Systems and methods for generating dynamic transit routes
The present embodiments may relate to generating dynamic transit routes based at least in part upon telematics data of users. For instance, a transit analysis (TA) computing device may be configured to: (1) receive telematics data from the plurality of user mobile devices, each user mobile device of the plurality of user mobile devices corresponding to one user identifier of a plurality of user identifiers; (2) build, for each user identifier of the plurality of user identifiers, a transit model; (3) generate, for each user identifier of the plurality of user identifiers, based at least in part upon the transit model associated with the user identifier, one or more transit predictions; and/or (4) generate, based at least in part upon the one or more transit predictions, for each user identifier of the plurality of user identifiers, a dynamic transit route.
1. A transit analytics (TA) computing device comprising:
at least one processor; and
at least one memory device, storing computing instructions that, when executed on the at least one processor, cause the at least one processor to perform operations comprising:
continuously receive telematics data from a mobile device of a user;
build, based at least in part upon the telematics data associated with the user, a transit model associated with the user using a machine learning algorithm, the transit model including one or more transit patterns of the user;
generate, based at least in part upon the transit model associated with the user, a dynamic transit route;
calculate, based at least in part upon the telematics data associated with the user, a potential benefit statistic, the potential benefit statistic including at least one of an amount of fuel cost savings, insurance savings, or environmental pollution reduction for the user if the user were to utilize the dynamic transit route;
generate a notification message including the dynamic transit route and the potential benefit statistic; and
transmit the notification message to the mobile device of the user.
2. The TA computing device of claim 1 , wherein the computing instructions, when executed on the at least one processor, further causes the at least one processor to perform operations comprising:
receive a response message from the mobile device of the user including an indicator that the user accepts or declines the dynamic transit route; and
modify the dynamic transit route based at least in part upon the indicator.
3. The TA computing device of claim 1 , wherein the computing instructions, when executed on the at least one processor, further causes the at least one processor to perform an operation comprising:
transmit the dynamic transit route to a transit vehicle computing device.
4. The TA computing device of claim 3 , wherein the transit vehicle computing device corresponds to an autonomous vehicle donfigured to autonomously travel the dynamic transit route to provide a transit service.
5. The TA computing device of claim 1 , wherein the mobile device comprises a connected vehicle.
6. The TA computing device of claim 1 , wherein generate the dynamic transit route further includes:
determine, based at least in part upon the transit model associated with the user, one or more transit predictions, the one or more transit predictions including a predicted time, a predicted origin location, and a predicted destination location; and
generate, based at least in part upon the one or more transit predictions, the dynamic transit route.
7. A computer-implemented method performed by a transit analytics (TA) computing device being implemented via execution of computing instructions configured to run on at least one processor and stored on at least one memory device, the computer-implemented method comprising:
continuously receiving, by the TA computing device, telematics data from a mobile device of a user;
building, based at least in part upon the telematics data associated with the user, by the TA computing device, a transit model associated with the user using a machine learning algorithm, the transit model including one or more transit patterns of the user;
generating, by the TA computing device, based at least in part upon the transit model associated with the user, a dynamic transit route;
calculating, based at least in part upon the telematics data associated with the user, a potential benefit statistic, the potential benefit statistic including at least one of an amount of fuel cost savings, insurance savings, or environmental pollution reduction for the user if the user were to utilize the dynamic transit route;
generating, by the TA computing device, a notification message including the dynamic transit route and the potential benefit statistic; and
transmitting, by the TA computing device, the notification message to the mobile device of the user.
8. The computer-implemented method of claim 7 , further comprising:
receiving, by the TA computing device, a response message from the mobile device of the user including an indicator that the user accepts or declines the dynamic transit route; and
modifying, by the TA computing device, the dynamic transit route based at least in part upon the indicator.
9. The computer-implemented method of claim 7 , further comprising:
transmitting, by the TA computing device, the dynamic transit route to a transit vehicle computing device.
10. The computer-implemented method of claim 9 , wherein the transit vehicle computimg device corresponds to an autonomous vehicle configured to autonomously travel the dynamic transit route to provide a transit service.
11. The computer-implemented method of claim 7 , wherein the mobile device comprises a connected vehicle.
12. The computer-implemented method of claim 7 , wherein generating the dynamic transit route further includes:
determining, based at least in part upon the transit model associated with the user, one or more transit predictions, the one or more transit predictions including a predicted time, a predicted origin location, and a predicted destination location; and
generating, based at least in part upon the one or more transit predictions, the dynamic transit route.
13. At least one non-transitory computer-readable media, storing computing instructions embodied thereon that, when executed by at least one processor of a transit analytics (TA) computing device, cause the at least one processor to perform operations comprising:
continuously receive telematics data from a mobile device of a user;
build, based at least in part upon the telematics data associated with the user, a transit model associated with the user using a machine learning algorithm, the transit model including one or more transit patterns of the user;
generate, based at least in part upon the transit model associated with the user, a dynamic transit route;
calculate, based at least in part upon the telematics data associated with the user, a potential benefit statistic, the potential benefit statistic including at least one of an amount of fuel cost savings, insurance savings, or environmental pollution reduction for the user if the user were to utilize the dynamic transit route;
generate a notification message including the dynamic transit route and the potential benefit statistic; and
transmit the notification message to the mobile device of the user.
14. The at least one non-transitory computer-readable media of claim 13 , wherein the computing instructions, when executed by the at least one processor of the TA computing device, further cause the at least one processor to perform an operation comprising:
receive a response message from the mobile device of the user including an indicator that the user accepts or declines the dynamic transit route.
15. The at least one non-transitory computer-readable media of claim 14 , wherein the computing instructions, when executed by the at least one processor of the TA computing device, further cause the at least one processor to perform an operation comprising:
modify the dynamic transit route based at least in part upon the indicator.
16. The at least one non-transitory computer-readable media of claim 13 , wherein the computing instructions, when executed by the at least one processor of the TA computing device, further cause the at least one processor to perform an operation comprising:
transmit the dynamic transit route to a transit vehicle computing device.
17. The at least one non-transitory computer-readable media of claim 16 , wherein the transit vehicle computing device corresponds to an autonomous vehicle configured to autonomously travel the dynamic transit route to provide a transit service.
18. The at least one non-transitory computer-readable media of claim 13 , wherein the mobile device comprises a connected vehicle.
19. The at least one non-transitory computer-readable media of claim 13 , wherein generate the dynamic transit route further includes:
determine, based at least in part upon the transit model associated with the user, one or more transit predictions, the one or more transit predictions including a predicted time, a predicted origin location, and a predicted destination location.
20. The at least one non-transitory computer-readable media of claim 19 , wherein generate the dynamic transit route further includes:
generate, based at least in part upon the one or more transit predictions, the dynamic transit route.