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

Shadi Djavadian, Joseph Y.J. Chow

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)36-57
Number of pages22
JournalTransportation Research Part B: Methodological
Volume104
DOIs
StatePublished - Oct 2017

Keywords

  • Agent-based
  • Day-to-day adjustment
  • Dynamic equilibrium
  • Flexible transport services
  • Last mile problem
  • Public transit

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Transportation

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