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2018: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | データ分析 / 機械学習 / ネットワーク分析 / ロケーションデータ / 入退室管理システム / データマイニング / 社会ネットワーク分析 / 最適化 / 入退室データ / 行動履歴 / ビッグデータ |
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| Outline of Final Research Achievements |
By quantitatively observing the movement behavior within a company based on the company's entry/exit history data, a scientific analysis of human behavior was conducted and applied to measures aimed at revitalizing the workplace. By clustering employees according to their movement patterns and analyzing the relationship with attribute information, we were able to gain insight into the differences in employee behavior patterns by department and gender. In addition, by focusing on the relationship between people who participate in meetings together and conducting a social network analysis based on the meeting participation network, we obtained results that suggest the possibility of using an index of centrality in the meeting participation network as a measure of employee activity. Furthermore, based on movement data, we constructed a meeting room allocation optimization system that takes into account the employees' movement cost level and busyness level.
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| Academic Significance and Societal Importance of the Research Achievements |
交通機関・ 職場など様々な場所における、ICカード・携帯端末等のIT機器からの人の移動に関する膨大なデータが存在する一方で、本来の目的以外への活用は十分でない。本研究では、企業内の入退室管理システムから得られる社員の入退室履歴を人の移動データとして利用し、人間行動の定量的分析を行った。分析結果を用いて、組織内の中心人物や孤立者、移動負担者や多忙者などの推定が可能となり、就業環境改善等への施策へ活用できる可能性を示した。
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