Analysis of the Structure of the Road Networks: A Network Science Perspective

• Autor(a) principal: Selim Reza
• Data de Publicação: 2022
• Outros Autores: José Joaquim M. Machado, João Manuel R. S. Tavares
• Tipo de documento: Livro
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/140816
• topic: Ciências Tecnológicas, Ciências da engenharia e tecnologias; Technological sciences, Engineering and technology

Studies on road networks have received extraordinary and overwhelming attention during the last few years as it is closely related to human life and city evolution. Current studies cover many aspects of a road network, for example, road feature extraction from video/image data, road map generalization, traffic simulation, optimization of optimal route finding problems, and traffic states prediction. However, little affords are given on the analysis of road networks as a graph. This study presents comparative studies on analyzing the Porto road network in terms of degree distributions, clustering coefficients, centrality measures, connected components, k-nearest neighbors, and shortest paths between three regions of Porto: Matosinhos, Paranhos, and Real. We also examined the community structures, page rank, and small-world analysis of the three regions to provide further insights into the network. The results demonstrate the information exchange efficiency of Matosinhos of 0.8, which is 10 and 12.8% higher than that of Real and Paranhos networks, respectively. Other findings are (1) road networks are highly reachable and densely connected (2) road networks are small-world with an average length of the shortest paths (between any two roads) of 12.8 (in case of the REAL road network), (3) the most important nodes of the 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 analysis of centrality measures.