Abstract: Systems and methods for predicting collision probabilities are provided. The methods involve operating at least one processor to: retrieve vehicle data originating from a telematics device installed in a vehicle, the vehicle data including location data and a plurality of safety exception events performed by the vehicle, the plurality of safety exception events including a plurality of exception event types; identify a plurality of road network edges traveled by the vehicle based on the location data; determine an aggregated area collision rate based on the plurality of road network edges; determine a plurality of exception rates based on the vehicle data, each exception rate representing a normalized rate of occurrence of one of the exception event types; and determine a collision probability using at least one machine learning model on the plurality of exception rates and the aggregated area collision rate, the collision probability representing a risk of collision.

Abstract: Systems and methods for generating vehicle safety scores and vehicle collision probabilities are provided. The methods involve operating at least one processor to: retrieve vehicle data originating from a telematics device installed in a vehicle, the vehicle data including a plurality of safety exception events performed by the vehicle; determine a plurality of exception rates based on the vehicle data, each exception rate representing a normalized rate of occurrence of one of the exception event types; determine plurality of collision sub-probabilities using a plurality of collision probability models and the plurality of exception rates, each collision probability model associated with one of the exception event types and operable to predict one of the collision sub-probabilities based on one of the exception rates of the associated exception event type; and determine a collision probability for the vehicle based on the plurality of collision sub-probabilities.