A Better Match for Drivers and Riders: Reinforcement Learning at Lyft

Xabi Azagirre, Akshay Balwally, Guillaume Candeli, Nicholas Chamandy, Benjamin Han +14 more
10/20/2023
cs.LG

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Abstract

To better match drivers to riders in our ridesharing application, we revised Lyft's core matching algorithm. We use a novel online reinforcement learning approach that estimates the future earnings of drivers in real time and use this information to find more efficient matches. This change was the first documented implementation of a ridesharing matching algorithm that can learn and improve in real time. We evaluated the new approach during weeks of switchback experimentation in most Lyft markets, and estimated how it benefited drivers, riders, and the platform. In particular, it enabled our drivers to serve millions of additional riders each year, leading to more than $30 million per year in incremental revenue. Lyft rolled out the algorithm globally in 2021.

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Cite this paper

@article{azagirre2023better,
  title  = {A Better Match for Drivers and Riders: Reinforcement Learning at Lyft},
  author = {Xabi Azagirre and Akshay Balwally and Guillaume Candeli and Nicholas Chamandy and Benjamin Han and Alona King and Hyungjun Lee and Martin Loncaric and Sebastien Martin and Vijay Narasiman and Zhiwei and Qin and Baptiste Richard and Sara Smoot and Sean Taylor and Garrett van Ryzin and Di Wu and Fei Yu and Alex Zamoshchin},
  year   = {2023},
  eprint = {2310.13810},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.13810v2}
}

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