Lyft, Inc.

Jianzhe Luo

Jatin Chopra

Christopher Sholley

Danial Afzal

Garrett Van Ryzin

Chirag Chhagan Chheda

Ido Avigdor Bright

Jimmy Young

Guy-Baptiste Richard De Capele d'Hautpoul

Ilan Lobel

Jeremy Alexander Karp

Philip Gerstoft

Vincent Chih-Jye Chang

Alexander Michael Weinstein

Adam Greenhall

Nihar Chandra Bhupalam

Sebastien Martin

Ryan Chung Leung

Rafah Mujtaba Ali

Neil Rajiv Chainani

The disclosed computer-implemented method may include receiving an indication that a transportation application has been initialized on a computing device and analyzing current dispatch conditions affecting the dispatch of transportation provider devices to the computing device. The method may also include determining, based on the analysis, that the current dispatch conditions allow for an ephemeral ride mode to be offered on the transportation application. The method may further include instructing, based on the determination, the computing device to at least temporarily present the ephemeral ride mode within the transportation application as a selectable option. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for enhanced transportation dispatch

Embodiments provide techniques, including systems and methods, for assignment and/or reassignment of transport requests received within a request matching time period. For example, transport requests received during a request matching time period, as well as requests determined to be eligible during the request matching time period for reassignment, are associated with a location identifier (e.g., geohash) and are pooled at a dynamic transportation matching system in order for a dynamic assignment and/or reassignment that may reduce a metric (e.g., an overall estimated time of arrival (ETA) for requests) associated with the dynamic transportation matching system.

Dynamic optimized reassignment of providers at a geohash level

The present application discloses an improved transportation matching system, and corresponding methods and computer-readable media. According to disclosed embodiments, a transportation matching system receives session information from a requestor computing device in response to one or more session events. The system utilizes the received session information to determine a likelihood that the associated session on the requestor computing device will result in a transportation request. If the determined likelihood is above a threshold amount, the system generates an upfront dispatch that matches the associated session to a provider computing device. The system then reserves the provider computing device and generates a predicted dispatch for the reserved provider computing device.

Session-based transportation dispatch

The disclosed computer-implemented method may include matching transportation requests to transportation providers using a dynamic transportation matching system. Matching transportation requestors which have varying preferences for waiting times and transportation pricing with transportation providers having varying levels of available supply may involve matching methods that satisfy the transportation requestors while maintaining an appropriate level of supply. A method which provides immediate service for transportation requestors that may be willing to pay a premium fare while placing transportation requestors not willing to pay a premium fare and may be willing to wait may provide a balance of supply and demand of transportation providers. Accordingly, immediately matching transportation requestors not willing to wait while placing the transportation requestors willing to wait in a queue may provide the balanced approach to managing transportation provider availability. Other methods, systems, and computer-readable media are disclosed.

Systems and methods for queueing in dynamic transportation networks

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Bellevue, WA, United States of America

Jianzhe Luo