Presentation 2007-09-18
Real-time Imputation Method for Sparse Floating Car Data
Tomoaki HIRUTA, Masatoshi KUMAGAI, Kenji SUZUKI, Takayoshi YOKOTA,
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Abstract(in English) This paper discusses real-time imputation method for sparse floating car data with feature space which has multiple bases which express correlation of a lot of links. This method consists of off-line and on-line process: determination of feature space from past floating car history (off-line process); feature space projection of current floating data and estimation of missing data performed by inverse projection from feature space (on-line process). In this paper, in the off-line process, we reveal relation between feature space and missing rate of floating car data, and feature space and accumulation time of past floating car data. And we show that feature space can be determined with enough accumulation time even under sparse floating car data.
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Keyword(in English) Floating Car / Missing Data / Imputation / Feature Space Projection
Paper # ITS2007-20
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Committee ITS
Conference Date 2007/9/11(1days)
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Registration To Intelligent Transport Systems Technology (ITS)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Real-time Imputation Method for Sparse Floating Car Data
Sub Title (in English)
Keyword(1) Floating Car
Keyword(2) Missing Data
Keyword(3) Imputation
Keyword(4) Feature Space Projection
1st Author's Name Tomoaki HIRUTA
1st Author's Affiliation Hitachi Research Lab., Hitachi Ltd.()
2nd Author's Name Masatoshi KUMAGAI
2nd Author's Affiliation Hitachi Research Lab., Hitachi Ltd.
3rd Author's Name Kenji SUZUKI
3rd Author's Affiliation Car Information Systems Div., Hitachi Ltd.
4th Author's Name Takayoshi YOKOTA
4th Author's Affiliation Hitachi Research Lab., Hitachi Ltd.
Date 2007-09-18
Paper # ITS2007-20
Volume (vol) vol.107
Number (no) 215
Page pp.pp.-
#Pages 6
Date of Issue