Chapter
Jan 13, 2020
Sixth International Conference on Transportation Engineering

Model Predictive Control for Public Transport Transfer Synchronization

Publication: ICTE 2019

ABSTRACT

This work proposes a model predictive control (MPC)-based optimization framework for public transport (PT) transfer synchronization. The MPC scheme is based on a transfer-based distributed control architecture using vehicle-to-vehicle communication. A library of operational tactics serves as the basis of a sequential decision-making process in improving PT transfer synchronization. The optimal operational tactics are determined at each decision epoch within a receding horizon control framework. It is anticipated that the MPC scheme can help significantly improve the actual occurrence of synchronized transfers in PT networks.

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ACKNOWLEDGEMENT

This work was supported by the National Engineering Laboratory of Integrated Transportation Big Data Application Technology (Project No.: CTBDAT201907).

REFERENCES

Camacho, E.F., and Alba, C.B. (2004). Model Predictive Control, 2nd Edition, Springer.
Ceder, A. (2016). Public Transit Planning and Operation: Modeling, Practice and Behavior, 2nd Edition, CRC Press, Boca Raton, USA.
LIU, T., Ceder, A., MA, J., and GUAN, W. (2014a). “Synchronizing public transport transfers by using intervehicle communication scheme: Case study.” Transportation Research Record, 2417, 78-91.
LIU, T., Ceder, A., MA, J., and GUAN, W. (2014b). “CBVC-B: A system for synchronizing public-transport transfers using vehicle-to-vehicle communication.” Procedia-Social and Behavioral Sciences, 138, 241-250.
LIU, T., Ceder, A., MA, J., Nesheli, M. M., and GUAN, W. (2015). “Optimal synchronized transfers in schedule-based public transport networks using online operational tactics.” Transportation Research Record, 2533, 78-90.

Information & Authors

Information

Published In

Go to ICTE 2019
ICTE 2019
Pages: 291 - 298
Editors: Xiaobo Liu, Ph.D., Southwest Jiaotong University, Qiyuan Peng, Ph.D., Southwest Jiaotong University, and Kelvin C. P. Wang, Ph.D., Oklahoma State University
ISBN (Online): 978-0-7844-8274-2

History

Published online: Jan 13, 2020

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Authors

Affiliations

National Engineering Laboratory of Integrated Transportation Big Data Application Technology and National United Engineering Laboratory of Integrated and Intelligent Transportation, School of Transportation and Logistics, Southwest Jiaotong Univ., Chengdu 611756, P.R. China (corresponding author). E-mail: [email protected]

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