Day-to-day market evaluation of modular autonomous vehicle fleet operations with en-route transfers

Nicholas S. Caros, Joseph Y.J. Chow

Research output: Contribution to journalArticle

Abstract

This study extends the two-sided day-to-day learning framework to simulate the performance of a mobility service using modular autonomous vehicles (MAVs) capable of en-route passenger transfers. An insertion heuristic is used to assign trips to a fleet of vehicles and to determine whether engaging in an en-route transfer is advantageous. The operator acts as an endogenous decision maker, updating the relative weight of the operator cost and user cost within the routing algorithm after each simulation day to optimize profit. Real transit ridership data from the United Arab Emirates are used for an empirical study of three operating strategies: door-to-door service within an urban core, commuter first/last mile service and a hub-and-spoke service. Results are compared with and without en-route transfers to quantify the advantage of the en-route transfer capability for each strategy.

Original languageEnglish (US)
JournalTransportmetrica B
DOIs
StateAccepted/In press - 2020

Keywords

  • Day-to-day adjustment
  • flexible transit
  • last mile problem
  • ridesharing

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Transportation

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