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Network Reconfiguration for Increasing Transportation System Resilience Under Extreme Events.

Xiaoge Zhang1, Sankaran Mahadevan1, Kai Goebel2,3

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Summary
This summary is machine-generated.

This study optimizes transportation network resilience during natural disaster evacuations using a bilevel model. It minimizes total travel time by reconfiguring traffic flow, improving system performance for all commuters.

Keywords:
Network reconfigurationoptimizationresiliencetraffic assignment

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Area of Science:

  • Transportation Engineering
  • Operations Research
  • Disaster Management

Background:

  • Natural disasters necessitate resident evacuations, causing significant transportation system congestion.
  • Increased travel demand during evacuations disrupts regular commuter activities and overall system efficiency.

Purpose of the Study:

  • To develop a bilevel mathematical optimization model to enhance transportation system resilience during disaster evacuations.
  • To restore transportation system performance by implementing network reconfiguration schemes like contraflow and crossing elimination.

Main Methods:

  • Formulated a bilevel optimization model with traffic operators as leaders and passengers as followers, aiming for user equilibrium.
  • Addressed the lower-level traffic assignment problem using a gradient projection method.
  • Tackled the upper-level discrete optimization problem with a probabilistic solution discovery algorithm.

Main Results:

  • The proposed model effectively minimizes total travel time for both evacuees and regular commuters.
  • Network reconfiguration schemes significantly restore traffic system performance post-disaster.
  • Numerical examples validated the method's effectiveness in improving traffic flow and reducing congestion.

Conclusions:

  • Bilevel optimization provides a robust framework for managing evacuation-induced traffic disruptions.
  • Contraflow and intersection improvements are key strategies for enhancing transportation resilience.
  • The developed method offers a near-optimal solution for real-world disaster response planning.