Systems and methods for modeling telematics, positioning, and environmental data

1. A modeling computing device comprising at least one processor in communication with at least one memory device, the at least one processor configured to:

retrieve, from the at least one memory device, historical data associated with a plurality of users, wherein the historical data includes historical liability amount data and historical user data, and wherein the historical user data includes at least one of historical personal information, historical vehicle telematics data, and historical environmental data;

create a first plurality of training datasets including the historical liability amount data and the historical user data;

build, using one or more machine learning programs, a model based upon the first plurality of training datasets;

store the model in the at least one memory device;

collect current user data of a candidate user associated with a vehicle, wherein the current user data includes current personal information, current vehicle telematics data, and current environmental data, wherein the current vehicle telematics data and the current environmental data are gathered by one or more sensors during operation of the vehicle, and wherein the one or more sensors include a GPS device, an accelerometer, a gyroscope, a camera, and a sensor installed within the vehicle or located remotely from the vehicle;

create a second plurality of training datasets by updating the first plurality of training datasets to include the collected current user data;

update the model by applying the second plurality of training datasets to the model; and

execute the updated model to determine a current liability amount for the candidate user.

2. The modeling computing device of claim 1 , wherein the model is configured to predict a liability amount based upon the historical user data, wherein the liability amount includes a liability limit, a liability loss amount, and a claim amount, and wherein the at least one processor is further configured to:

apply the updated model to further collected current user data to determine a further current liability amount for the candidate user; and

further update the updated model based upon the determined further current liability amount.

3. The modeling computing device of claim 1 , wherein the at least one processor is further configured to:

transmit the determined current liability amount to at least one third party, wherein the at least one third party includes an insurance company; and

generate an insurance policy for the insurance company based upon the determined current liability amount.

4. The modeling computing device of claim 1 , wherein the one or more machine learning programs include machine learning, artificial intelligence, or a combination thereof, and wherein the at least one processor is further configured to build the first plurality of training datasets using the historical data associated with the plurality of users, the historical data including historical insurance data.

5. The modeling computing device of claim 1 , wherein:

the historical vehicle telematics data comprises one or more historical measurements collected during historical operation of the vehicle including historical velocity, acceleration, direction, and driver behavior characteristics; and

the current vehicle telematics data comprises one or more current measurements collected during current operation of the vehicle including current velocity, acceleration, direction, and driver behavior characteristics.

6. The modeling computing device of claim 1 , wherein

the historical environmental data includes at least one of past traffic data and past pedestrian data; and

the current environmental data includes at least one of current traffic data and current pedestrian data.

7. A computer-implemented method implemented by a modeling computing device including at least one processor in communication with at least one memory device, the computer-implemented method comprising:

retrieving, from the at least one memory device, historical data associated with a plurality of users, wherein the historical data includes historical liability amount data and historical user data, and wherein the historical user data includes at least one of historical personal information, historical vehicle telematics data, and historical environmental data;

creating, by the at least one processor, a first plurality of training datasets including the historical liability amount data and the historical user data;

building, by the at least one processor using one or more machine learning programs, a model based upon the first plurality of training datasets;

storing the model in the at least one memory device;

collecting current user data of a candidate user associated with a vehicle, wherein the current user data includes current personal information, current vehicle telematics data, and current environmental data, wherein the current vehicle telematics data and the current environmental data are gathered by one or more sensors during operation of the vehicle, and wherein the one or more sensors include a GPS device, an accelerometer, a gyroscope, a camera, and a sensor installed within the vehicle or located remotely from the vehicle;

creating, by the at least one processor, a second plurality of training datasets by updating the first plurality of training datasets to include the collected current user data;

updating the model by applying the second plurality of training datasets to the model; and

executing the updated model to determine a current liability amount for the candidate user.

8. The computer-implemented method of claim 7 , wherein the model is configured to predict a liability amount based upon the historical user data, wherein the liability amount includes a liability limit, a liability loss amount, and a claim amount, and wherein the computer-implemented method further comprises:

applying the updated model to further collected current user data to determine a further current liability amount for the candidate user; and

further updating the updated model based upon the determined further current liability amount.

9. The computer-implemented method of claim 7 further comprising:

transmitting the determined current liability amount to at least one third party, wherein the at least one third party includes an insurance company; and

generating an insurance policy for the insurance company based upon the determined current liability amount.

10. The computer-implemented method of claim 7 wherein the one or more machine learning programs include using machine learning, artificial intelligence, or a combination thereof, and wherein the method further comprises building the first plurality of training datasets using the historical data associated with the plurality of users, the historical data including historical insurance data.

11. The computer-implemented method of claim 7 , wherein:

the historical vehicle telematics data comprises one or more historical measurements collected during historical operation of the vehicle including velocity, acceleration, direction, and driver behavior characteristics; and

the current vehicle telematics data comprises one or more current measurements collected during current operation of the vehicle including current velocity, acceleration, direction, and driver behavior characteristics.

12. The computer-implemented method of claim 7 , wherein:

the historical environmental data includes at least one of past traffic data and past pedestrian data; and

the current environmental data includes at least one of current traffic data and current pedestrian data.

13. At least one non-transitory computer-readable medium having computer-executable instructions embodied thereon, wherein when executed by a modeling computing device including at least one processor in communication with at least one memory device, the computer-executable instructions cause the at least one processor to:

retrieve, from the at least one memory device, historical data associated with a plurality of users, wherein the historical data includes historical liability amount data and historical user data, and wherein the historical user data includes at least one of historical personal information, historical vehicle telematics data, and historical environmental data;

create a first plurality of training datasets including the historical liability amount data and the historical user data;

build, using one or more machine learning programs, a model based upon the first plurality of training datasets;

store the model in the at least one memory device;

collect current user data of a candidate user associated with a vehicle, wherein the current user data includes current personal information, current vehicle telematics data, and current environmental data, wherein the current vehicle telematics data and the current environmental data are gathered by one or more sensors during operation of the vehicle, and wherein the one or more sensors include a GPS device, an accelerometer, a gyroscope, a camera, and a sensor installed within the vehicle or located remotely from the vehicle;

create a second plurality of training datasets by updating the first plurality of training datasets to include the collected current user data;

update the model by applying the second plurality of training datasets to the model; and

execute the updated model to determine a current liability amount for the candidate user.

14. The at least one non-transitory computer-readable medium of claim 13 , wherein the computer-executable instructions further cause the at least one processor to:

apply the updated model to further collected current user data to determine a further current liability amount for the candidate user, wherein the further current liability amount includes a liability limit, a liability loss amount, and a claim amount; and

update the model based upon the determined further current liability amount.

15. The at least one non-transitory computer-readable medium of claim 13 , wherein the computer-executable instructions further cause the at least one processor to:

transmit the determined current liability amount to at least one third party, wherein the at least one third party includes an insurance company; and

generate an insurance policy for the insurance company based upon the determined current liability amount.

16. The at least one non-transitory computer-readable medium of claim 13 , wherein the one or more machine learning programs include machine learning, artificial intelligence, or a combination thereof, and wherein the computer-executable instructions further cause the at least one processor to build the first plurality of training datasets using the historical data associated with the plurality of users, the historical data including historical insurance data.

17. The at least one non-transitory computer-readable medium of claim 13 , wherein:

the historical vehicle telematics data comprises one or more historical measurements collected during historical operation of the vehicle including historical velocity, acceleration, direction, and driver behavior characteristics; and

the current vehicle telematics data comprises one or more current measurements collected during current operation of the vehicle including current velocity, acceleration, direction, and driver behavior characteristics.