  Matching in two-sided markets such as ride-hailing has recently received
significant attention. However, existing studies on ride-hailing mainly focus
on optimising efficiency, and fairness issues in ride-hailing have been
neglected. Fairness issues in ride-hailing, including significant earning
differences between drivers and variance of passenger waiting times among
different locations, have potential impacts on economic and ethical aspects.
The recent studies that focus on fairness in ride-hailing exploit traditional
optimisation methods and the Markov Decision Process to balance efficiency and
fairness. However, there are several issues in these existing studies, such as
myopic short-term decision-making from traditional optimisation and instability
of fairness in a comparably longer horizon from both traditional optimisation
and Markov Decision Process-based methods. To address these issues, we propose
a dynamic Markov Decision Process model to alleviate fairness issues currently
faced by ride-hailing, and seek a balance between efficiency and fairness, with
two distinct characteristics: (i) a prediction module to predict the number of
requests that will be raised in the future from different locations to allow
the proposed method to consider long-term fairness based on the whole timeline
instead of consider fairness only based on historical and current data
patterns; (ii) a customised scalarisation function for multi-objective
multi-agent Q Learning that aims to balance efficiency and fairness. Extensive
experiments on a publicly available real-world dataset demonstrate that our
proposed method outperforms existing state-of-the-art methods.
