Uber Stable: Formulating the Rideshare System as a Stable Matching Problem

TL;DR

This paper models ride-sharing platforms as a stable matching problem using the Gale-Shapley algorithm, evaluating its performance through simulations to improve driver satisfaction and system efficiency.

Contribution

It introduces a preference-aware stable matching algorithm for ride-sharing, incorporating driver and passenger preferences, and compares its performance with existing algorithms via simulation.

Findings

Stable matching improves driver income distribution

Preference-aware algorithms outperform random matching

Prioritizing proximity affects system efficiency and fairness

Abstract

Peer-to-peer ride-sharing platforms like Uber, Lyft, and DiDi have revolutionized the transportation industry and labor market. At its essence, these systems tackle the bipartite matching problem between two populations: riders and drivers. This research paper comprises two main components: an initial literature review of existing ride-sharing platforms and efforts to enhance driver satisfaction, and the development of a novel algorithm implemented through simulation testing to allow us to make our own observations. The core algorithm utilized is the Gale-Shapley deferred acceptance algorithm, applied to a static matching problem over multiple time periods. In this simulation, we construct a preference-aware task assignment model, considering both overall revenue maximization and individual preference satisfaction. Specifically, the algorithm design incorporates factors such as passenger willingness-to-pay, driver preferences, and location attractiveness, with an overarching goal of achieving equitable income distribution for drivers while maintaining overall system efficiency. Through simulation, the paper compares the performance of the proposed algorithm with random matching and closest neighbor algorithms, looking at metrics such as total revenue, revenue per ride, and standard deviation to identify trends and impacts of shifting priorities. Additionally, the DA algorithm is compared to the Boston algorithm, and the paper explores the effect of prioritizing proximity to passengers versus distance from city center. Ultimately, the research underscores the importance of continued exploration in areas such as dynamic pricing models and additional modeling for unconventional driving times to further enhance the findings on the effectiveness and fairness of ride-sharing platforms.