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.
Bibtex entry:
@article{LiuDab:23-005,