Learn to Earn: Enabling Coordination within a Ride Hailing Fleet
- URL: http://arxiv.org/abs/2006.10904v2
- Date: Thu, 16 Jul 2020 17:07:58 GMT
- Title: Learn to Earn: Enabling Coordination within a Ride Hailing Fleet
- Authors: Harshal A. Chaudhari, John W. Byers and Evimaria Terzi
- Abstract summary: We study the problem of optimizing social welfare objectives on multi sided ride hailing platforms such as Uber, Lyft, etc.
An ideal solution aims to minimize the response time for each hyper local passenger ride request, while simultaneously maintaining high demand satisfaction and supply utilization across the entire city.
- Score: 5.016829322655594
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The problem of optimizing social welfare objectives on multi sided ride
hailing platforms such as Uber, Lyft, etc., is challenging, due to misalignment
of objectives between drivers, passengers, and the platform itself. An ideal
solution aims to minimize the response time for each hyper local passenger ride
request, while simultaneously maintaining high demand satisfaction and supply
utilization across the entire city. Economists tend to rely on dynamic pricing
mechanisms that stifle price sensitive excess demand and resolve the supply
demand imbalances emerging in specific neighborhoods. In contrast, computer
scientists primarily view it as a demand prediction problem with the goal of
preemptively repositioning supply to such neighborhoods using black box
coordinated multi agent deep reinforcement learning based approaches. Here, we
introduce explainability in the existing supply repositioning approaches by
establishing the need for coordination between the drivers at specific
locations and times. Explicit need based coordination allows our framework to
use a simpler non deep reinforcement learning based approach, thereby enabling
it to explain its recommendations ex post. Moreover, it provides envy free
recommendations i.e., drivers at the same location and time do not envy one
another's future earnings. Our experimental evaluation demonstrates the
effectiveness, the robustness, and the generalizability of our framework.
Finally, in contrast to previous works, we make available a reinforcement
learning environment for end to end reproducibility of our work and to
encourage future comparative studies.
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