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.
Bibtex entry:
@inproceedings{LiuDab:22-011,