Intermodal Autonomous Mobility-on-Demand

Mauro Salazar (Corresponding author), Nicolas Lanzetti, Federico Rossi, Maximilian Schiffer, Marco Pavone

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

In this paper we study models and coordination policies for intermodal Autonomous Mobility-on-Demand (AMoD), wherein a fleet of self-driving vehicles provides on-demand mobility jointly with public transit. Specifically, we first present a network flow model for intermodal AMoD, where we capture the coupling between AMoD and public transit and the goal is to maximize social welfare. Second, leveraging such a model, we design a pricing and tolling scheme that allows the system to recover a social optimum under the assumption of a perfect market with selfish agents. Third, we present real-world case studies for the transportation networks of New York City and Berlin, which allow us to quantify the general benefits of intermodal AMoD, as well as the societal impact of different vehicles. In particular, we show that vehicle size and powertrain type heavily affect intermodal routing decisions and, thus, system efficiency. Our studies reveal that the cooperation between AMoD fleets and public transit can yield significant benefits compared to an AMoD system operating in isolation, whilst our proposed tolling policies appear to be in line with recent discussions for the case of New York City.
Original languageEnglish
Article number8894439
Pages (from-to)3946-3960
Number of pages15
JournalIEEE Transactions on Intelligent Transportation Systems
Volume21
Issue number9
DOIs
Publication statusPublished - Sep 2020
Externally publishedYes

Keywords

  • Autonomous vehicles
  • networks
  • optimization
  • public transportation

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