# Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours

arXiv: https://arxiv.org/abs/1912.08388 · HTML: https://mlsanity.com/paper/1912.08388/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 17 of 17)

- [Two-sided fairness in rankings via Lorenz dominance](https://mlsanity.com/paper/2110.15781/) (2021)
- [Fair Clustering Under a Bounded Cost](https://mlsanity.com/paper/2106.07239/) (2021)
- [Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling](https://mlsanity.com/paper/2110.03524/) (2021)
- [Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual](https://mlsanity.com/paper/2201.06021/) (2022)
- [HCRide: Harmonizing Passenger Fairness and Driver Preference for Human-Centered Ride-Hailing](https://mlsanity.com/paper/2508.04811/) (2025)
- [Fairness Maximization among Offline Agents in Online-Matching Markets](https://mlsanity.com/paper/2109.08934/) (2021)
- [Using Simple Incentives to Improve Two-Sided Fairness in Ridesharing Systems](https://mlsanity.com/paper/2303.14332/) (2023)
- [Fairness through Social Welfare Optimization](https://mlsanity.com/paper/2102.00311/) (2021)
- [Subgroup Fairness in Two-Sided Markets](https://mlsanity.com/paper/2106.02702/) (2021)
- [A Survey of Machine Learning-Based Ride-Hailing Planning](https://mlsanity.com/paper/2303.14646/) (2023)
- [Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System](https://mlsanity.com/paper/2401.00093/) (2023)
- [Equity Promotion in Online Resource Allocation](https://mlsanity.com/paper/2112.04169/) (2021)
- [Promoting Two-sided Fairness in Dynamic Vehicle Routing Problem](https://mlsanity.com/paper/2405.19184/) (2024)
- [Deployment Optimization for Shared e-Mobility Systems with Multi-agent Deep Neural Search](https://mlsanity.com/paper/2111.02149/) (2021)
- [Online Task Assignment Problems with Reusable Resources](https://mlsanity.com/paper/2203.07605/) (2022)
- [Equity Promotion in Public Transportation](https://mlsanity.com/paper/2211.14531/) (2022)
- [Long-term Fairness in Ride-Hailing Platform](https://mlsanity.com/paper/2407.17839/) (2024)

## Abstract (arXiv)

Rideshare platforms, when assigning requests to drivers, tend to maximize profit for the system and/or minimize waiting time for riders. Such platforms can exacerbate biases that drivers may have over certain types of requests. We consider the case of peak hours when the demand for rides is more than the supply of drivers. Drivers are well aware of their advantage during the peak hours and can choose to be selective about which rides to accept. Moreover, if in such a scenario, the assignment of requests to drivers (by the platform) is made only to maximize profit and/or minimize wait time for riders, requests of a certain type (e.g. from a non-popular pickup location, or to a non-popular drop-off location) might never be assigned to a driver. Such a system can be highly unfair to riders. However, increasing fairness might come at a cost of the overall profit made by the rideshare platform. To balance these conflicting goals, we present a flexible, non-adaptive algorithm, lpalg, that allows the platform designer to control the profit and fairness of the system via parameters α and β respectively. We model the matching problem as an online bipartite matching where the set of drivers is offline and requests arrive online. Upon the arrival of a request, we use lpalg to assign it to a driver (the driver might then choose to accept or reject it) or reject the request. We formalize the measures of profit and fairness in our setting and show that by using lpalg, the competitive ratios for profit and fairness measures would be no worse than α/e and β/e respectively. Extensive experimental results on both real-world and synthetic datasets confirm the validity of our theoretical lower bounds. Additionally, they show that lpalg under some choice of (α, β) can beat two natural heuristics, Greedy and Uniform, on both fairness and profit.