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

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.

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BibTeX

@article{LiuDab:24-003,
   author  = {Liu, Xiaoyu and Dabiri, Azita and Wang, Yihui and De Schutter,
              Bart},
   title   = {Real-Time Train Scheduling with Uncertain Passenger Flows: {A}
              Scenario-Based Distributed Model Predictive Control Approach},
   journal = {IEEE Transactions on Intelligent Transportation Systems},
   volume  = {25},
   number  = {5},
   pages   = {4219--4232},
   month   = may,
   year    = {2024}
   }

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