Reference:
X. Liu,
A. Dabiri, and
B. De Schutter,
"Scenario-based MPC for real-time passenger-centric timetable
scheduling of urban rail transit networks," Proceedings of the
22nd IFAC World Congress, Yokohama, Japan, pp. 2347-2352, July
2023.
Abstract:
Effective timetable scheduling strategies are essential for passenger
satisfaction in urban rail transit networks. Most existing
passenger-centric timetable scheduling approaches generate a timetable
according to deterministic passenger origin-destination (OD) demands.
As passenger OD demands in urban rail transit networks generally show
a high level of uncertainty, an effective timetable scheduling
approach should take the uncertain passenger flows into account to
generate a reliable timetable. In this paper, a scenario-based model
predictive control (SMPC) approach is presented to handle uncertain
passenger flows based on a passenger absorption model, where
uncertainties are captured by several representative scenarios
according to historical data. In each SMPC step, the optimization
problem for generating the timetable can be reformulated as a
mixed-integer linear programming (MILP) problem, which can be
efficiently solved using current MILP solvers. A probabilistic
performance level can be then determined based on the performance of
SMPC under the representative scenarios. Numerical experiments based
on the Beijing subway network are conducted to evaluate the efficacy
of the proposed approach.
Bibtex entry:
@inproceedings{LiuDab:23-024,