Using the traffic heterogeneity of Chinese toll highway networks for hierarchical clustering

The spatial clustering of highway traffic is of great interest to researchers and policy makers. In this paper, instead of using the microscopic traffic parameters in the traditional clustering methods, we introduce a new heterogeneity index clustering the sections of a highway based on differences in the content, a.k.a. “Heterogeneity”, in their flow, which can be used as a universal guideline for network spatial clustering. Using real-world toll station origin to destination (O-D) data in three highway networks of China, we evaluate the stability of the traffic heterogeneity and verify the strong correlation between the traffic heterogeneity and the traffic variation. A case study on the hierarchical clustering for these highway roads was carried out, and we evaluate the clustering performances and show that the heterogeneity is a better partitioning criterion than other conventional traffic indices.