| Project/Area Number |
18K18921
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Review Section |
Medium-sized Section 25:Social systems engineering, safety engineering, disaster prevention engineering, and related fields
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| Research Institution | Osaka University |
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Principal Investigator |
Umetani Shunji 大阪大学, 情報科学研究科, 教授 (80367820)
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| Co-Investigator(Kenkyū-buntansha) |
蓮池 隆 早稲田大学, 理工学術院, 准教授 (50557949)
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| Project Period (FY) |
2018-06-29 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
Fiscal Year 2020: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2019: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2018: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
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| Keywords | 組合せ最適化 / モビリティ |
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| Outline of Final Research Achievements |
Although it has become easier to know the congestion status of routes from route guidance services on mobile phones, it is still hard to mitigate the congestion in the transportation network. The conventional route guidance services recommend individual users to routes obtained by solving a variant of the shortest path problems, which often leads to congestion of users at the same place.
We accordingly consider an integer programming problem to simultaneously find routes for all users that minimizes the peak congestion on the transfer network extended in the time horizon. Based on our numerical experiments for railroad users (during their morning commute) in major cities in Japan, we observed that it is possible to reduce the peak congestion by dispersing users' departure and arrival times by about 10 minutes.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究の成果は，都市圏における交通網の混雑緩和に限らず，移動体の誘導に関わる多くの現実問題に適用可能である．例えば，大規模なイベントやテーマパークにおける観客の誘導，大規模災害時における避難住民の誘導，将来の自動運転車の普及にともなう交通車両の誘導など，多くの重要な現実問題において貢献を果たすことが期待できる．その際に，現実世界から収集された大規模データに基づく最適化問題を現実的な計算時間で解く効率的なアルゴリズムが必要であり，本研究を通じて開発したアルゴリズムはその基盤技術となる．
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