Lyft, Inc.

Keshav Puranmalka

Dor Levi

Mayank Gulati

Krishna Kumar Selvam

Adriel Frederick

Molly Angelica Ingles Lorenzo

Orit Balicer Tsur

Vincent Chih-jye Chang

Michael Kae-Uei Chang

David Florian Kayode Ikuye

Jessica Kelly T Talbert

Simon Jack Jenkins

Pratik Hirachand Shah

Rebecca Lindsay Blum

Dan Xiaoyu Yu

Conglei Zhang

Ryan Lok Yan Ma

The present disclosure relates to systems, non-transitory computer readable media, and methods that provide graphical user interfaces comprising future transportation options with varying time windows at different transportation values and dynamically analyze the time windows to identify provider devices to fulfill transportation requests based on provider device efficiency metrics. For instance, the disclosed systems can delay selection of a provider device within a future time window utilizing a dynamic threshold provider device efficiency metric. For instance, the disclosed systems can analyze historical distributions of provider devices to generate a transition probability matrix that is utilized to analyze current provider devices and determine a threshold provider device efficiency metric that reflects the likelihood of identifying more efficient matches in the future. The disclosed systems can compare the determined threshold to anticipated efficiency metrics for individual provider devices to generate matches for digital transportation requests.

Utilizing provider device efficiency metrics to select a provider device for a future time window

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

The disclosed computer-implemented method may include matching transportation requests. By collecting and batching match requests over an extended period, a dynamic transportation matching system may identify more efficient matches (e.g., may match transportation requests with greater overlaps). In addition, by dynamically setting and/or extending the upper bound of time that a transportation request may remain batched with other transportation requests, the dynamic transportation matching system may account for contextual information thereby situationally improving matching efficiencies made possible with higher upper bounds while avoiding requestor dissatisfaction, lost conversions, or other inefficiencies that may result from upper bounds that are too high. Furthermore, by providing a notification interface for a transportation requestor device that notifies the requestor when to be ready, a transportation matching application may provide a positive requestor experience while allowing for longer match windows. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for matching transportation requests over extended batching windows

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Seattle, WA, United States of America

Keshav Puranmalka