Ride-Hailing Networks with Strategic Drivers: The Impact of Platform Control Capabilities on Performance
56 Pages Posted: 12 Feb 2018 Last revised: 26 Mar 2018
Date Written: February 16, 2018
Ride-hailing platforms such as Uber, Lyft and Gett match demand (riders) with service capacity (drivers) over a spatial network. This paper studies the impact of two platform control capabilities, demand-side admission control and supply-side repositioning control, on performance from the platform's, drivers' and riders' viewpoints, focusing on a two-location loss network that highlights two practically important characteristics: (i) there is significant demand imbalance across locations, such that the natural supply of drivers ending trips at a location is insufficient to serve the local rider demand, as is common during peak hours in urban areas; and (ii) drivers are self-interested and strategically decide whether to join the network, and if so, when and where to reposition themselves when not serving riders. We provide the following results in the context of a novel model: (1) Considering the steady-state fluid network model equations, we fully characterize the drivers' incentive compatibility conditions for repositioning decisions, and the system equilibrium under three control regimes, ranging from minimal platform control, to centralized admission and repositioning control. (2) We provide new insights on the interplay between admission control and drivers' strategic repositioning decisions: it may be optimal to strategically reject demand at the low-demand location, although there is an excess supply of drivers, to induce repositioning to the high-demand location. We provide necessary and sufficient conditions for this policy. (3) We derive bounds on the platform's and the drivers' achievable performance benefits due to platform controls; these are more significant when capacity is moderate, and when cross-location demand imbalance is significant.
Keywords: ride-hailing, control, network, matching, strategic drivers, demand imbalance
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