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.
Evaluation of driver accelera- tion/deceleration behavior at signalized intersections using vehicle tra- jectory data.Transportation Letters, 15(4):350–362
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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.