  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.
