A deep learning model called MotoTimePressure predicts time pressure levels in powered two-wheeler riders at 91.53% accuracy using 63 kinematic and behavioral features, and its predictions improve downstream collision risk models.
Analysis of factors associated with exceeding lawful speed traffic violations in indian metropolitan city.Journal of Transportation Safety & Security, 13(2):206–222
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Predicting Time Pressure of Powered Two-Wheeler Riders for Proactive Safety Interventions
A deep learning model called MotoTimePressure predicts time pressure levels in powered two-wheeler riders at 91.53% accuracy using 63 kinematic and behavioral features, and its predictions improve downstream collision risk models.