  The rapid growth of ride-hailing platforms has created a highly competitive
market where businesses struggle to make profits, demanding the need for better
operational strategies. However, real-world experiments are risky and expensive
for these platforms as they deal with millions of users daily. Thus, a need
arises for a simulated environment where they can predict users' reactions to
changes in the platform-specific parameters such as trip fares and incentives.
Building such a simulation is challenging, as these platforms exist within
dynamic environments where thousands of users regularly interact with one
another. This paper presents a framework to mimic and predict user,
specifically driver, behaviors in ride-hailing services. We use a data-driven
hybrid reinforcement learning and imitation learning approach for this. First,
the agent utilizes behavioral cloning to mimic driver behavior using a
real-world data set. Next, reinforcement learning is applied on top of the
pre-trained agents in a simulated environment, to allow them to adapt to
changes in the platform. Our framework provides an ideal playground for
ride-hailing platforms to experiment with platform-specific parameters to
predict drivers' behavioral patterns.
