Ride-sourcing platforms often face imbalances in the demand and supply of
rides across areas in their operating road-networks. As such, dynamic pricing
methods have been used to mediate these demand asymmetries through surge price
multipliers, thus incentivising higher driver participation in the market.
However, the anticipated commercialisation of autonomous vehicles could
transform the current ride-sourcing platforms to fleet operators. The absence
of human drivers fosters the need for empty vehicle management to address any
vehicle supply deficiencies. Proactive redistribution using integer programming
and demand predictive models have been proposed in research to address this
problem. A shortcoming of existing models, however, is that they ignore the
market structure and underlying customer choice behaviour. As such, current
models do not capture the real value of redistribution. To resolve this, we
formulate the vehicle redistribution problem as a non-linear minimum cost flow
problem which accounts for the relationship of supply and demand of rides, by
assuming a customer discrete choice model and a market structure. We
demonstrate that this model can have a convex domain, and we introduce an edge
splitting algorithm to solve a transformed convex minimum cost flow problem for
vehicle redistribution. By testing our model using simulation, we show that our
redistribution algorithm can decrease wait times up to 50% and increase vehicle
utilization up to 8%. Our findings outline that the value of redistribution is
contingent on localised market structure and customer behaviour.