| Project/Area Number |
18K04400
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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 22050:Civil engineering plan and transportation engineering-related
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| Research Institution | Akita National College of Technology |
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Principal Investigator |
Hasegawa Hironobu 秋田工業高等専門学校, その他部局等, 准教授 (00533374)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2020: ¥130,000 (Direct Cost: ¥100,000、Indirect Cost: ¥30,000)
Fiscal Year 2019: ¥390,000 (Direct Cost: ¥300,000、Indirect Cost: ¥90,000)
Fiscal Year 2018: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
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| Keywords | 交通行動分析 / 視線計測 / 慣性センサ / センサフュージョン / 行動文脈 / 交通安全 / 歩行者挙動 / 通学路 |
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| Outline of Final Research Achievements |
This research project was conducted for the following two purposes: 1) to automatically and accurately detect the activity state of pedestrians using time-series sensor data including physiological indices collected by an eyeglass-type device (eye measurement, 3-axis acceleration sensor, and 3-axis angular rate sensor) and a smartwatch (GPS), and 2) to develop a technology to identify the causal object from the eye measurement results. Of these, we achieved satisfactory results for the former. In addition, we were able to achieve mostly the latter. In addition, we proposed a methodology to test the effect of factors on the dissimilarity between scanpaths representing eye movements using PERMANOVA, and found that there was a significant effect on the relationship between gazing behavior and familiarity/unfamiliarity with the road environment and holding a driving license.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では視線の動きを表すスキャンパス同士の非類似度に対する因子の影響をPERMANOVAによって検定する方法論を提案し，注視行動と道路環境に対する慣れ・不慣れおよび自動車免許保有との関係について有意な影響があることを発見した．同様のアプローチはイベント時の群衆行動における視線誘導・交通誘導施策効果の把握，通学路の危険予知トレーニングの効果把握等にも適用可能であり，大きな発展可能性を有している．
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