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Area of Science:

  • Graph theory applied to urban infrastructure analysis.
  • Network science in transportation systems.
  • Geospatial analysis of road networks.

Background:

  • Road networks are crucial for urban evolution and human existence.
  • Existing research covers road extraction, generalization, simulation, and prediction.
  • Analyzing road networks as complex graphs remains an underexplored area.

Purpose of the Study:

  • To conduct comparative graph-based analyses of road networks in Matosinhos, Paranhos, and Maia (Porto, Portugal).
  • To explore network properties including degree distributions, clustering coefficients, centrality, and community structures.
  • To gain insights into information exchange efficiency and identify critical network intersections.

Main Methods:

  • Graph theory metrics: degree distribution, clustering coefficient, centrality measures, connected components.
  • Network analysis techniques: k-nearest neighbors, shortest paths, community structure, PageRank, small-world analysis.
  • Comparative study of road network sections in Matosinhos, Paranhos, and Maia.

Main Results:

  • Matosinhos exhibits 0.8 information exchange efficiency, surpassing Maia (10%) and Paranhos (12.8%).
  • The road networks are highly accessible, densely linked, and exhibit small-world characteristics.
  • The average shortest path length is 29.17 units (observed in Maia).
  • Critical intersections identified include 'Avenida da Boavista' and 'Autoestrada do Norte' in Porto.

Conclusions:

  • The graph-based analysis provides valuable insights into the structural properties of urban road networks.
  • Matosinhos demonstrates superior information exchange efficiency compared to Maia and Paranhos.
  • Identifying critical intersections through centrality measures aids in urban planning and traffic management.