An improved Newman fast division algorithm based on multi-factor correlation for dynamic traffic sub-region control
Xiujuan Tian, Jinyong Ding, Huanying Liu, Haoke Deng
TL;DR
This paper improves a traffic control algorithm by using traffic data to better divide urban areas into sub-regions for more effective signal management.
Contribution
A novel traffic-mechanism-driven edge weight framework combined with an improved Newman algorithm for dynamic sub-region division.
Findings
The proposed model improves sub-region division accuracy compared to the conventional Newman algorithm.
The integration of traffic volume, signal cycle, and density enhances the correlation degree calculation between intersections.
Empirical results show the model better adapts to complex traffic patterns.
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…
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Taxonomy
TopicsTraffic control and management · Traffic Prediction and Management Techniques · Transportation Planning and Optimization