Reference:
X. Liu,
A. Dabiri,
Y. Wang, and
B. De Schutter,
"Real-time train scheduling with uncertain passenger flows: A
scenario-based distributed model predictive control approach,"
IEEE Transactions on Intelligent Transportation Systems, vol.
25, no. 5, pp. 4219-4232, May 2024.
Abstract:
Real-time train scheduling is essential for passenger satisfaction in
urban rail transit networks. This paper focuses on real-time train
scheduling for urban rail transit networks considering uncertain
time-dependent passenger origin-destination demands. First, a
macroscopic passenger flow model we proposed before is extended to
include rolling stock availability. Then, a
distributed-knowledgeable-reduced-horizon (DKRH) algorithm is
developed to deal with the computational burden and the communication
restrictions of the train scheduling problem in urban rail transit
networks. For the DKRH algorithm, a cost-to-go function is designed to
reduce the prediction horizon of the original model predictive control
approach while taking into account the control performance. By
applying a scenario reduction approach, a scenario-based
distributed-knowledgeable-reduced-horizon (S-DKRH) algorithm is
proposed to handle the uncertain passenger flows with an acceptable
increase in computation time. Numerical experiments are conducted to
evaluate the effectiveness of the developed DKRH and S-DKRH algorithms
based on real-life data from the Beijing urban rail transit network.
The simulation results indicate that DKRH can be used to achieve
real-time train scheduling for the urban rail transit network, while
S-DKRH can handle the uncertainty in the passenger flows with an
acceptable sacrifice in computation time.
Bibtex entry:
@article{LiuDab:24-003,