| Project/Area Number |
21246080
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Civil engineering project/Traffic engineering
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| Research Institution | The University of Tokyo |
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Principal Investigator |
HATO Eiji 東京大学, 大学院・工学系研究科, 教授 (60304648)
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| Co-Investigator(Kenkyū-buntansha) |
ASAKURA Yasuo 神戸大学, 工学研究科, 教授 (80144319)
YAMAMOTO Toshiyuki 名古屋大学, 工学研究科, 准教授 (80273465)
MORIKAWA Takayuki 名古屋大学, 環境学研究科, 教授 (30166392)
KAWANO Hiroyuki 南山大学, 数理情報学部, 教授 (70224813)
KURAUCHI Shinya 愛媛大学, 理工学研究科, 講師 (90314038)
ZHANG Junyi 広島大学, 国際協力研究科, 教授 (20284169)
TAKAMI Atsushi 東京大学, 大学院・工学系研究科, 助教 (40305420)
IRYO Takamasa 神戸大学, 工学研究科, 教授 (10362758)
SASAKI Kuniaki 山梨大学, 医学工学総合研究部 (30242837)
井上 亮 東北大学, 情報科学研究科, 准教授 (60401303)
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| Project Period (FY) |
2009 – 2012
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| Project Status |
Completed (Fiscal Year 2012)
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| Budget Amount *help |
¥44,460,000 (Direct Cost: ¥34,200,000、Indirect Cost: ¥10,260,000)
Fiscal Year 2012: ¥9,620,000 (Direct Cost: ¥7,400,000、Indirect Cost: ¥2,220,000)
Fiscal Year 2011: ¥11,050,000 (Direct Cost: ¥8,500,000、Indirect Cost: ¥2,550,000)
Fiscal Year 2010: ¥11,700,000 (Direct Cost: ¥9,000,000、Indirect Cost: ¥2,700,000)
Fiscal Year 2009: ¥12,090,000 (Direct Cost: ¥9,300,000、Indirect Cost: ¥2,790,000)
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| Keywords | プローブパーソン調査 |
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| Research Abstract |
It turns to the advancement of the synthetic behavioral survey by the data fusion theory which used probe technology, development of the automatic identification algorithm of the behavioral context, the traffic survey and management systems based on probe data, and the mobility service incorporating these have been done. While the possibility of the synthetic behavioral survey which used for the probe technology which can store the same individual's time series behavior data was shown, possibility of probe technologies towards evaluations of various real-time transportation policies. By combining an Adaboost algorithm with SVM according to using especially sensor information, It succeeded in enabling automatic travel behavior identification by95% or more using the smart phone which has an accelerometer. With constituting the synthetic travel behavior data platform which combined PP technologies and PT survey data, the possibility was shown about the shift to all the year type travel survey from the one-shot travel survey, and transport planning using synthetic advanced travel survey technologies.
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