Machine Learning and Visualization of Sudden Braking using Probe Data

Takuya Kawatani, Eisuke Itoh, Sachio Hirokawa, Tsunenori Mine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2019 8th International Congress on Advanced Applied Informatics, IIAI-AAI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages67-72
Number of pages6
ISBN (Electronic)9781728126272
DOIs
Publication statusPublished - Jul 2019
Event8th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2019 - Toyama, Japan
Duration: Jul 7 2019Jul 11 2019

Publication series

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

Conference

Conference8th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2019
CountryJapan
CityToyama
Period7/7/197/11/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Social Sciences (miscellaneous)

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