Reference:
X. Liu,
A. Dabiri,
Y. Wang, and
B. De Schutter,
"Modeling and efficient passenger-oriented control for urban rail
transit networks," IEEE Transactions on Intelligent Transportation
Systems, vol. 24, no. 3, pp. 3325-3338, Mar. 2023.
Abstract:
Real-time timetable scheduling is an effective way to improve
passenger satisfaction and to reduce operational costs in urban rail
transit networks. In this paper, a novel passenger-oriented network
model is developed for real-time timetable scheduling that can model
time-dependent passenger origin-destination demands with consideration
of a balanced trade-off between model accuracy and computation speed.
Then, a model predictive control (MPC) approach is proposed for the
timetable scheduling problem based on the developed model. The
resulting MPC optimization problem is a nonlinear non-convex problem.
In this context, the online computational complexity becomes the main
issue for the real-time feasibility of MPC. To reduce the online
computational complexity, the MPC optimization problem is therefore
reformulated into a mixed-integer linear programming (MILP) problem.
The resulting MILP problem is exactly equivalent to the original MPC
optimization problem and can be solved very efficiently by existing
MILP solvers, so that we can obtain the solution very fast and realize
real-time timetable scheduling. Numerical experiments based on a part
of Beijing subway network show the effectiveness and efficiency of the
developed model and the MILP-based MPC method.
Bibtex entry:
@article{LiuDab:23-033,