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. 2022 Sep 29:1–20. Online ahead of print. doi: 10.1007/s10472-022-09818-x

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

Selim Reza 1, Marta Campos Ferreira 1, JJM Machado 2, João Manuel RS Tavares 2,✉
PMCID: PMC9520960  PMID: 36193340

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: Complex network analysis, Degree centrality, Closeness centrality, Betweenness centrality, Eigenvector centrality, Power-law distribution, Community detection

Acknowledgements

This article results from the project Safe Cities - “Inovação para Construir Cidades Seguras”, with reference POCI-01-0247-FEDER-041435, co-funded by the European Regional Development Fund (ERDF), through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under the PORTUGAL 2020 Partnership Agreement.

Data availability

The used data is public available at: https://www.openstreetmap.org/ (accessed in July 2022).

Declarations

Conflict of interests

The authors declare no conflict of interest.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Selim Reza, Email: up202003355@fe.up.pt.

Marta Campos Ferreira, Email: mferreira@fe.up.pt.

J.J.M. Machado, Email: jjmm@fe.up.pt

João Manuel R.S. Tavares, Email: tavares@fe.up.pt

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The used data is public available at: https://www.openstreetmap.org/ (accessed in July 2022).

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