First mile and last mile ride sharing method and system

1. A computer-implemented method of facilitating a first-mile/last-mile transfer of a vehicle, the method comprising:

analyzing a first route using a first trained machine learning model and a second route using a second trained machine learning model to determine, respectively, an incentive to a first user and a cost to a second user;

wherein the first trained machine learning model is trained using at least one of a historical data set of ride sharing data indicating at least one of a price that was paid, a distance traveled, a time of day, a time to arrival, a traffic congestion factor, or profile information about a vehicle operator, and

wherein the second trained machine learning model is trained using at least one of a detour in time, a delay in miles, a time of day, a traffic congestion factor, or profile information about a ride share passenger;

causing a first message including the incentive to be displayed to the first user via an electronic device of the first user; and

causing a second message including the cost to be displayed to the second user via an electronic device of the second user.

2. The computer-implemented method of claim 1 , further comprising:

receiving an acknowledgement of the first message from the first user and an acknowledgement of the second message from the second user; and

causing a respective confirmation to be displayed to the first user and to the second user.

3. The computer-implemented method of claim 1 , further comprising:

receiving telematics information from the vehicle, and

based on the telematics information, one or both of (i) providing the incentive to the first user, and (ii) collecting the cost from the second user.

4. The computer-implemented method of claim 1 , further comprising:

receiving telematics information from the vehicle,

wherein at least one of the first trained machine learning model or the second trained machine learning model is further trained using the telematics information.

5. The computer-implemented method of claim 1 , wherein determining a cost to the second user includes identifying a plurality of potential users likely to accept the cost with respect to the vehicle.

6. The computer-implemented method of claim 1 , further comprising:

identifying combinable routes.

7. The computer-implemented method of claim 1 , further comprising:

minimizing a distance between the first user and the second user using a graph theoretic algorithm.

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

determining whether the second user is along a path of the first user.

9. A computing system comprising:

one or more processors; and

one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to:

analyze a first route using a first trained machine learning model and a second route using a second trained machine learning model to determine, respectively, an incentive to a first user and a cost to a second user,

wherein the first trained machine learning model is trained using at least one of a historical data set of ride sharing data indicating at least one of a price that was paid, a distance traveled, a time of day, a time to arrival, a traffic congestion factor, or profile information about a vehicle operator, and

wherein the second trained machine learning model is trained using at least one of a detour in time, a delay in miles, a time of day, a traffic congestion factor, or profile information about a ride share passenger;

cause a first message including the incentive to be displayed to the first user via an electronic device of the first user; and

cause a second message including the cost to be displayed to the second user via an electronic device of the second user.

10. The computing system of claim 9 , the one or more memories storing further instructions that, when executed by the one or more processors, cause the computing system to:

receive an acknowledgement of the first message from the first user and an acknowledgement of the second message from the second user; and

cause a respective confirmation to be displayed to the first user and to the second user.

11. The computing system of claim 9 , the one or more memories storing further instructions that, when executed by the one or more processors, cause the computing system to:

receive telematics information from the vehicle, and

based on the telematics information, one or both of (i) provide the incentive to the first user, and (ii) collect the cost from the second user.

12. The computing system of claim 9 , the one or more memories storing further instructions that, when executed by the one or more processors, cause the computing system to:

receive telematics information from the vehicle,

wherein at least one of the first trained machine learning model or the second trained machine learning model is further trained using the telematics information.

13. The computing system of claim 9 , wherein determining a cost to the second user includes identifying a plurality of potential users likely to accept the cost with respect to the vehicle.

14. The computing system of claim 9 , the one or more memories storing further instructions that, when executed by the one or more processors, cause the computing system to:

identify combinable routes.

15. The computing system of claim 9 , the one or more memories storing further instructions that, when executed by the one or more processors, cause the computing system to:

minimize a distance between the first user and the second user using a graph theoretic algorithm.

16. The computing system of claim 9 , the one or more memories storing further instructions that, when executed by the one or more processors, cause the computing system to:

determine whether the second user is along the path of the first user.

17. A non-transitory computer readable medium containing program instructions that when executed, cause a computer system to:

analyze a first route using a first trained machine learning model and a second route using a second trained machine learning model to determine, respectively, an incentive to a first user and a cost to a second user,

wherein the first trained machine learning model is trained using at least one of a historical data set of ride sharing data, indicating at least one of a price that was paid, a distance traveled, a time of day, a time to arrival, a traffic congestion factor, or profile information about a vehicle operator, and

wherein the second trained machine learning model is trained using at least one of a detour in time, a delay in miles, a time of day, a traffic congestion factor, or profile information about a ride share passenger;

cause a first message including the incentive to be displayed to the first user via an electronic device of the first user; and

cause a second message including the cost to be displayed to the second user via an electronic device of the second user.

18. The non-transitory computer readable medium of claim 17 containing further program instructions that when executed, cause a computer system to:

receive telematics information from the vehicle, and

based on the telematics information, one or both of (i) provide the incentive to the first user, and (ii) collect the cost from the second user.

19. The non-transitory computer readable medium of claim 17 containing further program instructions that when executed, cause a computer system to:

minimize a distance between the first user and the second user using a graph theoretic algorithm.

20. The non-transitory computer readable medium of claim 17 containing further program instructions that when executed, cause a computer system to:

determine whether the second user is along the path of the first user.