Citation

Sato Y, Kawatani T, Mine T. Int. J. Intell. Transp. Syst. Res. 2021; 19(2): 366-377.

Copyright

(Copyright © 2021, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s13177-021-00253-6

PMID

unavailable

Abstract

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.

Language: en