Due to advances in communications technologies and social networks, flexible mobility systems such as taxi, carpool and demand responsive transit have gained interest among practitioners and researchers as a solution to address such problems as the "first/last mile problem". While recent research has modeled these systems using agent-based stochastic day-to-day processes, they assume only traveler adjustment under a one-sided market setting. What if such systems are naturally "two-sided markets" like Uber or AirBnB? In this study, we explore flexible transport services in the framework of two-sided markets, and extend an earlier day-to-day adjustment process to include day-to-day adjustment of the service operator(s) as the seller and the built environment as the platform of a two-sided market. We use the Ramsey pricing criterion for social optimum to show that a perfectly matched state from a day-to-day process is equivalent to a social optimum. A case study using real data from Oakville, Ontario, as a first/last mile problem example demonstrates the sensitivity of the day-to-day model to operating policies. Computational experiments confirm the existence of locally stable states. More importantly, the experiments show the existence of thresholds from which network externalities cause two-sided and one-sided market equilibria to diverge. (C) 2017 Elsevier Ltd. All rights reserved.
