Bi-Level Model Predictive Control for Metro Networks: Integration of Timetables, Passenger Flows, and Train Speed Profiles

Reference

X. Liu, A. Dabiri, J. Xun, and B. De Schutter, "Bi-Level Model Predictive Control for Metro Networks: Integration of Timetables, Passenger Flows, and Train Speed Profiles," Transportation Research Part E, vol. 180, p. 103339, Dec. 2023.

Abstract

This paper deals with the train scheduling problem for metro networks taking into account time-dependent passenger origin-destination demands and train speed profiles. The aim is to adjust train schedules online according to time-dependent passenger demands so that passenger satisfaction and operational costs are jointly optimized. An extended passenger absorption model that explicitly includes time-dependent passenger origin-destination demands is developed, where the term "absorption" refers to passengers boarding trains. Then, the passenger absorption model is extended to a bi-level framework, where passenger demands and rolling stock availability are considered at the higher level, and detailed timetables and train speed profiles are included at the lower level. A bi-level model predictive control (MPC) approach is developed for the integrated problem. The optimization problems of both levels of the bi-level MPC approach can be converted into mixed-integer linear programming (MILP) problems, which enables us to solve them with existing MILP solvers. We then show that the recursive feasibility of both the higher-level and the lower-level optimization problems can be guaranteed. In this way, we can achieve real-time train scheduling for the metro system. Numerical experiments, based on real-life data from the Beijing metro network, illustrate the effectiveness of the extended passenger absorption model and the proposed bi-level MPC approach.

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Bibtex entry

@article{LiuDab:23-005,
author={X. Liu and A. Dabiri and J. Xun and B. {D}e Schutter},
title={Bi-Level Model Predictive Control for Metro Networks: Integration of Timetables, Passenger Flows, and Train Speed Profiles},
journal={Transportation Research Part E},
volume={180},
pages={103339},
month=dec,
year={2023},
doi={10.1016/j.tre.2023.103339}
}

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