Ride-Hailing Networks with Strategic Drivers:
The Impact of Platform Control Capabilities on Performance
Abstract
Background and motivation. This work is motivated by the emergence of ride-hailing platforms
such as Uber, Lyft and Gett that match service supply (drivers) with demand (passengers) over a
geographically dispersed network. The problem of matching supply with demand is complicated by
two key challenges: (1) There are significant demand imbalances between network node pairs; e.g.,
during rush hour in Manhattan, the demand rate in one direction may be up to ten times the rate in
the opposite direction. (2) Matching is in part decentralized because drivers are self-interested and
behave strategically in two key decisions, whether to join, and – if they can – whether/where to
“reposition” (i.e., route) themselves in the network when they are not transporting a customer.
To address these challenges, we study the following questions on the value of platform control:
(1) What are the characteristics of the system equilibrium under three operating regimes, ranging
from minimal control to centralized admission and repositioning control?
(2) What is the impact of these controls on platform revenue, driver capacity and per-driver profits?
Literature and positioning. This work contributes to the literature on the sharing economy. The
key novelty of this paper is that it jointly considers (i) strategic server behavior, (ii) a network and
(iii) congestion and queueing. Bimpikis et al. (2016) consider (i) and (ii) but ignore (iii). To our
knowledge no other studies consider incentives in a network: They focus either on networks
without incentives (e.g., Caldentey et al. 2009, Adan and Weiss 2012, Gurvich and Ward 2014, Hu
and Zhou 2015, O'Mahony and Shmoys 2015, Ding et al. 2016, Braverman et al. 2016, Ozkan and
Ward 2016, Iglesias et al. 2016), or on incentives in single-location settings (e.g., Bai et al. 2016,
Benjaafar et al. 2016, Cachon et al. 2016, Gurvich et al. 2016, Riquelme et al. 2016, Taylor 2016).
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Model and problem formulation. We study the steady-state behavior of a deterministic fluid
model of a two-location ride-hailing loss network with four routes (two for local and two for crosslocation traffic). Travel times are deterministic. Three parties interact through this network.
Passengers generate demand for each route, paying a fixed price per unit travel time. Drivers decide,
based on their opportunity cost and their expected profit rate, whether to join the network, and if so,
where to position themselves in the network in anticipation of passenger demand. The platform
seeks to maximize its commission revenue from drivers and has two potential control levers:
demand-side admission control and supply-side repositioning. We consider three operating regimes:
(i) minimal control: FIFO admission control at each location and decentralized (driver)
repositioning; (ii) optimal admission control at each location and decentralized repositioning; (iii)
optimal centralized control, both for admission control and repositioning.
Considering a game-theoretic framework, we formulate and solve the platform’s revenue
maximization problem under each regime as the problem of allocating capacity (between service,
repositioning, and queueing) subject to system flow constraints and driver incentive constraints.
Key results and contributions. Our results and contributions are threefold:
(1) Complete characterization of system equilibrium under each regime. Of note, these results
contribute novel insights on the value of admission control to manipulate drivers’ strategic
routing choices: the platform may want to turn away demand at the low-demand location, to
increase the driver queue there and incentivize them to reposition to the high-demand location.
(2) The value of platform controls is largest at moderate capacity. When capacity is moderate, the
platform can utilize capacity more efficiently and improve the revenue by exercising admission
control; the platform’s ability to reposition capacity further improves performance. When
capacity is scarce or abundant, platform controls do not add value. The figures below illustrate
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these results: when capacity is moderate (zones 2 and 3), more control (vs. minimal control in
left panel) increases the capacity share allocated to service versus queueing or repositioning.
(3) The value of platform controls increases with the demand imbalance. Building on the
equilibrium results in (1), we provide bounds on the value of platform controls in terms of
percentage improvements in platform revenues and per-driver profits. These bounds reflect
both the operational and the economic characteristics of the network. We find that the demand
imbalance between the cross-traffic demand flows in opposite directions is the key driver of the
gains from control. As illustrated in the table below, the value of control grows very
significantly in the cross-traffic imbalance.
Cross-traffic demand imbalance
1
2
6
10
(ratio of high-to-low demand location)
Participating drivers 0% -5% -26% -25%
Optimal Admission Control
0% 26% 47%
50%
vs
Platform revenue
Minimal Control
0% -5% -26% -25%
Driver profit
70%
Participating drivers 0% 16% 70%
Central vs Decentral
0% 13% 93%
95%
Platform revenue
Repositioning
0% 16% 70%
70%
Driver profit
Optimal Centralized Control
27%
Participating drivers 0% 10% 26%
vs
0% 43% 184% 193%
Platform revenue
Minimal Control
0% 10% 26%
27%
Driver profit
Note: Example assumes same travel time for all routes, uniformly distributed driver
opportunity costs, a commission rate of 25%, and driving cost equal to 33% of the fare price.
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