@article{ref1,
title="An agent-based day-to-day adjustment process for modeling 'Mobility as a Service' with a two-sided flexible transport market",
journal="Transportation research part B: methodological",
year="2017",
author="Djavadian, Shadi and Chow, Joseph Y. J.",
volume="104",
number="",
pages="36-57",
abstract="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&quot;. 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&quot; 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.<p /> <p>Language: en</p>",
language="en",
issn="0191-2615",
doi="10.1016/j.trb.2017.06.015",
url="http://dx.doi.org/10.1016/j.trb.2017.06.015"
}