Trading the System Efficiency for the Income Equality of Drivers in Rideshare

TL;DR

This paper models the tradeoff between system efficiency and income equality among rideshare drivers by proposing an online matching algorithm that balances fairness and profit, validated through theoretical analysis and real-world data.

Contribution

It introduces a novel online bipartite-matching model with acceptance probabilities to address income inequality and proposes LP-based algorithms with competitive analysis.

Findings

Algorithms effectively balance fairness and profit.

Theoretical analysis confirms algorithm efficiency.

Experimental results support theoretical predictions.

Abstract

Several scientific studies have reported the existence of the income gap among rideshare drivers based on demographic factors such as gender, age, race, etc. In this paper, we study the income inequality among rideshare drivers due to discriminative cancellations from riders, and the tradeoff between the income inequality (called fairness objective) with the system efficiency (called profit objective). We proposed an online bipartite-matching model where riders are assumed to arrive sequentially following a distribution known in advance. The highlight of our model is the concept of acceptance rate between any pair of driver-rider types, where types are defined based on demographic factors. Specially, we assume each rider can accept or cancel the driver assigned to her, each occurs with a certain probability which reflects the acceptance degree from the rider type towards the driver type. We construct a bi-objective linear program as a valid benchmark and propose two LP-based parameterized online algorithms. Rigorous online competitive ratio analysis is offered to demonstrate the flexibility and efficiency of our online algorithms in balancing the two conflicting goals, promotions of fairness and profit. Experimental results on a real-world dataset are provided as well, which confirm our theoretical predictions.