Modelling and optimization of flexible users in large-scale ridesharing systems

Abstract

The performance of ridesharing systems is intricately entwined with user participation. To characterize such interplay, we adopt a repeated multi-player, non-cooperative game approach to model a ridesharing platform and its users’ decision-making. Users reveal to the platform their participation preferences over being only riders, only drivers, flexible users, and opt-out based on the utilities of each mode. The platform optimally matches users with different itineraries and participation preferences to maximize social welfare. We analytically establish the existence of equilibria and design an iterative algorithm for the solution. A case study is conducted with real travel demand data in Chicago. The results highlight the effect of users’ flexibility regarding mode preferences on system performance. A sensitivity analysis of the participation level of users underscores the effect of economies of scale in such systems, emphasizing the pivotal mode of user participation in system efficiency.