Pricing Shared Rides

Shared rides, which pool individual riders into a single vehicle, are essential for mitigating congestion and promoting more sustainable urban transportation. However, major ridesharing platforms have long struggled to maintain a healthy and profitable shared rides product. To understand why shared rides have struggled, we analyze procedures commonly used in practice to set static prices for shared rides and discuss their pitfalls. We then propose a pricing policy that is adaptive to matching outcomes, dubbed match-based pricing, which varies prices depending on whether a rider is dispatched alone or to what extent she is matched with another rider. Analysis on a single origin-destination setting reveals that match-based pricing is both profit-maximizing and altruistic, simultaneously improving cost efficiency (i.e., the fraction of cost saved by shared rides relative to individual rides) and reducing rider payments relative to the optimal static pricing policy. These theoretical results are validated on a large-scale simulation with hundreds of origin-destinations from Chicago ridesharing data. The improvements in efficiency and reductions in payments are especially noticeable when costs are high and demand density is low, enabling healthy operations where they have historically been most challenging.

Funding: This work was supported by the National Science Foundation [Grant 2517861], the University of Washington-Amazon Science Hub Faculty Research Award and the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2022-03524].

Supplemental Material: The empirical results in this paper were replicated. The code, data, and files required to reproduce the results were reviewed and are available at https://doi.org/10.1287/opre.2023.0513.