Abstract: The disclosed embodiments provide techniques for assigning drivers to unassigned trips using a predictive model. In one embodiment, a method is disclosed comprising loading heuristic data associated with a trip performed by a vehicle, the heuristic data comprising at least one driver identifier; identifying a plurality of driver identifiers near to the vehicle during the trip, the plurality of driver identifiers based on mobile device data and in-vehicle monitoring data; generating a set of binary comparisons based on the heuristic data; and generating a set of vectors based on the plurality of driver identifiers and the set of binary comparisons.

Abstract: The disclosed embodiments provide techniques for generating a fuel score for a driver or vehicle. In one embodiment, a method is disclosed comprising generating a deviation vector based on recorded vehicle metrics for a given user, wherein the deviation vector comprises differences between recorded non-fuel metrics and expected non-fuel metrics; predicting, using a statistical learning (e.g., linear regression) model, deviation in fuel consumption amount using the deviation vector of non-fuel metrics; and computing a fuel score for the given user based on the deviation in fuel consumption and a current fuel score.