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

Associate Professorship of Business Analytics and Intelligent Systems

EPFL
Stanford University

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

12 Scopus citations

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 Inc.
Pages	1953-1960
Number of pages	8
ISBN (Electronic)	9781538670248
DOIs	https://doi.org/10.1109/ITSC.2019.8917521
State	Published - Oct 2019
Event	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 - Auckland, New Zealand Duration: 27 Oct 2019 → 30 Oct 2019

Publication series

Name	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019

Conference

Conference	2019 IEEE Intelligent Transportation Systems Conference, 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

Access to Document

10.1109/ITSC.2019.8917521

Cite this

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 (pp. 1953-1960). Article 8917521 (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC.2019.8917521

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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",

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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., 8917521, 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1953-1960, 2019 IEEE Intelligent Transportation Systems Conference, 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 Inc., 2019. p. 1953-1960 8917521 (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019).

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

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AU - Schiffer, Maximilian

AU - Pavone, Marco

PY - 2019/10

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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.

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Zgraggen J, Tsao M, Salazar M, Schiffer M, Pavone M. A Model Predictive Control Scheme for Intermodal Autonomous Mobility-on-Demand. In 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1953-1960. 8917521. (2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019). doi: 10.1109/ITSC.2019.8917521

A Model Predictive Control Scheme for Intermodal Autonomous Mobility-on-Demand

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