Reference
X. Liu, A. Dabiri, and B. De Schutter, "Timetable scheduling for
passenger-centric urban rail networks: Model predictive control based on a
novel absorption model,"
Proceedings of the 2022 IEEE
Conference on Control Technology and Applications (CCTA), Trieste,
Italy, pp. 1147-1152, Aug. 2022.
Abstract
Timetable scheduling plays a key role in daily operations of urban rail transit
systems, as it determines the quality of service provided to passengers. In
order to develop efficient timetable scheduling methods, it is necessary to
develop a proper model to integrate timetable-related and passenger-related
factors in urban rail network efficiently. In this paper, a novel passenger
absorption model for passenger-centric urban rail networks is established. The
model explicitly integrates time-varying passenger origin-destination demands
and the departure frequency of each line for real-time timetable scheduling.
Then, a model predictive control (MPC) method for the timetable scheduling
problem is proposed based on the developed model. The resulting MPC
optimization problem can be formulated as a mixed-integer programming (MILP)
problem, which can be solved efficiently by using the existing MILP solvers.
The effectiveness of the absorption model and the corresponding MILP-based MPC
approach is illustrated through the case study based on two Beijing subway
lines.
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BibTeX
@inproceedings{LiuDab:22-011,
author = {Liu, Xiaoyu and Dabiri, Azita and De Schutter, Bart},
title = {Timetable Scheduling for Passenger-Centric Urban Rail Networks:
{Model} Predictive Control Based on a Novel Absorption Model},
booktitle = {Proceedings of the 2022 IEEE Conference on Control Technology
and Applications (CCTA)},
address = {Trieste, Italy},
pages = {1147--1152},
month = aug,
year = {2022}
}