Lyft, Inc.

John Torres Fremlin

Garrett Van Ryzin

Mayank Gulati

Demitri Nava

Aidan Church

Mengqi Niu

Matthew Lawrence Green

Shashi Kant Sharma

Daniel Allen Sullivan

Helen Wai-Quen Bentley

Rachel Marie Wasko

Yilei Li

Man Geen Harold Li

Xabier Azagirre Lekuona

Wilson James Turner

Sebastien Jean Francois Martin

Alexander Michael Weinstein

Kevin Stewart

Michael Sasha Frumin

David Halsted Riege

Akshay Ramesh Patil

Dorian Anthony Goldman

Yunshu Liu

The present application discloses systems, methods, and computer-readable media that utilize computer-implemented models to monitor surplus provider device slots and transmit digital invitations to provider devices to join an online mode by dynamically leveraging previous transportation provider device requests within a threshold rejection time span. For instance, the disclosed systems can identify rejected provider devices that requested to be in an online mode of a transportation matching application within a geographical area. Then the disclosed systems can monitor device activity over computer networks and determine a number of surplus provider device slots corresponding to the geographical region. Furthermore, the disclosed systems can select provider devices (that were rejected within a threshold rejection time span) from the previously rejected requests. Furthermore, the disclosed systems can transmit digital invitations to join the online mode to selected provider devices with a guaranteed online mode slot for a threshold reservation time.

Dynamically transmitting online mode invitations to provider devices in response to detected changes in provider device efficiency

The disclosed systems can regulate access to an online mode for a dynamic transportation matching system. For example, based on a provider efficiency parameter associated with the dynamic transportation matching system, the disclosed systems can prevent a transportation provider device from switching to the online mode within a geographic area. In addition, the disclosed systems can detect a pattern of behavior and, based on a comparison between the pattern of behavior and a behavioral threshold, cause a transportation provider device to switch from the online mode to an offline mode. Further, the disclosed systems can provide a map interface that indicates where a transportation provider device can switch from the offline mode to the online mode. Additionally, the disclosed systems can determine priorities associated with transportation provider devices and, based on the prioritization, selectively allow the transportation provider devices to switch from the offline mode to the online mode.

Dynamically adjusting transportation provider pool size

This disclosure describes a pickup location determination system that determines a pickup location for a received transportation request by filtering out door points based on various factors and by utilizing a pickup location model to select a pickup location from the filtered door points. For example, the disclosed systems generate door points relative to intersection points within a request radius of a request location associated with a received transportation request. The disclosed systems generate potential pickup locations by filtering out door points that are impractical and/or inefficient based on proximity to other door points, locations relative to venues, locations relative to side streets, and/or locations between parallel road segments (e.g., medians). The disclosed systems further utilize a pickup location model to select a pickup location from the potential pickup locations.

Determining efficient pickup locations for transportation requests utilizing a pickup location model

The present application discloses systems, methods, and computer-readable media that utilize computing devices to model multi-outcome transportation-value metrics that account for spatio-temporal trajectories across locations, times and other contextual features, and then utilize computer networks to dispatch provider devices to locations based on the multi-outcome transportation-value metrics. Moreover, the disclosed systems can utilize these multi-outcome transportation-value metrics and/or other models to manage and utilize dynamic transportation dispatch modes to more efficiently align provider devices and requestor devices across computer networks. For instance, the disclosed system can dispatch a provider device based on a discounted multi-outcome transportation-value metric. Furthermore, the disclosed system can dynamically determine prioritized dispatch mode provider device slots, fill provider device slots based on performance metrics, and then select provider device from between a prioritized transportation dispatch mode and another transportation dispatch mode based on prioritization metrics corresponding to a transportation request.

Dispatching provider devices utilizing multi-outcome transportation-value metrics and dynamic provider device modes

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and utilizing provider device balance graphs reflecting device utilization ratio between provider devices and requester devices. In particular, in one or more embodiments, the disclosed systems determine and utilize probabilities to generate and provide provider device balance graphs based on probability distributions for device utilization ratios. The disclosed systems can apply various scaling models to address seasonality, special events, and/or differences between regions. The disclosed systems can also apply thresholds to generate device incentive graphs that more efficiently deploy provider devices across a transportation matching system.

Generating and communicating device balance graphical representations for a dynamic transportation system

Growing community of inventors

New York, NY, United States of America

John Torres Fremlin