A model predictive control scheme for Intermodal Autonomous Mobility-on-Demand

Jannik Zgraggen; Matthew Tsao; Mauro Salazar; Maximilian Schiffer; Marco Pavone

doi:10.1109/ITSC.2019.8917521

A model predictive control scheme for Intermodal Autonomous Mobility-on-Demand

Jannik Zgraggen
, Matthew Tsao
, Mauro Salazar
, Maximilian Schiffer
, Marco Pavone

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

12 Citations (Scopus)

Abstract

This paper presents a routing algorithm for intermodal Autonomous Mobility on Demand (AMoD) systems, whereby a fleet of self-driving cars provides on-demand mobility in coordination with public transit. Specifically, we present a time-variant flow-based optimization approach that captures the operation of an AMoD system in coordination with public transit. We then leverage this model to devise a model predictive control (MPC) algorithm to route customers and vehicles through the network with the objective of minimizing customers' travel time. To validate our MPC scheme, we present a real-world case study for New York City. Our results show that servicing transportation demands jointly with public transit can significantly improve the service quality of AMoD systems. Additionally, we highlight the differences of our time-variant framework compared to existing mesoscopic, time-invariant models.

Original language	English
Title of host publication	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Publisher	Institute of Electrical and Electronics Engineers
Pages	1953-1960
Number of pages	8
ISBN (Electronic)	9781538670248
DOIs	https://doi.org/10.1109/ITSC.2019.8917521
Publication status	Published - Oct 2019
Externally published	Yes
Event	22nd International IEEE Conference on Intelligent Transportation Systems, ITSC 2019 - Auckland, New Zealand Duration: 27 Oct 2019 → 30 Oct 2019 Conference number: 22

Conference

Conference	22nd International IEEE Conference on Intelligent Transportation Systems, ITSC 2019
Abbreviated title	ITSC 2019
Country/Territory	New Zealand
City	Auckland
Period	27/10/19 → 30/10/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

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10.1109/ITSC.2019.8917521

Cite this

@inproceedings{9612ee6fbc714d0a82ef2bf26781ad9a,

title = "A model predictive control scheme for Intermodal Autonomous Mobility-on-Demand",

abstract = "This paper presents a routing algorithm for intermodal Autonomous Mobility on Demand (AMoD) systems, whereby a fleet of self-driving cars provides on-demand mobility in coordination with public transit. Specifically, we present a time-variant flow-based optimization approach that captures the operation of an AMoD system in coordination with public transit. We then leverage this model to devise a model predictive control (MPC) algorithm to route customers and vehicles through the network with the objective of minimizing customers' travel time. To validate our MPC scheme, we present a real-world case study for New York City. Our results show that servicing transportation demands jointly with public transit can significantly improve the service quality of AMoD systems. Additionally, we highlight the differences of our time-variant framework compared to existing mesoscopic, time-invariant models.",

author = "Jannik Zgraggen and Matthew Tsao and Mauro Salazar and Maximilian Schiffer and Marco Pavone",

year = "2019",

month = oct,

doi = "10.1109/ITSC.2019.8917521",

language = "English",

pages = "1953--1960",

booktitle = "2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "22nd International IEEE Conference on Intelligent Transportation Systems, ITSC 2019, ITSC 2019 ; Conference date: 27-10-2019 Through 30-10-2019",

}

Zgraggen, J, Tsao, M, Salazar, M, Schiffer, M & Pavone, M 2019, A model predictive control scheme for Intermodal Autonomous Mobility-on-Demand. in 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019., 19211343, Institute of Electrical and Electronics Engineers, pp. 1953-1960, 22nd International IEEE Conference on Intelligent Transportation Systems, ITSC 2019, Auckland, New Zealand, 27/10/19. https://doi.org/10.1109/ITSC.2019.8917521

A model predictive control scheme for Intermodal Autonomous Mobility-on-Demand. / Zgraggen, Jannik; Tsao, Matthew; Salazar, Mauro et al.
2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers, 2019. p. 1953-1960 19211343.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - A model predictive control scheme for Intermodal Autonomous Mobility-on-Demand

AU - Zgraggen, Jannik

AU - Tsao, Matthew

AU - Salazar, Mauro

AU - Schiffer, Maximilian

AU - Pavone, Marco

N1 - Conference code: 22

PY - 2019/10

Y1 - 2019/10

N2 - This paper presents a routing algorithm for intermodal Autonomous Mobility on Demand (AMoD) systems, whereby a fleet of self-driving cars provides on-demand mobility in coordination with public transit. Specifically, we present a time-variant flow-based optimization approach that captures the operation of an AMoD system in coordination with public transit. We then leverage this model to devise a model predictive control (MPC) algorithm to route customers and vehicles through the network with the objective of minimizing customers' travel time. To validate our MPC scheme, we present a real-world case study for New York City. Our results show that servicing transportation demands jointly with public transit can significantly improve the service quality of AMoD systems. Additionally, we highlight the differences of our time-variant framework compared to existing mesoscopic, time-invariant models.

AB - This paper presents a routing algorithm for intermodal Autonomous Mobility on Demand (AMoD) systems, whereby a fleet of self-driving cars provides on-demand mobility in coordination with public transit. Specifically, we present a time-variant flow-based optimization approach that captures the operation of an AMoD system in coordination with public transit. We then leverage this model to devise a model predictive control (MPC) algorithm to route customers and vehicles through the network with the objective of minimizing customers' travel time. To validate our MPC scheme, we present a real-world case study for New York City. Our results show that servicing transportation demands jointly with public transit can significantly improve the service quality of AMoD systems. Additionally, we highlight the differences of our time-variant framework compared to existing mesoscopic, time-invariant models.

UR - https://www.scopus.com/pages/publications/85076814580

U2 - 10.1109/ITSC.2019.8917521

DO - 10.1109/ITSC.2019.8917521

M3 - Conference contribution

SP - 1953

EP - 1960

BT - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

PB - Institute of Electrical and Electronics Engineers

T2 - 22nd International IEEE Conference on Intelligent Transportation Systems, ITSC 2019

Y2 - 27 October 2019 through 30 October 2019

ER -

A model predictive control scheme for Intermodal Autonomous Mobility-on-Demand

Abstract

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UN SDGs

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