PREDICTING A DRIVER IDENTITY FOR UNASSIGNED DRIVING TIME
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.
1 . A method 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.
2 . The method of claim 1 , further comprising:
classifying the set of vectors to obtain a set of predictions;
selecting a prediction from the set of predictions; and
assigning a driver identifier associated with the prediction to the trip.
3 . The method of claim 2 , wherein classifying the set of vectors comprises classifying the set of vectors using a predictive model, the predictive model generating a binary classification for each vector in the set of vectors.
4 . The method of claim 1 , further comprising:
assigning a label to each vector in the set of vectors to generate a set of labeled vectors; and
training a predictive model using the set of labeled vectors, the predictive model generating a binary classification for each vector in the set of vectors.
5 . The method of claim 1 , wherein the heuristic data comprises driver identifiers associated with one or more of a previous trip, a next trip, and an inspection report.
6 . The method of claim 5 , wherein generating a set of binary comparisons comprises comparing a candidate driver identifier to the driver identifiers in the heuristic data and to a matching driver identifier in the plurality of driver identifiers.
7 . The method of claim 1 , wherein identifying a plurality of driver identifiers near to the vehicle during the trip comprises analyzing position and time data associated with a plurality of mobile device pings and a plurality of in-vehicle monitoring device pings and generating a feature vector based on the analysis.
8 . A non-transitory computer-readable storage medium for tangibly storing computer program instructions capable of being executed by a computer processor, the computer program instructions defining steps of:
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.
9 . The non-transitory computer-readable storage medium of claim 8 , the steps further comprising:
classifying the set of vectors to obtain a set of predictions;
selecting a prediction from the set of predictions; and
assigning a driver identifier associated with the prediction to the trip.
10 . The non-transitory computer-readable storage medium of claim 9 , wherein classifying the set of vectors comprises classifying the set of vectors using a predictive model, the predictive model generating a binary classification for each vector in the set of vectors.
11 . The non-transitory computer-readable storage medium of claim 8 , the steps further comprising:
assigning a label to each vector in the set of vectors to generate a set of labeled vectors; and
training a predictive model using the set of labeled vectors, the predictive model generating a binary classification for each vector in the set of vectors.
12 . The non-transitory computer-readable storage medium of claim 8 , wherein the heuristic data comprises driver identifiers associated with one or more of a previous trip, a next trip, and an inspection report.
13 . The non-transitory computer-readable storage medium of claim 12 , wherein generating a set of binary comparisons comprises comparing a candidate driver identifier to the driver identifiers in the heuristic data and to a matching driver identifier in the plurality of driver identifiers.
14 . The non-transitory computer-readable storage medium of claim 8 , wherein identifying a plurality of driver identifiers near to the vehicle during the trip comprises analyzing position and time data associated with a plurality of mobile device pings and a plurality of in-vehicle monitoring device pings and generating a feature vector based on the analysis.
15 . A device comprising:
a processor configured to:
load heuristic data associated with a trip performed by a vehicle, the heuristic data comprising at least one driver identifier;
identify 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;
generate a set of binary comparisons based on the heuristic data; and
generate a set of vectors based on the plurality of driver identifiers and the set of binary comparisons.
16 . The device of claim 15 , the processor further configured to:
classify the set of vectors to obtain a set of predictions;
select a prediction from the set of predictions; and
assign a driver identifier associated with the prediction to the trip.
17 . The device of claim 15 , the processor further configured to:
assign a label to each vector in the set of vectors to generate a set of labeled vectors; and
train a predictive model using the set of labeled vectors, the predictive model generating a binary classification for each vector in the set of vectors.
18 . The device of claim 15 , wherein the heuristic data comprises driver identifiers associated with one or more of a previous trip, a next trip, and an inspection report.
19 . The device of claim 18 , wherein generating a set of binary comparisons comprises comparing a candidate driver identifier to the driver identifiers in the heuristic data and to a matching driver identifier in the plurality of driver identifiers.
20 . The device of claim 15 , wherein identifying a plurality of driver identifiers near to the vehicle during the trip comprises analyzing position and time data associated with a plurality of mobile device pings and a plurality of in-vehicle monitoring device pings and generating a feature vector based on the analysis.