Ridesharing with Driver Location Preferences

Duncan Rheingans-Yoo, Scott Duke Kominers, Hongyao Ma, David C. Parkes

IJCAI 2019 pp. 557-564

doi:10.24963/IJCAI.2019/79 /ijcai/2019/rheingansyoo2019ijcai-ridesharing/

Abstract

We study revenue-optimal pricing and driver compensation in ridesharing platforms when drivers have heterogeneous preferences over locations. If a platform ignores drivers' location preferences, it may make inefficient trip dispatches; moreover, drivers may strategize so as to route towards their preferred locations. In a model with stationary and continuous demand and supply, we present a mechanism that incentivizes drivers to both (i) report their location preferences truthfully and (ii) always provide service. In settings with unconstrained driver supply or symmetric demand patterns, our mechanism achieves (full-information) first-best revenue. Under supply constraints and unbalanced demand, we show via simulation that our mechanism improves over existing mechanisms and has performance close to the first-best.

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Cite

Text

Rheingans-Yoo et al. "Ridesharing with Driver Location Preferences." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/79

Markdown

[Rheingans-Yoo et al. "Ridesharing with Driver Location Preferences." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/rheingansyoo2019ijcai-ridesharing/) doi:10.24963/IJCAI.2019/79

BibTeX

@inproceedings{rheingansyoo2019ijcai-ridesharing,
  title     = {{Ridesharing with Driver Location Preferences}},
  author    = {Rheingans-Yoo, Duncan and Kominers, Scott Duke and Ma, Hongyao and Parkes, David C.},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {557-564},
  doi       = {10.24963/IJCAI.2019/79},
  url       = {https://mlanthology.org/ijcai/2019/rheingansyoo2019ijcai-ridesharing/}
}