Blueowl, LLC

Quanata, LLC

Mary Piontkowski

Kenneth Jason Sanchez

Theobolt N Leung

Bennett Smith

A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: building, using a machine learning algorithm, a transit model based at least in part upon telematics data associated with a user; generating, based at least in part upon the transit model, a dynamic transit route for the user; calculating, based at least in part upon the telematics data associated with the user, a potential benefit comprising an amount of fuel cost savings for the user, reduced travel time for the user, insurance savings, or environmental pollution reduction for the user; generating a notification comprising the dynamic transit route and the potential benefit; and transmitting the notification to a mobile device of the user. Other embodiments are described.

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.

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San Francisco, CA, United States of America

Mary Piontkowski