Machine Learning and Visualization of Sudden Braking using Probe Data

Takuya Kawatani, Eisuke Itoh, Sachio Hirokawa, Tsunenori Mine

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

2 被引用数 (Scopus)

抄録

This paper presents a novel mining and visualizing tool that detects features to estimate sudden braking. The tool uses a machine learning and feature selection method to find the features exhaustively from combinations of the features which include not only vehicle-related factors, but also outer circumstances or temporal factors. The tool also obtains the locations inferred by the features detected. A normal way would first search for locations where sudden braking behavior frequently occurred, but it is not always true that the occurrence probability of sudden braking at the locations is high. On the other hand, our tool finds the locations related to sudden braking with high probability, more than 98%. Through the visualizing process, the features can be used as clues to find new factors which affect sudden braking.

本文言語英語
ホスト出版物のタイトルProceedings - 2019 8th International Congress on Advanced Applied Informatics, IIAI-AAI 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ67-72
ページ数6
ISBN(電子版)9781728126272
DOI
出版ステータス出版済み - 7 2019
イベント8th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2019 - Toyama, 日本
継続期間: 7 7 20197 11 2019

出版物シリーズ

名前Proceedings - 2019 8th International Congress on Advanced Applied Informatics, IIAI-AAI 2019

会議

会議8th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2019
国/地域日本
CityToyama
Period7/7/197/11/19

All Science Journal Classification (ASJC) codes

  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • 情報システム
  • 情報システムおよび情報管理
  • 社会科学(その他)

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