Influence of Weather Features in Determining Sudden Braking

Yuta Sato, Takuya Kawatani, Tsunenori Mine

Research output: Contribution to journalArticlepeer-review


Understanding conditions and situations causing abnormal driving behaviors like sudden braking or sudden acceleration is important for preventing traffic accidents. Previous studies have used probe vehicle data to detect risky situations where sudden braking frequently occurred. However, they have mainly focused on location and vehicle-related factors. In this paper, we build models which discriminate sudden braking using a machine learning method. The models use weather-related information as well as probe data. To investigate how weather-related factors help to determine sudden braking, we conducted extensive experiments using probe data obtained from dashboard cameras and two types of weather-related information obtained from meteorological observatories (MO) and AMeDAS. Experimental results illustrate that using weather-related information improves performance in determining sudden braking and that the temporally and spatially denser characteristics of weather-related factors from AMeDAS help to compensate for insufficiencies in the model with MO data.

Original languageEnglish
Pages (from-to)366-377
Number of pages12
JournalInternational Journal of Intelligent Transportation Systems Research
Issue number2
Publication statusPublished - Jun 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Neuroscience(all)
  • Information Systems
  • Automotive Engineering
  • Aerospace Engineering
  • Computer Science Applications
  • Applied Mathematics


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