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

Abstract

Given the ongoing expansion of urban traffic signal control coverage, it is imperative to systematically delineate sub-regions based on the topology of road networks and traffic flow characteristics to enhance the stability of traffic management systems. To enhance the rationality of dynamic control sub-region division, this paper establishes a traffic-mechanism-driven edge weight construction framework integrated with modilarity-based community detection. This model integrates traffic volume, signal cycle, and traffic density to compute a comprehensive correlation degree between adjacent intersections. Furthermore, an entropy-weighted TOPSIS framework is proposed to objectively evaluate the proximity of neighboring nodes. This proximity measure is subsequently utilized as the edge weight within an improved Newman fast partitioning algorithm, thereby augmenting modularity and guiding the delineation of sub-regions. Empirical results indicate that while the conventional Newman algorithm exhibits limited adaptability to complex traffic patterns, the proposed model yields more distinct and precise sub-region divisions that more accurately reflect actual traffic conditions. Consequently, this methodology provides a robust foundation for the development of more effective signal control strategies.