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.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
Data availability
The used data is public available at: https://www.openstreetmap.org/ (accessed in July 2022).
References
Watts, D.J., Strogatz, S.H.: Collective dynamics of ’small-world’networks. Nature 393(6684), 440–442 (1998)
Barabási, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Gao, S., Wang, Y., Gao, Y., Liu, Y.: Understanding urban traffic-flow characteristics: a rethinking of betweenness centrality. Environment and Planning B: Planning and Design 40(1), 135–153 (2013)
Ahmadzai, F., Rao, K.L., Ulfat, S.: Assessment and modelling of urban road networks using integrated graph of natural road network (a gis-based approach). Journal of Urban Management 8(1), 109–125 (2019)
Duan, Y., Lu, F.: Robustness of city road networks at different granularities. Physica A Stat. Mechan. Appl. 411, 21–34 (2014)
Davidović, S., Bogdanović, V., Garunović, N., Papić, Z., Pamučar, D.: Research on speeds at roundabouts for the needs of sustainable traffic management. Sustainability 13(1), 399 (2021)
Khojasteh, H., Khanteymoori, A., Olyaee, M.H.: Comparing protein–protein interaction networks of sars-cov-2 and (h1n1) influenza using topological features. Sci. Rep. 12(1), 1–11 (2022)
De Montis, A., Barthélemy, M., Chessa, A., Vespignani, A.: The structure of interurban traffic: a weighted network analysis. Environ. Plann. B Plann. Design 34(5), 905–924 (2007)
Masucci, A.P., Stanilov, K., Batty, M.: Exploring the evolution of london’s street network in the information space: A dual approach. Phys. Rev. E 89(1), 012805 (2014)
Xiao, Z., Jian-Ping, C., Jia-Li, S., Li-Dong, B.: Analysis on topological properties of beijing urban public transit based on complex network theory. Acta Physica Sinica 61(19) (2012)
Scardoni, G., Laudanna, C.: Identifying critical traffic jam areas with node centralities interference and robustness. Networks & Heterogeneous Media 7(3), 463 (2012)
Yang, Y., Cao, J., Qin, Y., Jia, L., Dong, H., Zhang, A.: Spatial correlation analysis of urban traffic state under a perspective of community detection. Int. J. Modern Phys. B 32(12), 1850150 (2018)
Hong, Y., Yao, Y.: Hierarchical community detection and functional area identification with osm roads and complex graph theory. Int. J. Geogr. Inf. Sci. 33(8), 1569–1587 (2019)
Feng, W., Li, B., Chen, Z., Liu, P.: City size based scaling of the urban internal nodes layout. PLoS ONE 16(4), 0250348 (2021)
Tsiotas, D.: Drawing indicators of economic performance from network topology: The case of the interregional road transportation in greece. Res. Transp. Econ. 90, 101004 (2021)
Borgatti, S.P.: Centrality and network flow. Soc. Netw. 27(1), 55–71 (2005)
Newman, M.: Networks, 2nd edn. Oxford University Press, Oxford (2018)
Otte, E., Rousseau, R.: Social network analysis: a powerful strategy, also for the information sciences. J. inform. Sci. 28(6), 441–453 (2002)
Bodendorf, F., Kaiser, C.: Detecting opinion leaders and trends in online social networks. In: Proceedings of the 2nd ACM Workshop on Social Web Search and Mining, pp. 65–68 (2009)
Crucitti, P., Latora, V., Porta, S.: Centrality measures in spatial networks of urban streets. Phys. Rev. E 73(3), 036125 (2006)
Jayasinghe, A., Sano, K., Nishiuchi, H.: Explaining traffic flow patterns using centrality measures. Int. J. Traff. Transport Eng. 5(2), 134–149 (2015)
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)
Chea, E., Livesay, D.R.: How accurate and statistically robust are catalytic site predictions based on closeness centrality? BMC Bioinformatics 8(1), 1–14 (2007)
Porta, S., Strano, E., Iacoviello, V., Messora, R., Latora, V., Cardillo, A., Wang, F., Scellato, S.: Street centrality and densities of retail and services in bologna, italy. Environment and Planning B: Planning and Design 36(3), 450–465 (2009)
Sabidussi, G.: The centrality index of a graph. Psychometrika 31(4), 581–603 (1966)
Barthelemy, M., Bordin, P., Berestycki, H., Gribaudi, M.: Self-organization versus top-down planning in the evolution of a city. Scientif. Rep. 3(1), 1–8 (2013)
Brandes, U.: On variants of shortest-path betweenness centrality and their generic computation. Soc. Netw. 30(2), 136–145 (2008)
Hansen, D., Shneiderman, B., Smith, M.A.: Analyzing Social Media Networks with nodeXL: Insights from a Connected World. Morgan Kaufmann (2010)
Xia, S., Xiong, Z., Luo, Y., Dong, L., Zhang, G.: Location difference of multiple distances based k-nearest neighbors algorithm. Knowl.-Based Syst. 90, 99–110 (2015)
Yao, D., van der Hoorn, P., Litvak, N.: Average nearest neighbor degrees in scale-free networks. arXiv:1704.05707 (2017)
Barrat, A., Barthelemy, M., Pastor-Satorras, R., Vespignani, A.: The architecture of complex weighted networks. Proc. Nat. Acad. Sci. 101(11), 3747–3752 (2004)
Yang, B., Liu, D., Liu, J.: Discovering Communities from Social Networks: Methodologies and Applications. In: Handbook of Social Network Technologies and Applications, pp 331–346. Springer (2010)
Reddy, P.K., Kitsuregawa, M., Sreekanth, P., Rao, S.S.: A graph based approach to extract a neighborhood customer community for collaborative filtering. In: International Workshop on Databases in Networked Information Systems, pp 188–200. Springer (2002)
Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016)
Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Nat. Acad. Sci. 99(12), 7821–7826 (2002)
Chapter four-Island biogeography of food webs. In: Bohan, D.A., Dumbrell, A.J., Massol, F. (eds.) Networks of Invasion: A Synthesis of Concepts. Advances in Ecological Research, vol. 56, pp. 183–262 Academic Press (2017)
Clauset, A., Newman, M.E., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)
Lu, H., Halappanavar, M., Kalyanaraman, A.: Parallel heuristics for scalable community detection. Parallel Comput. 47, 19–37 (2015)
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mechan. Theor. Exp. 2008(10), 10008 (2008)
Dugué, N., Perez, A.: Directed Louvain: Maximizing Modularity in Directed Networks. PhD thesis, Université d’Orléans (2015)
Hagberg, A., Swart, P., S Chult, D.: Exploring Network Structure, Dynamics, and Function Using Networkx. Technical Report, Los Alamos National Lab.(LANL). Los Alamos, NM (United States) (2008)
Boeing, G.: Osmnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Comput. Environ. Urban. Syst. 65, 126–139 (2017)
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.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interests
The authors declare no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Reza, S., Ferreira, M.C., Machado, J. et al. Road networks structure analysis: A preliminary network science-based approach. Ann Math Artif Intell 92, 215–234 (2024). https://doi.org/10.1007/s10472-022-09818-x
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10472-022-09818-x