Lyft, Inc.

Ehud Milo

Omar Khalid

Robert Anthony Farmer

Davide Crapis

Saurabh Bajaj

Amy J Kim

Richard Zhao

Eran Davidov

Jacky Yi Han Lu

Akash Gaurav Shah

Gaurav Gupta

The present application discloses an improved transportation matching system, and corresponding methods and computer-readable media. According to disclosed embodiments, a transportation matching system trains a predictive request model to generate a metric predicted to trigger an increase in transportation provider activity within the geographic area for a given time period. Furthermore, the system determines a predicted gap between expected request activity and expected transportation provider activity for the geographic area during a future time period, utilizes the predictive request model and the predicted gap to generate a metric for the geographic area, and generates an interactive map associated with a customized schedule for the geographic area and the future time period based on the generated metric.

Using a predictive request model to optimize provider resources

This disclosure describes a transportation matching system that manages the allocation of transportation providers by training and utilizing multiple machine-learning models to identify, allocate, and serve specific transportation providers with customized opportunities to relocate the transportation providers between geocoded areas in a geocoded region. For instance, the transportation matching system trains and utilizes an incremental provider model, a provider allocation model, and personalized provider behavioral models as well as a customized provider interface generator to satisfy anticipated transportation requests and improve transportation matching within a geocoded region.

Using geocoded provider models to improve efficiency of a transportation matching system

Efficiency of a transportation matching system using geocoded provider models

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San Mateo, CA, United States of America

Ehud Milo