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Bi-Level Simulation-Driven Optimization for Route Guidance in Disrupted Metro Networks via Hybrid Swarm Intelligence.

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Summary

This study introduces an optimization framework for urban rail transit disruptions, improving route guidance by reducing congestion and travel time. It enhances passenger experience during emergencies through intelligent simulation and optimization.

Keywords:
adaptive large neighborhood search (ALNS)hybrid swarm intelligenceroute guidancesimulation-driven optimizationurban rail transit

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Area of Science:

  • Operations Research
  • Transportation Science
  • Computational Science

Background:

  • Urban rail transit disruptions necessitate efficient real-time route guidance.
  • Existing strategies often struggle to balance congestion alleviation with passenger travel time.

Purpose of the Study:

  • To develop an optimization framework for real-time route guidance during urban rail transit disruptions.
  • To incorporate passenger behavioral responses, including travel time, congestion perception, and information costs.

Main Methods:

  • A bi-level simulation evaluation mechanism using a Physically Consistent Incremental Simulator for rapid, high-fidelity assessment.
  • A hybrid algorithm combining Gray Wolf Optimizer and Adaptive Large Neighborhood Search for origin-destination route guidance optimization.
  • Integration of domain knowledge-based operators and a sequential repair mechanism for efficient global and local search.

Main Results:

  • The proposed framework reduced congestion by 36% and average travel time by 7.16 minutes.
  • Solution quality improved by 12-30% compared to baseline algorithms.
  • The simulation mechanism achieved a 599-fold speedup with high fidelity (Pearson correlation > 0.96).

Conclusions:

  • The framework demonstrates practical applicability for emergency route guidance in large-scale metro networks.
  • Integrating intelligent optimization with high-efficiency simulation is effective for managing transit disruptions.
  • The approach successfully balances operational efficiency with passenger needs during disruptions.