A Bi-Level Optimization Model for Ride-Sourcing Platform’s Spatial Pricing Strategy

This article investigates a long-term optimal spatial pricing strategy for a ride-sourcing platform that serves a particular (possibly populated) area with profit-driven service providers (i.e., drivers) and time- and price-sensitive customers (i.e., passengers). By observing that oftentimes, the price strategy is anisotropic and spatial-dependent, both the supply and request are endogenous, and the authors build an analytical bi-level optimization mode. In the upper-level formulation, the ride-sourcing platform aims at setting up the spatially heterogeneous pricing strategy to maximize its total profit. However, in the lower level, the authors solve the trip distribution model that characterizes the flow rates among zones given the travel demand rate at each zone. The authors prove that when the platform seeks to expand its business, the optimal number of participating drivers and their optimal wages will be influenced not only by the pricing strategy but also by the level of service of the entire platform. The authors' further investigation shows that the profit at a particular zone can be influenced by the potential customers’ service requests from other zones. Finally, the authors use the real-world data provided by DiDi Chuxing to numerically illustrate their model and theoretical results.

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  • English

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  • Accession Number: 01840200
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Mar 25 2022 12:08PM