Iterative Network Pricing for Ridesharing Platforms

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 the destinations of the trips, 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 made public by the City of Chicago, we illustrate via simulation the iterative updates under our mechanism for morning rush hours, demonstrating not only substantial welfare improvements but also remarkable robustness amid the significant fluctuations of market conditions from early 2019 through the end of 2020.