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- Noguchi Naoki
- Graduate School of Engineering Science, Osaka University
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- Inuiguchi Masahiro
- Graduate School of Engineering Science, Osaka University
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- Hayashi Naoki
- Graduate School of Engineering Science, Osaka University
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- Seki Hirosato
- Graduate School of Engineering Science, Osaka University
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- Suyama Takahito
- Traffic Planning Division, Hyogo Prefectural Police
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- Hasegawa Jun
- Traffic Planning Division, Hyogo Prefectural Police
Bibliographic Information
- Other Title
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- 交通事故データの解析による重大事故の要因探索
- コウツウ ジコ データ ノ カイセキ ニ ヨル ジュウダイ ジコ ノ ヨウイン タンサク
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Abstract
<p>Although the number of traffic accidents is decreasing year by year owing to the safety per- formance improvement of automobiles, traffic accidents are still a major problem in our day-to-day life. In this study, aiming to prevent serious accidents, we analyze the collection of traffic accidents data by several data mining techniques for extracting potent factors and features of serious accidents. The real traffic accident data are preprocessed to be useful for the data analysis. The potent factors and features of serious accidents are investigated in the data by fuzzy reasoning models, SVM, random forest, and rough set analysis.</p>
Journal
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- Proceedings of the Fuzzy System Symposium
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Proceedings of the Fuzzy System Symposium 37 (0), 675-680, 2021
Japan Society for Fuzzy Theory and Intelligent Informatics
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Details 詳細情報について
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- CRID
- 1390290802315574016
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- NII Article ID
- 130008143652
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- NII Book ID
- AA12165648
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- ISSN
- 18820212
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- NDL BIB ID
- 031715072
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- CiNii Articles
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- Abstract License Flag
- Disallowed