Analysis Method for the Spatial Layout Equilibrium of Highway Transportation Network Based on Community Detection

Area of Science:

• Transportation Network Analysis
• Graph Theory
• Data Mining

Background:

• Optimizing highway transportation networks is crucial for efficiency and sustainability.
• Equitable distribution of traffic resources requires robust assessment methods.
• Existing community detection algorithms face challenges with unconnected networks.

Purpose of the Study:

• To develop a framework for assessing spatial layout equilibrium in highway networks using community structure.
• To introduce and validate the C-Louvain algorithm for improved community detection in transportation networks.
• To quantitatively evaluate network spatial layout imbalance and inform investment decisions.

Main Methods:

• Construction of a spatial node-based network.
• Application of the C-Louvain algorithm for community structure detection.
• Identification of key communities via depth-first search and Gini coefficient for supply-demand equilibrium analysis.

Main Results:

• The C-Louvain algorithm demonstrates superior accuracy, stability, and efficiency compared to FN, Leiden, and Louvain algorithms.
• Significant heterogeneity in network structure was identified across different connected components.
• The methodology effectively pinpointed imbalanced areas in the Shenyang regional highway network.

Conclusions:

• The C-Louvain algorithm offers an optimal solution for community detection in complex, unconnected networks.
• The study provides a quantitative method for assessing highway network spatial layout equilibrium.
• Findings support evidence-based investment decisions for road network development and traffic resource management.