The number of accidents involving pedestrians and bicyclists has been reported to be about 1.8 times higher on narrow roads than on arterial roads in Japan. We consider investigating the circumstances under which accidents occur on narrow roads to be an important research task. Statistics from the Tokyo Metropolitan Police Department indicate that the number of traffic accident fatalities in winter is relatively high. We used a Driving Simulator (DS) in order to safely perform sensing on roads that replicate a local city in a heavy snowfall region. Brain activity during driving was measured using a portable functional Near-Infrared Spectroscopy (fNIRS) device. We used a machine-learning algorithm for analyzing time-series datasets to demonstrate differences in brain activity across driving events. We classified subjects into four groups based on the results of questionnaires that assessed their driving characteristics. Experimentally obtained results demonstrated that Root Mean Squared Error (RMSE) changes that represent increased brain activity were greater in winter than in summer for each event. We infer that the winter events had a larger impact on the drivers.
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