Road traffic accidents cause a great loss of lives and property damage. Reliable accident prediction and proactive prevention are undoubtedly of great benefit and necessity. This study focuses on the risk assessment and prediction of traffic accidents associated with vehicle conflicts, using machine learning and surrogate indicators to achieve vehicle-level risk rating and prediction based on instantaneous driving behaviours. Accident events are generally unexpected and occur rarely. Pre-crash risk assessment by surrogate indicators is an effective way to identify risk levels, and thus boost crash prediction. Herein, the concept of Key Risk Indicator (KRI) is proposed, which assesses risk exposures using hybrid indicators. To evaluate the ...