  With the electrification of ride-hailing fleets, there will be a need to
incentivize where and when the ride-hailing vehicles should charge. In this
work, we assume that a central authority wants to control the distribution of
the vehicles and can do so by selecting charging prices. Since there will
likely be more than one ride-hailing company in the market, we model the
problem as a single-leader multiple-follower Stackelberg game. The followers,
i.e., the companies, compete about the charging resources under given prices
provided by the leader. We present a learning algorithm based on the concept of
contextual bandits that allows the central authority to find an efficient
pricing strategy. We also show how the exploratory phase of the learning can be
improved if the leader has some partial knowledge about the companies'
objective functions. The efficiency of the proposed algorithm is demonstrated
in a simulated case study for the city of Shenzhen, China.
