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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Multi-agent based optimal equilibrium selection with resilience constraints for traffic flow.

Ping Liu1, Iakov Korovin2, Sergey Gorbachev3

  • 1School of Transportation, Southeast University, Nanjing, 210096, China.

Neural Networks : the Official Journal of the International Neural Network Society
|September 13, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a distributed optimization algorithm for traffic management, enhancing road resource utilization and minimizing network costs. The approach uses multi-agent modeling with real-time traffic data and time-varying communication for resilient traffic flow optimization.

Keywords:
Distributed algorithmEquilibrium selectionFlow networkMulti-agent systemStrong resilience

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Area of Science:

  • Transportation Engineering
  • Operations Research
  • Distributed Systems

Background:

  • Traffic congestion is a significant urban problem, impacting efficiency and costs.
  • Effective traffic guidance and control are crucial for optimizing road network performance.
  • Real-time traffic flow data is essential for dynamic traffic management strategies.

Purpose of the Study:

  • To develop a distributed optimization algorithm for balanced traffic flow.
  • To minimize overall traffic network costs from a traffic manager's perspective.
  • To address strong resilience constraints in traffic network optimization.

Main Methods:

  • Multi-agent modeling where each agent accesses link-specific traffic information.
  • Design of a distributed optimization algorithm for real-time traffic flow.
  • Consideration of time-varying communication topologies among sensors for enhanced realism.

Main Results:

  • The proposed distributed algorithm effectively optimizes balanced traffic flow.
  • The algorithm demonstrates resilience under dynamic and time-varying conditions.
  • Numerical simulations validate the algorithm's effectiveness in a multi-agent environment.

Conclusions:

  • The developed distributed optimization algorithm offers a practical solution for traffic managers.
  • This approach enhances road resource utilization and reduces overall network costs.
  • The study provides a valuable reference for urban congestion optimization.