  We investigate the impacts of spatial pricing for ride-sourcing services in a
Stackelberg framework considering traffic congestion. In the lower level, we
use combined distribution and assignment approaches to explicitly capture the
interactions between drivers' relocation, riders' mode choice, and all
travelers' routing decisions. In the upper level, a single transportation
network company (TNC) determines spatial pricing strategies to minimize
imbalance in a two-sided market. We show the existence of the optimal pricing
strategies for locational imbalance minimization, and propose effective
algorithms with reliable convergence properties. Furthermore, the optimal
pricing is unique and can be solved in a convex reformulation when matching
time can be ignored. We conduct numerical experiments on different scales of
transportation networks with different TNC objectives to generate policy
insights on how spatial pricing could impact transportation systems.
