Spatial Pricing of Ride-sourcing Services in a CongestedTransportation Network

TL;DR

This paper develops a Stackelberg model to analyze how spatial pricing by transportation network companies affects congestion, driver relocation, and rider choices, providing algorithms and insights for optimizing market balance.

Contribution

It introduces a novel Stackelberg framework incorporating congestion effects and proves the existence, uniqueness, and convex reformulation of optimal spatial pricing strategies.

Findings

Optimal pricing strategies exist and are unique.

Convex reformulation enables efficient computation.

Spatial pricing impacts congestion and market balance.

Abstract

We investigate the impacts of spatial pricing for ride-sourcing services in a Stackelberg framework considering traffic congestion. In the lower level, we use combined distribution and assignment approaches to explicitly capture the interactions between drivers' relocation, riders' mode choice, and all travelers' routing decisions. In the upper level, a single transportation network company (TNC) determines spatial pricing strategies to minimize imbalance in a two-sided market. We show the existence of the optimal pricing strategies for locational imbalance minimization, and propose effective algorithms with reliable convergence properties. Furthermore, the optimal pricing is unique and can be solved in a convex reformulation when matching time can be ignored. We conduct numerical experiments on different scales of transportation networks with different TNC objectives to generate policy insights on how spatial pricing could impact transportation systems.