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Vehicle Redistribution in Ride-Sourcing Markets using Convex Minimum Cost Flows

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Document pages: 12 pages

Abstract: Ride-sourcing platforms often face imbalances in the demand and supply ofrides across areas in their operating road-networks. As such, dynamic pricingmethods have been used to mediate these demand asymmetries through surge pricemultipliers, thus incentivising higher driver participation in the market.However, the anticipated commercialisation of autonomous vehicles couldtransform the current ride-sourcing platforms to fleet operators. The absenceof human drivers fosters the need for empty vehicle management to address anyvehicle supply deficiencies. Proactive redistribution using integer programmingand demand predictive models have been proposed in research to address thisproblem. A shortcoming of existing models, however, is that they ignore themarket structure and underlying customer choice behaviour. As such, currentmodels do not capture the real value of redistribution. To resolve this, weformulate the vehicle redistribution problem as a non-linear minimum cost flowproblem which accounts for the relationship of supply and demand of rides, byassuming a customer discrete choice model and a market structure. Wedemonstrate that this model can have a convex domain, and we introduce an edgesplitting algorithm to solve a transformed convex minimum cost flow problem forvehicle redistribution. By testing our model using simulation, we show that ourredistribution algorithm can decrease wait times by more than 50 , increaseprofit up to 10 with less than 20 increase in vehicle mileage. Our findingsoutline that the value of redistribution is contingent on localised marketstructure and customer behaviour.

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