Model predictive control of ride-sharing autonomous mobility-on-demand systems

Matthew Tsao; Dejan Milojevic; Claudio Ruch; Mauro Salazar; Emilio Frazzoli; Marco Pavone

doi:10.1109/ICRA.2019.8794194

Model predictive control of ride-sharing autonomous mobility-on-demand systems

Matthew Tsao
, Dejan Milojevic
, Claudio Ruch
, Mauro Salazar
, Emilio Frazzoli
, Marco Pavone

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

70 Citations (Scopus)

Abstract

This paper presents a model predictive control (MPC) approach to optimize routes for Ride-sharing Autonomous Mobility-on-Demand (RAMoD) systems, whereby self-driving vehicles provide coordinated on-demand mobility, possibly allowing multiple customers to share a ride. Specifically, we first devise a time-expanded network flow model for RAMoD. Second, leveraging this model, we design a real-time MPC algorithm to optimize the routes of both empty and customer-carrying vehicles, with the goal of optimizing social welfare, namely, a weighted combination of customers' travel time and vehicles' mileage. Finally, we present a real-world case study for the city of San Francisco, CA, by using the micro-scopic traffic simulator MATSim. The simulation results show that a RAMoD system can significantly improve social welfare with respect to a single-occupancy Autonomous Mobility-on-Demand (AMoD) system, and that the predictive structure of the proposed MPC controller allows it to outperform existing reactive ride-sharing coordination algorithms for RAMoD.

Original language	English
Title of host publication	2019 International Conference on Robotics and Automation, ICRA 2019
Publisher	Institute of Electrical and Electronics Engineers
Pages	6665-6671
Number of pages	7
ISBN (Electronic)	9781538660263
DOIs	https://doi.org/10.1109/ICRA.2019.8794194
Publication status	Published - May 2019
Externally published	Yes
Event	2019 IEEE International Conference on Robotics and Automation, ICRA 2019 - Montreal, Canada Duration: 20 May 2019 → 24 May 2019

Conference

Conference	2019 IEEE International Conference on Robotics and Automation, ICRA 2019
Country/Territory	Canada
City	Montreal
Period	20/05/19 → 24/05/19

Funding

ACKNOWLEDGMENTS We would also like to thank Dr. Ilse New and Ramón Iglesias for their assistance with the proofreading, useful advice and comments. 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.

Access to Document

10.1109/ICRA.2019.8794194

Cite this

@inproceedings{f4ed3396727746c594f431a193985a17,

title = "Model predictive control of ride-sharing autonomous mobility-on-demand systems",

abstract = "This paper presents a model predictive control (MPC) approach to optimize routes for Ride-sharing Autonomous Mobility-on-Demand (RAMoD) systems, whereby self-driving vehicles provide coordinated on-demand mobility, possibly allowing multiple customers to share a ride. Specifically, we first devise a time-expanded network flow model for RAMoD. Second, leveraging this model, we design a real-time MPC algorithm to optimize the routes of both empty and customer-carrying vehicles, with the goal of optimizing social welfare, namely, a weighted combination of customers' travel time and vehicles' mileage. Finally, we present a real-world case study for the city of San Francisco, CA, by using the micro-scopic traffic simulator MATSim. The simulation results show that a RAMoD system can significantly improve social welfare with respect to a single-occupancy Autonomous Mobility-on-Demand (AMoD) system, and that the predictive structure of the proposed MPC controller allows it to outperform existing reactive ride-sharing coordination algorithms for RAMoD.",

author = "Matthew Tsao and Dejan Milojevic and Claudio Ruch and Mauro Salazar and Emilio Frazzoli and Marco Pavone",

year = "2019",

month = may,

doi = "10.1109/ICRA.2019.8794194",

language = "English",

pages = "6665--6671",

booktitle = "2019 International Conference on Robotics and Automation, ICRA 2019",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2019 IEEE International Conference on Robotics and Automation, ICRA 2019 ; Conference date: 20-05-2019 Through 24-05-2019",

}

Tsao, M, Milojevic, D, Ruch, C, Salazar, M, Frazzoli, E & Pavone, M 2019, Model predictive control of ride-sharing autonomous mobility-on-demand systems. in 2019 International Conference on Robotics and Automation, ICRA 2019., 8794194, Institute of Electrical and Electronics Engineers, pp. 6665-6671, 2019 IEEE International Conference on Robotics and Automation, ICRA 2019, Montreal, Quebec, Canada, 20/05/19. https://doi.org/10.1109/ICRA.2019.8794194

Model predictive control of ride-sharing autonomous mobility-on-demand systems. / Tsao, Matthew; Milojevic, Dejan; Ruch, Claudio et al.
2019 International Conference on Robotics and Automation, ICRA 2019. Institute of Electrical and Electronics Engineers, 2019. p. 6665-6671 8794194.

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

TY - GEN

T1 - Model predictive control of ride-sharing autonomous mobility-on-demand systems

AU - Tsao, Matthew

AU - Milojevic, Dejan

AU - Ruch, Claudio

AU - Salazar, Mauro

AU - Frazzoli, Emilio

AU - Pavone, Marco

PY - 2019/5

Y1 - 2019/5

N2 - This paper presents a model predictive control (MPC) approach to optimize routes for Ride-sharing Autonomous Mobility-on-Demand (RAMoD) systems, whereby self-driving vehicles provide coordinated on-demand mobility, possibly allowing multiple customers to share a ride. Specifically, we first devise a time-expanded network flow model for RAMoD. Second, leveraging this model, we design a real-time MPC algorithm to optimize the routes of both empty and customer-carrying vehicles, with the goal of optimizing social welfare, namely, a weighted combination of customers' travel time and vehicles' mileage. Finally, we present a real-world case study for the city of San Francisco, CA, by using the micro-scopic traffic simulator MATSim. The simulation results show that a RAMoD system can significantly improve social welfare with respect to a single-occupancy Autonomous Mobility-on-Demand (AMoD) system, and that the predictive structure of the proposed MPC controller allows it to outperform existing reactive ride-sharing coordination algorithms for RAMoD.

AB - This paper presents a model predictive control (MPC) approach to optimize routes for Ride-sharing Autonomous Mobility-on-Demand (RAMoD) systems, whereby self-driving vehicles provide coordinated on-demand mobility, possibly allowing multiple customers to share a ride. Specifically, we first devise a time-expanded network flow model for RAMoD. Second, leveraging this model, we design a real-time MPC algorithm to optimize the routes of both empty and customer-carrying vehicles, with the goal of optimizing social welfare, namely, a weighted combination of customers' travel time and vehicles' mileage. Finally, we present a real-world case study for the city of San Francisco, CA, by using the micro-scopic traffic simulator MATSim. The simulation results show that a RAMoD system can significantly improve social welfare with respect to a single-occupancy Autonomous Mobility-on-Demand (AMoD) system, and that the predictive structure of the proposed MPC controller allows it to outperform existing reactive ride-sharing coordination algorithms for RAMoD.

U2 - 10.1109/ICRA.2019.8794194

DO - 10.1109/ICRA.2019.8794194

M3 - Conference contribution

AN - SCOPUS:85071460205

SP - 6665

EP - 6671

BT - 2019 International Conference on Robotics and Automation, ICRA 2019

PB - Institute of Electrical and Electronics Engineers

T2 - 2019 IEEE International Conference on Robotics and Automation, ICRA 2019

Y2 - 20 May 2019 through 24 May 2019

ER -

Model predictive control of ride-sharing autonomous mobility-on-demand systems

Abstract

Conference

Funding

Access to Document

Fingerprint

Cite this