Congestion-aware routing and rebalancing of Autonomous Mobility-on-Demand systems in mixed traffic

Salomón Wollenstein-Betech, Arian Houshmand, Mauro Salazar, Marco Pavone, Christos G. Cassandras, Ioannis Ch. Paschalidis

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Abstract

This paper studies congestion-aware route-planning policies for Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on-demand mobility under mixed traffic conditions. Specifically, we first devise a network flow model to optimize the AMoD routing and rebalancing strategies in a congestion-aware fashion by accounting for the endogenous impact of AMoD flows on travel time. Second, we capture reactive exogenous traffic consisting of private vehicles selfishly adapting to the AMoD flows in a user-centric fashion by leveraging an iterative approach. Finally, we showcase the effectiveness of our framework with two case-studies considering the transportation sub-networks in Eastern Massachusetts and New York City. Our results suggest that for high levels of demand, pure AMoD travel can be detrimental due to the additional traffic stemming from its rebalancing flows, while the combination of AMoD with walking or micromobility options can significantly improve the overall system performance.
Original languageEnglish
Title of host publication2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
PublisherInstitute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)9781728141497
DOIs
Publication statusPublished - 20 Sept 2020
Event23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 - Rhodes, Greece
Duration: 20 Sept 202023 Sept 2020
https://www.ieee-itsc2020.org/

Conference

Conference23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020
Abbreviated titleITSC2020
Country/TerritoryGreece
CityRhodes
Period20/09/2023/09/20
Internet address

Funding

*This work was supported by NSF under grants ECCS-1509084, DMS-1664644, CNS-1645681, IIS-1914792, and CMMI-1454737, by AFOSR under grant FA9550-19-1-0158, by ARPA-E’s NEXTCAR grant DEAR0000796, by the MathWorks, by the ONR grant N00014-19-1-2571, by the NIH grant 1R01GM135930, and by the Toyota Research Institute (TRI). This article solely reflects the opinions and conclusions of its authors and not NSF, TRI, or any other entity. We thank D. Sverdlin-Lisker, Dr. I. New and Dr. K. Solovey for proofreading this paper.

FundersFunder number
National Science FoundationDMS-1664644, 1645681, 1914792, IIS-1914792, CNS-1645681, CMMI-1454737, 1664644, ECCS-1509084
National Institutes of Health1R01GM135930
Office of Naval ResearchN00014-19-1-2571
Air Force Office of Scientific Research (AFOSR)FA9550-19-1-0158

    Keywords

    • eess.SY
    • cs.SY
    • math.OC

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