  Public vehicle (PV) systems are promising transportation systems for future
smart cities which provide dynamic ride-sharing services according to
passengers' requests. PVs are driverless/self-driving electric vehicles which
require frequent recharging from smart grids. For such systems, the challenge
lies in both the efficient scheduling scheme to satisfy transportation demands
with service guarantee and the cost-effective charging strategy under the
real-time electricity pricing. In this paper, we study the joint transportation
and charging scheduling for PV systems to balance the transportation and
charging demands, ensuring the long-term operation. We adopt a cake cutting
game model to capture the interactions among PV groups, the cloud and smart
grids. The cloud announces strategies to coordinate the allocation of
transportation and energy resources among PV groups. All the PV groups try to
maximize their joint transportation and charging utilities. We propose an
algorithm to obtain the unique normalized Nash equilibrium point for this
problem. Simulations are performed to confirm the effects of our scheme under
the real taxi and power grid data sets of New York City. Our results show that
our scheme achieves almost the same transportation performance compared with a
heuristic scheme, namely, transportation with greedy charging; however, the
average energy price of the proposed scheme is 10.86% lower than the latter
one.
