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Solving the Ride-Sharing Productivity Paradox: Priority Dispatch and Optimal Priority Sets.

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Title: Solving the Ride-Sharing Productivity Paradox: Priority Dispatch and Optimal Priority Sets.
Authors: Krishnan, Varun (AUTHOR), Iglesias, Ramon (AUTHOR), Martin, Sebastien (AUTHOR), Wang, Su (AUTHOR), Pattabhiraman, Varun (AUTHOR), Van Ryzin, Garrett (AUTHOR)
Source: INFORMS Journal on Applied Analytics. Sep/Oct2022, Vol. 52 Issue 5, p433-445. 13p.
Subjects: Prices, Intuition, Marketing models, Paradox, Equilibrium, Ridesharing services
Abstract: Ride-sharing platforms face a "productivity paradox," whereby any efficiency gained through improved dispatch or pricing strategies will not benefit drivers or riders. We show that this is a limit of the traditional ride-hailing model and a consequence of the Hall-Horton driver equilibrium earning hypothesis. In response to this challenge, Lyft introduced Priority Mode (PM), which allows drivers to concentrate their work during specific prioritized hours. We prove that PM solves the productivity paradox. As a result, the average driver earnings increase, and the platform and the riders also benefit. Implementing PM requires significant changes to the platform's dispatch and pricing policy but most importantly requires careful control of the number of drivers that can be offered the opportunity to be prioritized at any given time. In this paper, we introduce a queuing setting to model the market dynamics of PM and illustrate the challenges of this control problem. We then leverage this intuition to build a real-time priority admission control system that can balance the number of drivers offered priority and achieve the desired productivity increase. Lyft has successfully rolled out PM throughout North America, and drivers have completed hundreds of thousands of driving hours thus far. It has generated tens of millions of dollars of value that the drivers, the riders, and Lyft have shared, with the potential to generate much more when rolled out in all markets. Finally, our internal driver surveys reveal that it has been well received by drivers. [ABSTRACT FROM AUTHOR]
Copyright of INFORMS Journal on Applied Analytics is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Solving the Ride-Sharing Productivity Paradox: Priority Dispatch and Optimal Priority Sets.
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  Data: <searchLink fieldCode="JN" term="%22INFORMS+Journal+on+Applied+Analytics%22">INFORMS Journal on Applied Analytics</searchLink>. Sep/Oct2022, Vol. 52 Issue 5, p433-445. 13p.
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  Data: Ride-sharing platforms face a "productivity paradox," whereby any efficiency gained through improved dispatch or pricing strategies will not benefit drivers or riders. We show that this is a limit of the traditional ride-hailing model and a consequence of the Hall-Horton driver equilibrium earning hypothesis. In response to this challenge, Lyft introduced Priority Mode (PM), which allows drivers to concentrate their work during specific prioritized hours. We prove that PM solves the productivity paradox. As a result, the average driver earnings increase, and the platform and the riders also benefit. Implementing PM requires significant changes to the platform's dispatch and pricing policy but most importantly requires careful control of the number of drivers that can be offered the opportunity to be prioritized at any given time. In this paper, we introduce a queuing setting to model the market dynamics of PM and illustrate the challenges of this control problem. We then leverage this intuition to build a real-time priority admission control system that can balance the number of drivers offered priority and achieve the desired productivity increase. Lyft has successfully rolled out PM throughout North America, and drivers have completed hundreds of thousands of driving hours thus far. It has generated tens of millions of dollars of value that the drivers, the riders, and Lyft have shared, with the potential to generate much more when rolled out in all markets. Finally, our internal driver surveys reveal that it has been well received by drivers. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of INFORMS Journal on Applied Analytics is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=182962484
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              Text: Sep/Oct2022
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