| アイテムタイプ |
学術雑誌論文 / Journal Article.(1) |
| 公開日 |
2018-06-28 |
| 書誌情報 |
en : IEEE Transactions on Sustainable Computing
巻 2,
号 4,
p. 333-344,
発行日 2017-07-19
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| タイトル |
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タイトル |
Predicting Transportation Carbon Emission with Urban Big Data |
|
言語 |
en |
| 言語 |
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言語 |
eng |
| キーワード |
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言語 |
en |
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主題Scheme |
Other |
|
主題 |
Transportation carbon emission |
| キーワード |
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言語 |
en |
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主題Scheme |
Other |
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主題 |
urban big data |
| キーワード |
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|
言語 |
en |
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主題Scheme |
Other |
|
主題 |
multilayer perceptron neural network |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
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主題 |
real-time prediction |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| アクセス権 |
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|
アクセス権 |
open access |
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アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
LU, Xiangyong
太田, 香
董, 冕雄
YU, Chen
JIN, Hai
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| 室蘭工業大学研究者データベースへのリンク |
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表示名 |
太田 香（OTA Kaoru） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000140_ja.html |
| 室蘭工業大学研究者データベースへのリンク |
|
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表示名 |
董 冕雄（DONG Mianxiong） |
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URL |
http://rdsoran.muroran-it.ac.jp/html/100000145_ja.html |
| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Transportation carbon emission is a significant contributor to the increase of greenhouse gases, which directly threatens the change of climate and human health. Under the pressure of the environment, it is very important to master the information of transportation carbon emission in real time. In the traditional way, we get the information of the transportation carbon emission by calculating the combustion of fossil fuel in the transportation sector. However, it is very difficult to obtain the real-time and accurate fossil fuel combustion in the transportation field. In this paper, we predict the real-time and fine-grained transportation carbon emission information in the whole city, based on the spatio-temporal datasets we observed in the city, that is taxi GPS data, transportation carbon emission data, road networks, points of interests (POIs), and meteorological data. We propose a three-layer perceptron neural network (3-layerPNN) to learn the characteristics of collected data and infer the transportation carbon emission. We evaluate our method with extensive experiments based on five real data sources obtained in Zhuhai, China. The results show that our method has advantages over the well-known three machine learning methods (Gaussian Naive Bayes, Linear Regression, and Logistic Regression) and two deep learning methods (Stacked Denoising Autoencoder and Deep Belief Networks). |
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言語 |
en |
| 出版者 |
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出版者 |
IEEE |
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言語 |
en |
| 出版者版へのリンク |
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表示名 |
10.1109/TSUSC.2017.2728805 |
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URL |
https://doi.org/10.1109/TSUSC.2017.2728805 |
| DOI |
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関連タイプ |
isVersionOf |
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識別子タイプ |
DOI |
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関連識別子 |
10.1109/TSUSC.2017.2728805 |
| 日本十進分類法 |
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主題Scheme |
NDC |
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主題 |
007 |
| ISSN |
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収録物識別子タイプ |
PISSN |
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収録物識別子 |
2377-3782 |
| 権利 |
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権利情報 |
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
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言語 |
en |
| 著者版フラグ |
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出版タイプ |
AM |
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出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| フォーマット |
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内容記述タイプ |
Other |
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内容記述 |
application/pdf |
IEEETSC_2_4_333_344 (9.6 MB)
