Trial on Extracting Riding Comfort Factors of Electric Bike Considering Temporal Factors by Machine Learning

Abstract

The purpose of this study is to clarify conditions for riding comforts of an electric motorcycle which should be different from those for an engine bike. In the previous report, the authors applied machine learning by decision tree using riders’ subjective evaluation as an objective variable and physical and dynamic states of the bike and the riders as explanatory variables. In the machine learning, however, temporal factors such as the bike’s temporal response to rider’s operation and reaction time of riders from the occurrence of some phenomena till the rider’s perception and utterance. In this report, the authors propose a method of machine learning by decision tree considering such temporal factors, apply it to the riding data and confirm to its effectiveness.