Entropy-Based Node Importance Identification Method for Public Transportation Infrastructure Coupled Networks: A Case...

Area of Science:

• Urban planning and transportation networks
• Network science and complex systems
• Information theory and entropy metrics

Background:

• Public transportation systems are complex, coupled networks (e.g., bus and subway lines).
• Effective planning and resilience require accurate identification of critical network nodes.
• Existing centrality metrics may not fully capture node importance in coupled urban transit systems.

Purpose of the Study:

• To develop an entropy-based node importance identification method for coupled public transportation networks.
• To enhance the integrated planning of urban public transport and traffic flows.
• To improve network information dissemination and maintain network resilience.

Main Methods:

• Developed a systematic entropy-based metric integrating five centrality metrics: degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), and clustering coefficient (CCO).
• Identified important nodes by considering the information entropy of nodes and their neighbors within the coupled network.
• Evaluated the method using a real-world bus-subway coupled network in Chengdu (10,652 nodes, 15,476 edges).

Main Results:

• Multi-functional fitting analysis improved analytical accuracy by 30% compared to power law functions alone.
• The improved entropy-based metric significantly enhanced performance in identifying important nodes for both CC and CCO.
• The method demonstrated good network resilience based on assessments using MCC, NE, S, and NC metrics.

Conclusions:

• The proposed entropy-based method effectively identifies critical nodes in complex, coupled public transportation networks.
• This approach offers a more accurate and robust tool for urban transit planning and network management.
• The findings contribute to enhancing the resilience and efficiency of urban public transportation systems.