Real-Time Train Scheduling With Uncertain Passenger Flows

Real-Time Train Scheduling With Uncertain Passenger Flows: A Scenario-Based Distributed Model Predictive Control Approach

Liu, X. (TU Delft Team Bart De Schutter)
Dabiri, A. (TU Delft Team Azita Dabiri)
Wang, Yihui (Beijing Jiaotong University)
De Schutter, B.H.K. (TU Delft Delft Center for Systems and Control)

Delft Center for Systems and Control

2023

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.

distributed model predictive control
Prediction algorithms
Predictive control
Processor scheduling
Rail transportation
Rails
Real-time systems
scenario approach
time-dependent passenger origin-destination demands
uncertain passenger flows
Uncertainty
Urban rail transit networks

http://resolver.tudelft.nl/uuid:cfef28b3-4299-4173-97de-17bac51c0d12

https://doi.org/10.1109/TITS.2023.3329445

1524-9050

IEEE Transactions on Intelligent Transportation Systems, 25 (5), 4219-4232

Institutional Repository

journal article

© 2023 X. Liu, A. Dabiri, Yihui Wang, B.H.K. De Schutter