2022 Fiscal Year Final Research Report
Foundation of path selection and privacy protection for collaborative transportation
| Project/Area Number |
18K11314
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 60080:Database-related
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| Research Institution | Toyo University (2019-2022)
Kyoto University (2018) |
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Principal Investigator |
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Keywords | 協調型交通 / ネットワークアルゴリズム / グラフ / 位置情報プライバシ / データ統合 |
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| Outline of Final Research Achievements |
We addressed the following three issues that are considered important from the viewpoint of data engineering in collaborative transportation as typified by ridesharing: (1) Route selection. (1) For route selection, we proposed a relayable collaborative urban delivery model, and constructed an strict algorithm and a heuristic that enables to handle on-demand delivery. We also proposed an on-demand bus scheduling method that takes into account users with reservations. (2) We proposed a location privacy protection technique on road networks based on differential privacy. (3) We developed three types of data integration models and their demonstrations, including distributed transactions, for integrating data of multiple transportation providers to form an alliance.
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| Free Research Field |
データ工学
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| Academic Significance and Societal Importance of the Research Achievements |
近年，UberやAmazon Flexに代表される協調型交通・輸送が急速に普及し，従来の交通・輸送モデルを大きく転換している．一方で，交通・輸送に関するデータ工学の従来の技術は協調型交通・輸送モデルにおいてはそのままでは成立しない．例えば経路選択においても従来モデルでは単純な最短経路を求めれば良かったが，協調型交通においては乗客の相乗り等を考慮した経路が必要となる．我々は協調型交通・輸送においてデータ工学上重要な3種類の課題である，(1)経路選択(2)位置情報プライバシの保護(3)複数会社のデータ統合モデル，について研究した．本研究の成果は，今後協調型交通・輸送がさらに発展する基盤となり得る．
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