Courteous or Crude? Understanding and Shaping User Behavior in Ride-hailing

In this paper, we build an evolutionary game theory model to investigate how riders' and drivers' behavior evolves in response to a ride-hailing platform's operational decisions, and study how it impacts the platform's performance and the social welfare. We identify two sustainable asymptotically stable equilibria of the dynamic system of the platform, one resembling a traditional taxi service while the other resembling a successful ride-hailing platform. Using this characterization, we then show how the platform could leverage operational tools at its disposal to optimize its performance. Finally, we establish that a platform can generally improve social welfare and may achieve the socially optimal state by prioritizing high-rating riders in matching under supply shortage. Our analysis highlights the importance for ride-hailing platforms to implement and strategically leverage rider ratings, and can potentially provide guidelines for improving platform performance not just with standard instruments such as price and wage adjustments, but also by making rider rating-driven adjustments into the matching procedure.