Iterative Network Pricing for Ridesharing Platforms

TL;DR

This paper proposes an iterative network pricing mechanism for ridesharing platforms that improves welfare by considering origin-destination demand and updating prices based on observable data, converging to near-optimal outcomes.

Contribution

It introduces a practical iterative pricing method that accounts for OD demand and converges to welfare-optimal prices using real-time market data.

Findings

Welfare improves significantly with the proposed mechanism.

The mechanism converges under stationary market conditions.

Simulation shows robustness to market fluctuations.

Abstract

Ridesharing platforms match riders and drivers, using dynamic pricing to balance supply and demand. The origin-based "surge pricing", however, does not take into consideration market conditions at trip destinations, leading to inefficient driver flows in space and incentivizes drivers to strategize. In this work, we introduce the Iterative Network Pricing mechanism, addressing a main challenge in the practical implementation of optimal origin-destination (OD) based prices, that the model for rider demand is hard to estimate. Assuming that the platform's surge algorithm clears the market for each origin in real-time, our mechanism updates the OD-based price adjustments week-over-week, using only information immediately observable during the same time window in the prior weeks. For stationary market conditions, we prove that our mechanism converges to an outcome that is approximately welfare-optimal. Using data from the City of Chicago, we illustrate (via simulation) the iterative updates under our mechanism for morning rush hours, demonstrating substantial welfare improvements despite significant fluctuations of market conditions from early 2019 through the end of 2020.