| Project/Area Number |
17K18912
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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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| Research Field |
Civil engineering and related fields
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| Research Institution | Ritsumeikan University |
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Principal Investigator |
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| Project Period (FY) |
2017-06-30 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2019: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2018: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | 可聴化 / 交通流 / 高速道路 / 歩行者 / 自転車 / 背景音 / 車両感知器 / パルスデータ / 聴覚ディスプレイ / 音響合成 / サウンドヒューマンインタラクション / 高度道路交通システム（ITS） / 交通工学・国土計画 / データマイニング |
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| Outline of Final Research Achievements |
In this research, we focused on the sonification approach that has been recently proven to be effective for detecting slight dynamical differences. As a basic study on its applicability, we verified the identifiability of traffic states by rendering sounds of traffic flow data. In addition, in order to examine the possibility of controlling the crowd flow in urban space by managing background sound environment, we analyzed the relationship between background sound and pedestrian/bicycle traffic flow characteristics. As a result of sensory experiment, the subjects can classify the traffic condition based on an impression of sound, and the identify the traffic state just before Breakdown. It is concluded that, the sonification approach can be utilized to detecting traffic states with higher accuracy than the visualization. Regarding the pedestrian/bicycle crowd flow, It is found that there were significant differences in traffic flow characteristics through the field experiments.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は交通と音の関係性を明らかにしたものである．とりわけ，可聴化というアプローチに着目した．可聴化とは数値データを音に変換する手法のことである．高速道路で渋滞が発生すると効率性が低下するのみならず，交通事故の危険性が高まる．それを制御するためには，渋滞が発生しそうな状況を感知し，それに応じてドライバーに運転行動を変容させ，渋滞を回避することが有効であると考えられる．前者に関しては，交通流データを可聴化することで，直感的に渋滞が発生しそうな状況を特定できることを明らかにした．また，後者に関して，背景音に応じて人間の行動に影響を及ぼすことが明らかとなった．
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