Road networks structure analysis: A preliminary network science-based approach

Selim Reza; Marta Campos Ferreira; J J M Machado; João Manuel R S Tavares

doi:10.1007/s10472-022-09818-x

Road networks structure analysis: A preliminary network science-based approach

Ann Math Artif Intell. 2022 Sep 29:1-20. doi: 10.1007/s10472-022-09818-x. Online ahead of print.

Authors

Selim Reza¹, Marta Campos Ferreira¹, J J M Machado², João Manuel R S Tavares²

Affiliations

¹ Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal.
² Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal.

Abstract

Road network studies attracted unprecedented and overwhelming interest in recent years due to the clear relationship between human existence and city evolution. Current studies cover many aspects of a road network, for example, road feature extraction from video/image data, road map generalisation, traffic simulation, optimisation of optimal route finding problems, and traffic state prediction. However, analysing road networks as a complex graph is a field to explore. This study presents comparative studies on the Porto, in Portugal, road network sections, mainly of Matosinhos, Paranhos, and Maia municipalities, regarding degree distributions, clustering coefficients, centrality measures, connected components, k-nearest neighbours, and shortest paths. Further insights into the networks took into account the community structures, page rank, and small-world analysis. The results show that the information exchange efficiency of Matosinhos is 0.8, which is 10 and 12.8% more significant than that of the Maia and Paranhos networks, respectively. Other findings stated are: (1) the studied road networks are very accessible and densely linked; (2) they are small-world in nature, with an average length of the shortest pathways between any two roads of 29.17 units, which as found in the scenario of the Maia road network; and (3) the most critical intersections of the studied network are 'Avenida da Boavista, 4100-119 Porto (latitude: 41.157944, longitude: - 8.629105)', and 'Autoestrada do Norte, Porto (latitude: 41.1687869, longitude: - 8.6400656)', based on the analysis of centrality measures.

Keywords: Betweenness centrality; Closeness centrality; Community detection; Complex network analysis; Degree centrality; Eigenvector centrality; Power-law distribution.

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