Ride-Hailing Networks with Strategic Drivers: The Impact of Platform Control Capabilities on Performance

Afèche, Philipp; Liu, Zhe; Maglaras, Costis

doi:10.2139/ssrn.3120544

Cited by 81 publications

(45 citation statements)

References 24 publications

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“…They suggest the use of a rate cap regulation to avoid excessively high pricing and provide an empirical study using data from a Chinese TNC. Also assuming platform revenue maximization, Castro et al [9], Afèche et al [20], Bimpikis et al [5] considered the problem of matching ride-sourcing costumers to "strategic" drivers in a geometric area by examining how the platform should respond to a short-term [9] and long-term [20,5] supply-demand imbalance. Drivers are considered strategic because they move in equilibrium in a simultaneous move game, choosing where to reposition based on prices, supply levels, and driving costs.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of ride-sourcing search frictions and driver productivity: A spatial denoising approach

Zuniga-Garcia

Tec

Scott

et al. 2020

Transportation Research Part C: Emerging Technologies

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This paper considers the problem of spatial and temporal mispricing of ride-sourcing trips from a driver perspective. Using empirical data from more than 1.1 million rides in Austin, Texas, we explore the spatial structure of ride-sourcing search frictions and driver performance variables as a function of the trip destination. The spatial information is subject to noise and sparsity, and researchers tend to aggregate the data in large areas, which results in the loss of high-resolution insights. We implemented the graph-fused lasso (GFL), a spatial smoothing or denoising methodology that allows for high-definition spatial evaluation. GFL removes noise in discrete areas by emphasizing edges, which is practical for evaluating zones with heterogeneous types of trips, such as airports, without blurring the information to surrounding areas. Principal findings suggest that there are differences in driver productivity depending on trip type and pickup and drop-off location. Therefore, providing spatio-temporal pricing strategies could be one way to balance driver equity across the network.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluation of ride-sourcing search frictions and driver productivity: A spatial denoising approach

Zuniga-Garcia

Tec

Scott

et al. 2020

Transportation Research Part C: Emerging Technologies

View full text Add to dashboard Cite

show abstract

“…However, dynamic pricing is more robust to fluctuations and to mis-estimation of system parameters. By analizing a two-location, stationary state queueing model, Afèche et al [1] study the impact of platform control on platform revenue and driver income.…”

Section: Related Workmentioning

confidence: 99%

“…The problem in our setting is that the existence of a driver for only one period may exert negative externality on the rest of the economy by inducing suboptimal positioning of the rest of the drivers in the subsequent time periods. See Appendix D. 1 for examples and discussions.…”

Section: Related Workmentioning

confidence: 99%

“…If a rider requests the ride, the platform dispatches a nearby driver to pick up the rider, and if neither side cancels and the trip is completed, payment is made from the rider to the driver through the platform. 1 Comparing with traditional taxi systems, ridesharing platforms significantly increase the fraction of time when drivers have a rider in the back seat, reducing trip costs for riders [19]. The with demonstrated learning effects leading to differences in long-run earnings (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Pricing for Ridesharing Platforms

Fang

Parkes

2019

Proceedings of the 2019 ACM Conference on Economics and Computation

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Ridesharing platforms match drivers and riders to trips, using dynamic prices to balance supply and demand. A challenge is to set prices that are appropriately smooth in space and time, so that drivers will choose to accept their dispatched trips, rather than drive to another area or wait for higher prices or a better trip. We work in a complete information, discrete time, multiperiod, multi-location model, and introduce the Spatio-Temporal Pricing (STP) mechanism. The mechanism is incentive-aligned, in that it is a subgame-perfect equilibrium for drivers to accept their dispatches. The mechanism is also welfare-optimal, envy-free, individually rational, budget balanced and core-selecting from any history onward. The proof of incentive alignment makes use of the M ♮ concavity of min-cost flow objectives. We also give an impossibility result, that there can be no dominant-strategy mechanism with the same economic properties. An empirical analysis conducted in simulation suggests that the STP mechanism can achieve significantly higher social welfare than a myopic pricing mechanism. * Thanks to

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“…We believe this paper is the first to examine the impact of market thickness on spatial distribution of supply. Papers that study spatial demand-supply mismatches (e.g., Buchholz (2018); Lagos (2000); Afèche et al (2018); Banerjee et al (2018)) focus on other mechanisms, mainly search frictions. Papers that study the consequences of market thickness (such as Frechette et al (2019); Nikzad (2018)) have not looked at its implications for the geographical distribution of supply.…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution of Supply and the Role of Market Thickness: Theory and Evidence from Ride Sharing

Ghili

Kumar

2020

SSRN Journal

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This paper develops a strategy with simple implementation and limited data requirements to identify spatial distortion of supply from demand-or, equivalently, unequal access to supply among regions-in transportation markets. We apply our method to ride-level, multi-platform data from New York City (NYC) and show that for smaller rideshare platforms, supply tends to be disproportionately concentrated in more densely populated areas. We also develop a theoretical model to argue that a smaller platform size, all else being equal, distorts the supply of drivers toward more densely populated areas due to network effects. Motivated by this, we estimate a minimum required platform size to avoid geographical supply distortions, which informs the current policy debate in NYC around whether ridesharing platforms should be downsized. We find the minimum required size to be approximately 3.5M rides/month for NYC, implying that downsizing Lyft or Via-but not Uber-can increase geographical inequity.

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Ride-Hailing Networks with Strategic Drivers: The Impact of Platform Control Capabilities on Performance

Cited by 81 publications

References 24 publications

Evaluation of ride-sourcing search frictions and driver productivity: A spatial denoising approach

Evaluation of ride-sourcing search frictions and driver productivity: A spatial denoising approach

Spatio-Temporal Pricing for Ridesharing Platforms

Spatial Distribution of Supply and the Role of Market Thickness: Theory and Evidence from Ride Sharing

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