An agent-based day-to-day adjustment process for modeling ‘Mobility as a Service’ with a two-sided flexible transport market

Shadi Djavadian and Joseph Y.J. Chow

Transportation Research Part B: Methodological, 2017, vol. 104, issue C, 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". 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.

Keywords: Flexible transport services; Public transit; Dynamic equilibrium; Day-to-day adjustment; Agent-based; Last mile problem (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (26)

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DOI: 10.1016/j.trb.2017.06.015

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