Model validation and Robust State Feedback control for nonlinear subway traffic networks.

Area of Science:

• * Operations Research
• * Control Systems Engineering
• * Transportation Systems Modeling

Background:

• * Subway networks are complex systems susceptible to delays.
• * Existing models may not fully capture the dynamic and nonlinear nature of subway operations.
• * Efficient control strategies are needed to ensure reliable service and minimize disruptions.

Purpose of the Study:

• * To develop a nonlinear Discrete-Event (DE) model for intersecting lines (ILs) within a subway network (SN).
• * To validate the proposed DE model using Monte Carlo (MC) simulation methods.
• * To design and evaluate a Robust State Feedback (RSF) controller for mitigating delays.

Main Methods:

• * Development of a nonlinear Discrete-Event (DE) model for subway network intersecting lines.
• * Application of Monte Carlo (MC) methods for model validation, incorporating random delay rates and external disturbances.
• * Design of a Robust State Feedback (RSF) controller to compensate for network delays.
• * Simulation studies using real-world data from Tehran subway lines 2 and 4.

Main Results:

• * The developed DE model accurately represents subway network dynamics.
• * Monte Carlo simulations confirmed the model's validity and performance.
• * The RSF controller effectively compensated for delays in both centralized and decentralized configurations.
• * Simulation results demonstrated the proposed approach's accuracy and effectiveness.

Conclusions:

• * The nonlinear DE model provides a robust framework for analyzing subway network operations.
• * The RSF controller is a viable solution for enhancing subway system resilience and performance.
• * The study validates the effectiveness of the proposed modeling and control strategy using real-world data.