On the interaction between autonomous mobility-on-demand and public transportation systems

Mauro Salazar, Federico Rossi, Maximilian Schiffer, Christopher H. Onder, Marco Pavone

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

88 Citations (Scopus)
1 Downloads (Pure)

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 to achieve the social optimum under the assumption of a perfect market with selfish agents. Finally, we present a real-world case study for New York City. Our results show that the coordination between AMoD fleets and public transit can yield significant benefits compared to an AMoD system operating in isolation.

Original languageEnglish
Title of host publication2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages2262-2269
Number of pages8
ISBN (Electronic)9781728103235
DOIs
Publication statusPublished - 7 Dec 2018
Externally publishedYes
Event21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018 - Maui, HI, USA, Maui, United States
Duration: 4 Nov 20187 Nov 2018

Conference

Conference21st IEEE International Conference on Intelligent Transportation Systems, ITSC 2018
Country/TerritoryUnited States
CityMaui
Period4/11/187/11/18

Funding

ACKNOWLEDGMENTS We would like to thank Dr. Daniele Vigo and Dr. Guido Gentile for the fruitful discussions, and Dr. Ilse New for her assistance with the proofreading and useful advice. The first author would like to thank Dr. Lino Guzzella for his support. This research was supported by the National Science Foundation under CAREER Award CMMI-1454737 and the Toyota Research Institute (TRI). This article solely reflects the opinions and conclusions of its authors and not NSF, TRI, or any other entity. This paper is dedicated to Stella.

Keywords

  • cs.SY
  • cs.RO

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