[Paper Review] Spatio-Temporal Pricing for Ridesharing Platforms

Hongyao Ma, Fei Fang+1 more|arXiv (Cornell University)|Jan 11, 2018

Transportation and Mobility InnovationsEngineering33 references16 citations

TL;DR

This paper proposes the Spatio-Temporal Pricing (STP) mechanism for ridesharing platforms to address spatial and temporal imbalances in supply and demand. By setting dynamic, location- and time-specific prices that ensure drivers are incentivized to accept dispatched trips, STP achieves welfare-optimality, individual rationality, budget balance, and core selection in a subgame-perfect equilibrium—proving impossible in a dominant-strategy setting.

ABSTRACT

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 with the flexibility to decide how to work will nevertheless choose to accept their dispatched trips, rather than drive to another area or wait for higher prices or a better trip. In this work, we propose a complete information model that is simple yet rich enough to incorporate spatial imbalance and temporal variations in supply and demand -- conditions that lead to market failures in today's platforms. We introduce the Spatio-Temporal Pricing (STP) mechanism. The mechanism is incentive-aligned, in that it is a subgame-perfect equilibrium for drivers to always accept their trip dispatches. From any history onward, the equilibrium outcome of the STP mechanism is welfare-optimal, envy-free, individually rational, budget balanced, and core-selecting. We also prove the impossibility of achieving the same economic properties in a dominant-strategy equilibrium. Simulation results show that the STP mechanism can achieve substantially improved social welfare and earning equity than a myopic mechanism.

Motivation & Objective

• Address market failures in ridesharing platforms caused by spatial and temporal imbalances in supply and demand.
• Overcome driver mispricing incentives such as 'chasing the surge' and strategic offlining during high-demand events.
• Design a pricing mechanism that maintains driver incentives to accept dispatched trips despite spatial and temporal price variations.
• Ensure economic efficiency and fairness through subgame-perfect equilibrium outcomes that are welfare-optimal, envy-free, and budget-balanced.
• Demonstrate that achieving these properties in a dominant-strategy equilibrium is impossible, justifying the use of subgame-perfect incentives.

Proposed method

• Propose a complete information model that captures spatial and temporal variations in supply and demand.
• Introduce the Spatio-Temporal Pricing (STP) mechanism, which sets dynamic prices based on location and time to align incentives.
• Establish that STP induces a subgame-perfect equilibrium where drivers always accept dispatched trips.
• Prove that the equilibrium outcome is welfare-optimal, envy-free, individually rational, budget-balanced, and core-selecting.
• Use a mapping to trading networks to analyze equilibrium properties, though the mechanism's dynamic decision structure differs from standard trading network models.
• Conduct simulations comparing STP to a myopic pricing mechanism under various demand scenarios, including morning rush hour and event-based surges.

Experimental results

Research questions

• RQ1 Can a dynamic pricing mechanism be designed to align driver incentives with system-wide efficiency in the presence of spatial and temporal imbalances?
• RQ2 Is it possible to achieve welfare-optimality, budget balance, and individual rationality in a dominant-strategy equilibrium for ridesharing dispatching?
• RQ3 How do spatial mispricing and temporal mispricing affect driver behavior and platform efficiency?
• RQ4 What are the performance differences between STP and myopic pricing in terms of social welfare and earning equity?
• RQ5 Can the STP mechanism ensure that drivers accept dispatched trips even when prices vary significantly across locations and times?

Key findings

• The STP mechanism ensures that drivers always accept dispatched trips in a subgame-perfect equilibrium, eliminating incentives to chase surge or go off-line.
• STP achieves welfare-optimality, envy-freeness, individual rationality, budget balance, and core selection in equilibrium, which are not simultaneously achievable in a dominant-strategy mechanism.
• Simulation results show that STP significantly improves social welfare and earning equity compared to myopic pricing, especially in imbalanced demand scenarios.
• During morning rush hour simulations, STP effectively redirects drivers to high-demand areas like location C, while myopic pricing leads to driver congestion in low-demand zones like B.
• STP generates more intuitive and interpretable price patterns across trips, reflecting actual supply-demand dynamics, unlike myopic pricing which fails to correct for spatial imbalances.
• The mechanism successfully mitigates network externalities by accounting for destination-side market conditions, reducing the incentive for drivers to decline trips to low-priced or high-wait-time locations.