Exploring the evolution of road centrality: A case study of Hong Kong from 1976 to 2018

Abstract

Centrality, which originated from sociology, has been one of the most powerful measures to describe structural properties of road networks. Among existing road centrality studies, it is noticed that most studies explore relationships between road centrality and urban quantities; a few studies use centrality to predict traffic flow or interpolate traffic volume; but few studies focus on the evolution of road centrality under long-term time series. In this study, we have explored evolutionary centrality characteristics of Hong Kong urban road networks from 1976 to 2018. The acquired centrality values are normalized, and four zones for the normalized values are formed, i.e. very low [0–0.25], low (0.25–0.50], high (0.50–0.75] and very high (0.75–1.00]. It is found that the cumulative degree distributions are long-tail distributions, and the Matthew Effect appears (i.e. the degree values of those highest-degree roads are increasing, while those of lowest-degree roads keep low). In terms of closeness centrality, the corresponding distributions are evolved to be normal distributions with the adjusted R-square increasing approximately from 0.7 to 0.9. In terms of betweenness centrality, the number of roads with very high betweenness centrality is decreasing. The above findings show the self-organized optimization process in the structural evolution of road networks, which is helpful to improve our understanding of how cities evolve.