Expressway Important Section Identification Model Based on Complex Network
As expressway structures become increasingly complex,identifying the expressway important section is essential for traffic characteristic analysis and operational management.Focusing on both the supply and demand aspects of transportation system,the expressway important section identification model was developed based on the principle of multi-layer complex network.The model consists of 3 modules:multilayer complex network,section importance assessment metrics,and entropy weighted K-means clustering.First,in view of the complexity of expressway transportation system,the multilayer complex network was established,comprising the structural network,weighted network,and travel network.The multilayer network effectively captured the structural,cost-related,and travel attributes of each section,realizing a more precise model of transportation system.Second,the importance metrics representing the supply and demand characteristics of each section were developed based on this multilayer complex network.On the weighted network,a set of effective paths was generated by using the K-shortest paths.Combining with the multinomial Logit model for route choice,the section probability betweenness was constructed to accurately reflect a section's significance on transportation supply under conditions of multiple route choices.On the travel network,the section flow betweenness was created to capture its importance on transportation demand.Further,constructed weighted probability betweenness and weighted traffic betweenness by combining the inherent characteristics of section weights,forming the multi-attribute importance metrics for each section.Then,the entropy weighted clustering algorithm was studied,implementing the classification on expressway important sections by using clustering,and reducing the subjectivity of equal-interval division.Finally,the case study of expressways in the Guangdong-Hong Kong-Macao Greater Bay Area demonstrated the model's effectiveness and reliability.The result was validated through the SIR network propagation model and comparative analysis.
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