An Efficient Auction with Variable Reserve Prices for Ridesourcing

Abstract

Ridesourcing refers to the service that matches passengers who need a car to personal drivers. In this work, we study an auction model for ridesourcing that sells multiple items to unit-demand single-parameter agents with variable reserve price constraints. In this model, there is an externally imposed reserve price set for every item, and the price is both item- and bidder-dependent. Such auctions can also find applications in a number of other traditional and online markets, such as ad auction or online laboring market.

Our main result is a truthful, individually rational, and computationally efficient mechanism that respects the reserve price constraints and always achieves at least half of the optimal social benefit (i.e., the sum of the valuations of the winning agents). Furthermore, we show such efficiency approximation is tight by proving that even without any computational constraints, no truthful and individually rational mechanism can achieve better than 2-approximation for social benefit maximization. Finally, we evaluate the performance of our mechanism based on real taxi-trace data. The empirical results show that our mechanism outperforms other benchmark mechanisms in terms of both social benefit and revenue.

This work was supported in part by the State Key Development Program for Basic Research of China (973 project 2014CB340303), in part by China NSF grant 61672348, 61672353, and 61472252, and in part by Shanghai Science and Technology fund 15220721300. The opinions, findings, conclusions, and recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the funding agencies or the government.