Robust Safety Monitoring and Synergistic Operation Planning Between Time- and Energy-Efficient Movements of High-Speed Trains Based on MPC

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Abstract
When disturbances occur during high-speed train operations, real-time conflict prediction and operation replanning play a critical role in guaranteeing the safety and efficiency of networked railway traffic. This paper attempts to propose a methodology to synergistically deal with the three critical aspects, i.e., safety monitoring, conflict resolution and energy-efficient planning under the mechanism of model predictive control (MPC). A non-analytical rule-based high-level train movement model is employed to predict train movements and find out potential operation conflicts. It describes dynamic speed feedback adjustments under the precondition of keeping appropriate safety distances between trains. Conflict detection is implemented considering variation ranges of accelerations and decelerations to keep appropriate robustness. A predictive train rescheduling model is proposed incorporating the MPC mechanism and the non-analytical prediction model. After conflict prediction and resolution, the energy-efficient problems are formulated for overtaken trains towards junctions in a railway network. The corresponding solution algorithm is developed, taking into account the confinements of time-efficient movements of overtaking trains on energy-efficient movements of overtaken trains. Accordingly, operation synergies between overtaking and overtaken trains towards the junctions in a railway network can be achieved. An implementation algorithm is proposed, which holistically deals with robust safety monitoring and synergistic operation planning of high-speed trains in the prediction horizon, employing the train movement prediction model. Numerical experiments demonstrate the efficiency of the proposed methodology for train synergistic safe and efficient operations.
Funding Information
  • National Natural Science Foundation of China (61673049)
  • Fundamental Research Funds for the Central Universities of China (2017JBM302)

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