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An improved Newman fast division algorithm based on multi-factor correlation for dynamic traffic sub-region control.

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This study introduces a new method for dividing urban traffic networks into stable sub-regions using traffic data. The approach improves traffic signal control by creating more accurate and distinct management zones.

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Area of Science:

  • Intelligent Transportation Systems
  • Network Analysis
  • Urban Planning

Background:

  • Urban traffic management systems require systematic sub-region delineation for stability.
  • Existing methods struggle with complex traffic patterns and dynamic control needs.

Purpose of the Study:

  • To develop a robust framework for dynamic control sub-region division in urban traffic networks.
  • To enhance the rationality and precision of traffic management sub-regions.

Main Methods:

  • A traffic-mechanism-driven edge weight construction framework using modularity-based community detection.
  • Integration of traffic volume, signal cycle, and density to calculate intersection correlation.
  • Application of entropy-weighted TOPSIS for objective proximity evaluation of neighboring nodes.
  • Utilizing proximity as edge weight in an improved Newman fast partitioning algorithm.

Main Results:

  • The proposed model yields more distinct and precise sub-region divisions compared to the conventional Newman algorithm.
  • The method demonstrates superior adaptability to complex traffic patterns.
  • Empirical results validate the accuracy of sub-region divisions reflecting actual traffic conditions.

Conclusions:

  • The developed methodology provides a strong foundation for more effective traffic signal control strategies.
  • This approach enhances the stability and efficiency of urban traffic management systems.