Lyft, Inc.

Adriel Frederick

Ricky Chachra

Christopher Sholley

Ashivni Shekhawat

Jerome Hong-Phat Thai

Tzu-Hsin Chiao

Shashi Kant Sharma

Chirag Chhagan Chheda

Xabier Azagirre Lekuona

Gerardo Dela Rosa Michicol

Amarnath Pundalika Pai

Jack Chun Zhou

Piyush Garg

Xianzhe Liang

Mayank Gulati

Keshav Puranmalka

Pratik Hirachand Shah

Rebecca Lindsay Blum

Ryan Lok Yan Ma

Jessica Kelly T Talbert

Simon Jack Jenkins

The present disclosure relates to systems, non-transitory computer-readable media, and methods for dynamically matching requestor devices to generate efficient transportation groups that satisfy user-selected arrival deadlines. In particular, in one or more embodiments, the disclosed systems generate timeslots corresponding to a transportation request including pick-up times, arrival deadlines, and transportation values. In response to receiving user selections of timeslots, the disclosed systems can partition requestor devices into sets of requestor devices. Additionally, the disclosed systems can utilize the sets of requestor devices to match compatible requestor devices to both maximize system efficiency and satisfy selected arrival deadlines for each requestor device.

Providing interfaces with scheduled transportation options to intelligently generate transportation groups

Methods and systems for correcting errors in transportation routes are provided. In one embodiment, a method is provided that includes receiving a transportation request that includes at least two locations. A first route prediction may be generated based on a first set of previously-completed routes associated with the at least two locations. A first predictive model may compare the first route prediction with route information associated with a second set of previously-completed routes identified based on the first route prediction. A second route prediction may be generated based on the comparison and may be sent to a mobile device for presentation to a user.

Transportation route error detection and adjustment

This disclosure covers machine-learning methods, non-transitory computer readable media, and systems that generate a multiplier that efficiently and effectively provides on-demand transportation services for a geographic area. The methods, non-transitory computer readable media, and systems dynamically adjust the multiplier with machine learners to maintain a target estimated time of arrival for a provider device to fulfill a request received from a requestor device. In some embodiments, the methods, non-transitory computer readable media, and systems generate a multiplier report comprising a representation of a geographic area and an indication of the multiplier to facilitate inflow and outflow of provider devices within and without the geographic area.

Dynamically generating and updating multipliers for a transportation matching system using machine learning

Growing community of inventors

Berkeley, CA, United States of America

Adriel Frederick